From a4fa8ef647d88139f3225809313ade54d7c68ea5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 22 Aug 2021 18:31:58 +0300
Subject: [PATCH 01/93] Add template for T2 exp, still need to check results
---
.../library/characterization/T2Hahn.py | 149 ++++++++++++++++++
1 file changed, 149 insertions(+)
create mode 100644 qiskit_experiments/library/characterization/T2Hahn.py
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
new file mode 100644
index 0000000000..9f6436fd1f
--- /dev/null
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -0,0 +1,149 @@
+from typing import Union, Iterable, Optional, List
+
+import numpy as np
+from numpy.random import Generator, default_rng
+
+from qiskit import QuantumCircuit, QiskitError, QuantumRegister, ClassicalRegister
+from qiskit.ignis.characterization.characterization_utils import pad_id_gates, time_to_ngates
+from qiskit.providers import Backend
+from qiskit.quantum_info import Clifford
+from qiskit.providers.options import Options
+from qiskit.circuit import Gate
+
+from qiskit_experiments.framework import BaseExperiment, ParallelExperiment
+from qiskit_experiments.curve_analysis.data_processing import probability
+from .rb_analysis import RBAnalysis
+from .clifford_utils import CliffordUtils
+from .rb_utils import RBUtils
+
+
+class T2Hahn(BaseExperiment):
+ """Standard randomized benchmarking experiment.
+
+ # section: overview
+ Randomized Benchmarking (RB) is an efficient and robust method
+ for estimating the average error-rate of a set of quantum gate operations.
+ See `Qiskit Textbook
+ <https://qiskit.org/textbook/ch-quantum-hardware/randomized-benchmarking.html>`_
+ for an explanation on the RB method.
+
+ A standard RB experiment generates sequences of random Cliffords
+ such that the unitary computed by the sequences is the identity.
+ After running the sequences on a backend, it calculates the probabilities to get back to
+ the ground state, fits an exponentially decaying curve, and estimates
+ the Error Per Clifford (EPC), as described in Refs. [1, 2].
+
+ See :class:`RBUtils` documentation for additional information
+ on estimating the Error Per Gate (EPG) for 1-qubit and 2-qubit gates,
+ from 1-qubit and 2-qubit standard RB experiments, by Ref. [3].
+
+ # section: reference
+ .. ref_arxiv:: 1 1009.3639
+ .. ref_arxiv:: 2 1109.6887
+ .. ref_arxiv:: 3 1712.06550
+
+ """
+
+ # Analysis class for experiment
+ __analysis_class__ = T2Analysis # need to add T2Analysis
+
+ def __init__(
+ self,
+ qubits: Union[int, Iterable[int]],
+ lengths: Union[List[int], np.array], # need to change name?
+ gate_time: float,
+ n_echos: int = 1,
+ phase_alt_echo: bool = False,
+ ):
+ """Initialize a T2 experiment with Hahn echo.
+
+ Args:
+ num_of_gates:
+ Each element of the list corresponds to a circuit.
+ `num_of_gates[i]` is the number of identity gates in each section
+ "t" of the pulse sequence in circuit no. i.
+ Must be in an increasing order.
+ gate_time: time of running a single identity gate.
+ qubits: indices of the qubits whose
+ T\ :sub:`2`:sup:`*`\ 's are to be measured.
+ n_echos: number of echo gates (`X` or `Y`).
+ phase_alt_echo: if True then alternate the echo between
+ `X` and `Y`.
+ """
+ # Initialize base experiment
+ super().__init__(qubits)
+ self._verify_parameters(qubits, lengths, n_echos,gate_time)
+
+ # Set configurable options
+ self.set_experiment_options(lengths=list(lengths), n_echos=n_echos, phase_alt_echo=phase_alt_echo)
+ self.set_analysis_options(data_processor=probability(
+ outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
+
+ # Set fixed options
+ self._full_sampling = full_sampling # Don't need?
+ self._clifford_utils = CliffordUtils() # Don't need?
+
+ if not isinstance(seed, Generator):
+ self._rng = default_rng(seed=seed)
+ else:
+ self._rng = seed
+
+ def _verify_parameters(self, qubits, lengths, n_echos, gate_time):
+ """Verify input correctness, raise QiskitError if needed"""
+ if any(length <= 0 for length in lengths):
+ raise QiskitError(
+ f"The lengths list {lengths} should only contain " "positive elements."
+ )
+ if len(set(lengths)) != len(lengths):
+ raise QiskitError(
+ f"The lengths list {lengths} should not contain " "duplicate elements."
+ )
+ if any(lengths[idx - 1] >= lengths[idx] for idx in range(1, lengths)):
+ raise QiskitError(f"The number of identity gates {lengths} should " "be increasing.")
+
+ if any(qubit < 0 for qubit in qubits):
+ raise QiskitError(f"The index of the qubits {qubits} should " "be non-negative.")
+
+ if n_echos < 1:
+ raise QiskitError(f"The number of echoes {n_echos} should " "be at least 1.")
+
+ if gate_time <= 0:
+ raise QiskitError(f"The gate time {gate_time} should " "be positive.")
+
+ def _generate_circuits(self, num_of_gates: Union[List[int], np.array],
+ gate_time: float,
+ qubits: List[int],
+ n_echos: int = 1,
+ phase_alt_echo: bool = False):
+ if n_echos < 1:
+ raise ValueError('Must be at least one echo')
+
+ xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
+ qr = QuantumRegister(max(qubits) + 1)
+ cr = ClassicalRegister(len(qubits))
+ circuits = []
+ for circ_index, circ_length in enumerate(num_of_gates):
+ circ = QuantumCircuit()
+ circ.name = 't2circuit_' + str(circ_index) + '_0'
+ for qind, qubit in enumerate(qubits):
+
+ # First Y90 and Y echo
+ circ.append(circ.ry(np.pi/4, [qr[qubit]])) # Y90
+ circ = pad_id_gates(circ, qr, qubit, circ_length) # ids
+ circ.y(qr[qubit])
+
+ for echoid in range(n_echos - 1): # repeat
+ circ = pad_id_gates(circ, qr, qubit, 2 * circ_length) # ids
+ if phase_alt_echo and (not echoid % 2): # optionally
+ circ.x(qr[qubit]) # X
+ else:
+ circ.y(qr[qubit])
+
+ circ = pad_id_gates(circ, qr, qubit, circ_length) # ids
+ circ.append(circ.y, [qr[qubit]]) # Y90
+ circ.barrier(qr)
+ for qind, qubit in enumerate(qubits):
+ circ.measure(qr[qubit], cr[qind]) # measure
+ circuits.append(circ)
+
+ return circuits, xdata
From e638dcad953b6739ce8d5956aab4d793f7264782 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 22 Aug 2021 18:37:46 +0300
Subject: [PATCH 02/93] Made the code cleaner
---
qiskit_experiments/library/characterization/T2Hahn.py | 8 --------
1 file changed, 8 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 9f6436fd1f..94b00940eb 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -79,14 +79,6 @@ def __init__(
self.set_analysis_options(data_processor=probability(
outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
- # Set fixed options
- self._full_sampling = full_sampling # Don't need?
- self._clifford_utils = CliffordUtils() # Don't need?
-
- if not isinstance(seed, Generator):
- self._rng = default_rng(seed=seed)
- else:
- self._rng = seed
def _verify_parameters(self, qubits, lengths, n_echos, gate_time):
"""Verify input correctness, raise QiskitError if needed"""
From 28271b9f88c1f21b8879dc5e198ee72f9249daaa Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 22 Aug 2021 18:41:56 +0300
Subject: [PATCH 03/93] cleared RB documantation
---
.../library/characterization/T2Hahn.py | 25 -------------------
1 file changed, 25 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 94b00940eb..6637566cac 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -18,31 +18,6 @@
class T2Hahn(BaseExperiment):
- """Standard randomized benchmarking experiment.
-
- # section: overview
- Randomized Benchmarking (RB) is an efficient and robust method
- for estimating the average error-rate of a set of quantum gate operations.
- See `Qiskit Textbook
- <https://qiskit.org/textbook/ch-quantum-hardware/randomized-benchmarking.html>`_
- for an explanation on the RB method.
-
- A standard RB experiment generates sequences of random Cliffords
- such that the unitary computed by the sequences is the identity.
- After running the sequences on a backend, it calculates the probabilities to get back to
- the ground state, fits an exponentially decaying curve, and estimates
- the Error Per Clifford (EPC), as described in Refs. [1, 2].
-
- See :class:`RBUtils` documentation for additional information
- on estimating the Error Per Gate (EPG) for 1-qubit and 2-qubit gates,
- from 1-qubit and 2-qubit standard RB experiments, by Ref. [3].
-
- # section: reference
- .. ref_arxiv:: 1 1009.3639
- .. ref_arxiv:: 2 1109.6887
- .. ref_arxiv:: 3 1712.06550
-
- """
# Analysis class for experiment
__analysis_class__ = T2Analysis # need to add T2Analysis
From 1f8c4ca519b6f7e6989abc1b2bac853a36bf879f Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 30 Aug 2021 15:44:24 +0300
Subject: [PATCH 04/93] Fixed errors and for 1 echo the circuit is generated
(need to work on options)
Changed rotation to Pi/2
This code support only n_echoes=1 for further testing
---
.../library/characterization/T2Hahn.py | 105 +++++++++++-------
1 file changed, 64 insertions(+), 41 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 6637566cac..2ea66541e1 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -12,20 +12,22 @@
from qiskit_experiments.framework import BaseExperiment, ParallelExperiment
from qiskit_experiments.curve_analysis.data_processing import probability
-from .rb_analysis import RBAnalysis
-from .clifford_utils import CliffordUtils
-from .rb_utils import RBUtils
+from qiskit.utils import apply_prefix
+from qiskit.providers import Backend
+from qiskit.test.mock import FakeParis
+from qiskit.providers.aer import AerSimulator
+
class T2Hahn(BaseExperiment):
# Analysis class for experiment
- __analysis_class__ = T2Analysis # need to add T2Analysis
+# __analysis_class__ = T2Analysis # need to add T2Analysis
def __init__(
self,
qubits: Union[int, Iterable[int]],
- lengths: Union[List[int], np.array], # need to change name?
+ delays: Union[List[int], np.array], # need to change name?
gate_time: float,
n_echos: int = 1,
phase_alt_echo: bool = False,
@@ -47,26 +49,32 @@ def __init__(
"""
# Initialize base experiment
super().__init__(qubits)
- self._verify_parameters(qubits, lengths, n_echos,gate_time)
+ self._verify_parameters(qubits, delays, n_echos,gate_time)
+ self._qubits = qubits
+ self._delays = delays
+ self._gate_time = gate_time
+ self._n_echos = n_echos
+ self._phase_alt_echo = phase_alt_echo
# Set configurable options
- self.set_experiment_options(lengths=list(lengths), n_echos=n_echos, phase_alt_echo=phase_alt_echo)
- self.set_analysis_options(data_processor=probability(
- outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
+# self.set_experiment_options(delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo)
+# self.set_analysis_options(data_processor=probability(
+# outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
- def _verify_parameters(self, qubits, lengths, n_echos, gate_time):
+
+ def _verify_parameters(self, qubits, delays, n_echos, gate_time):
"""Verify input correctness, raise QiskitError if needed"""
- if any(length <= 0 for length in lengths):
+ if any(delay <= 0 for delay in delays):
raise QiskitError(
- f"The lengths list {lengths} should only contain " "positive elements."
+ f"The lengths list {delays} should only contain " "positive elements."
)
- if len(set(lengths)) != len(lengths):
+ if len(set(delays)) != len(delays):
raise QiskitError(
- f"The lengths list {lengths} should not contain " "duplicate elements."
+ f"The lengths list {delays} should not contain " "duplicate elements."
)
- if any(lengths[idx - 1] >= lengths[idx] for idx in range(1, lengths)):
- raise QiskitError(f"The number of identity gates {lengths} should " "be increasing.")
+ if any(delays[idx - 1] >= delays[idx] for idx in range(1, len(delays))):
+ raise QiskitError(f"The number of identity gates {delays} should " "be increasing.")
if any(qubit < 0 for qubit in qubits):
raise QiskitError(f"The index of the qubits {qubits} should " "be non-negative.")
@@ -77,40 +85,55 @@ def _verify_parameters(self, qubits, lengths, n_echos, gate_time):
if gate_time <= 0:
raise QiskitError(f"The gate time {gate_time} should " "be positive.")
- def _generate_circuits(self, num_of_gates: Union[List[int], np.array],
- gate_time: float,
- qubits: List[int],
+ def circuits(self, backend, qubits: Union[List[int], np.array],
n_echos: int = 1,
phase_alt_echo: bool = False):
- if n_echos < 1:
- raise ValueError('Must be at least one echo')
- xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
+ conversion_factor = 1
+# if self.experiment_options.unit == "dt":
+# try:
+# dt_factor = getattr(backend._configuration, "dt")
+# conversion_factor = dt_factor
+# except AttributeError as no_dt:
+# raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
+# elif self.experiment_options.unit != "s":
+# apply_prefix(1, self.experiment_options.unit)
+# xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
qr = QuantumRegister(max(qubits) + 1)
cr = ClassicalRegister(len(qubits))
circuits = []
- for circ_index, circ_length in enumerate(num_of_gates):
- circ = QuantumCircuit()
+ for circ_index, delay in enumerate(self._delays):
+ circ = QuantumCircuit(max(qubits) + 1,len(qubits))
circ.name = 't2circuit_' + str(circ_index) + '_0'
for qind, qubit in enumerate(qubits):
- # First Y90 and Y echo
- circ.append(circ.ry(np.pi/4, [qr[qubit]])) # Y90
- circ = pad_id_gates(circ, qr, qubit, circ_length) # ids
- circ.y(qr[qubit])
-
- for echoid in range(n_echos - 1): # repeat
- circ = pad_id_gates(circ, qr, qubit, 2 * circ_length) # ids
- if phase_alt_echo and (not echoid % 2): # optionally
- circ.x(qr[qubit]) # X
- else:
- circ.y(qr[qubit])
-
- circ = pad_id_gates(circ, qr, qubit, circ_length) # ids
- circ.append(circ.y, [qr[qubit]]) # Y90
- circ.barrier(qr)
+ # First Y rotation in 90 degrees
+ circ.ry(np.pi/2, qubit) # Bring to qubits to X Axis
+ # circ = pad_id_gates(circ, qr, qubit, circ_length) # ids - waiting
+# circ.delay(delay, qr[qubit], self.experiment_options.unit)
+ circ.delay(delay, qubit, 's')
+ circ.rx(np.pi/2, qubit)
+
+# for echoid in range(n_echos - 1): # repeat
+# circ = pad_id_gates(circ, qr, qubit, 2 * delay) # ids
+# if phase_alt_echo and (not echoid % 2): # optionally
+# circ.x(qr[qubit]) # X
+# else:
+# circ.y(qr[qubit])
+
+# circ.delay(delay, qr[qubit], self.experiment_options.unit) # ids
+ circ.delay(delay, qubit, 's')
+ circ.ry(-np.pi/2, qubit) # Y90
for qind, qubit in enumerate(qubits):
- circ.measure(qr[qubit], cr[qind]) # measure
+ circ.measure(qubit, qind) # measure
+ circ.metadata = {
+ "experiment_type": self._type,
+ "qubit": self.physical_qubits,
+ "xval": delay,
+ "unit": 's',
+ }
+# if self.experiment_options.unit == "dt":
+# circ.metadata["dt_factor"] = dt_factor
circuits.append(circ)
- return circuits, xdata
+ return circuits
\ No newline at end of file
From d3af4959c581752de1ba75e529e86de9249b9c8b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 30 Aug 2021 15:58:08 +0300
Subject: [PATCH 05/93] Changed the Echo pulse to pi instead of pi/2
---
qiskit_experiments/library/characterization/T2Hahn.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 2ea66541e1..560fac67f7 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -112,7 +112,7 @@ def circuits(self, backend, qubits: Union[List[int], np.array],
# circ = pad_id_gates(circ, qr, qubit, circ_length) # ids - waiting
# circ.delay(delay, qr[qubit], self.experiment_options.unit)
circ.delay(delay, qubit, 's')
- circ.rx(np.pi/2, qubit)
+ circ.rx(np.pi, qubit)
# for echoid in range(n_echos - 1): # repeat
# circ = pad_id_gates(circ, qr, qubit, 2 * delay) # ids
From 0a3935d337e7188258e6fa437264c487070b9efb Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Thu, 9 Sep 2021 14:19:43 +0300
Subject: [PATCH 06/93] Changed the class to support only single qubit
Changed the class to support only single qubit and added checks to verify parameters
---
.../library/characterization/T2Hahn.py | 108 +++++++++---------
1 file changed, 57 insertions(+), 51 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 560fac67f7..22a63c5a71 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -14,19 +14,19 @@
from qiskit_experiments.curve_analysis.data_processing import probability
from qiskit.utils import apply_prefix
-
from qiskit.providers import Backend
from qiskit.test.mock import FakeParis
from qiskit.providers.aer import AerSimulator
+
class T2Hahn(BaseExperiment):
# Analysis class for experiment
-# __analysis_class__ = T2Analysis # need to add T2Analysis
+ # __analysis_class__ = T2Analysis # need to add T2Analysis
def __init__(
self,
- qubits: Union[int, Iterable[int]],
+ qubit: Union[int, Iterable[int]],
delays: Union[List[int], np.array], # need to change name?
gate_time: float,
n_echos: int = 1,
@@ -48,22 +48,22 @@ def __init__(
`X` and `Y`.
"""
# Initialize base experiment
- super().__init__(qubits)
- self._verify_parameters(qubits, delays, n_echos,gate_time)
- self._qubits = qubits
+ super().__init__(qubit)
+ self._verify_parameters(qubit, delays, n_echos, gate_time)
+ self._qubit = qubit
self._delays = delays
self._gate_time = gate_time
self._n_echos = n_echos
self._phase_alt_echo = phase_alt_echo
# Set configurable options
-# self.set_experiment_options(delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo)
-# self.set_analysis_options(data_processor=probability(
-# outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
-
+ # self.set_experiment_options(delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo)
+ # self.set_analysis_options(data_processor=probability(
+ # outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
- def _verify_parameters(self, qubits, delays, n_echos, gate_time):
+ @staticmethod
+ def _verify_parameters(self, qubit, delays, n_echos, gate_time):
"""Verify input correctness, raise QiskitError if needed"""
if any(delay <= 0 for delay in delays):
raise QiskitError(
@@ -76,8 +76,17 @@ def _verify_parameters(self, qubits, delays, n_echos, gate_time):
if any(delays[idx - 1] >= delays[idx] for idx in range(1, len(delays))):
raise QiskitError(f"The number of identity gates {delays} should " "be increasing.")
- if any(qubit < 0 for qubit in qubits):
- raise QiskitError(f"The index of the qubits {qubits} should " "be non-negative.")
+ # if any(qubit < 0 for qubit in qubits):
+ # raise QiskitError(f"The index of the qubits {qubits} should " "be non-negative.")
+ if isinstance(qubit, List):
+ if len(qubit) != 1:
+ raise QiskitError(f"The experiment if for 1 qubit. For multiple qubits, please use "
+ f"parallel experiments.")
+ if qubit[0] < 0:
+ raise QiskitError(f"The index of the qubit {qubit[0]} should " "be non-negative.")
+ else:
+ if qubit < 0:
+ raise QiskitError(f"The index of the qubit {qubit} should " "be non-negative.")
if n_echos < 1:
raise QiskitError(f"The number of echoes {n_echos} should " "be at least 1.")
@@ -85,55 +94,52 @@ def _verify_parameters(self, qubits, delays, n_echos, gate_time):
if gate_time <= 0:
raise QiskitError(f"The gate time {gate_time} should " "be positive.")
- def circuits(self, backend, qubits: Union[List[int], np.array],
- n_echos: int = 1,
- phase_alt_echo: bool = False):
+ def circuits(self, backend, qubit: Union[List[int], np.array],
+ n_echos: int = 1,
+ phase_alt_echo: bool = False):
conversion_factor = 1
-# if self.experiment_options.unit == "dt":
-# try:
-# dt_factor = getattr(backend._configuration, "dt")
-# conversion_factor = dt_factor
-# except AttributeError as no_dt:
-# raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
-# elif self.experiment_options.unit != "s":
-# apply_prefix(1, self.experiment_options.unit)
-# xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
- qr = QuantumRegister(max(qubits) + 1)
- cr = ClassicalRegister(len(qubits))
+ # if self.experiment_options.unit == "dt":
+ # try:
+ # dt_factor = getattr(backend._configuration, "dt")
+ # conversion_factor = dt_factor
+ # except AttributeError as no_dt:
+ # raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
+ # elif self.experiment_options.unit != "s":
+ # apply_prefix(1, self.experiment_options.unit)
+ # xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
+ qr = QuantumRegister(max(qubit) + 1)
+ cr = ClassicalRegister(len(qubit))
circuits = []
for circ_index, delay in enumerate(self._delays):
- circ = QuantumCircuit(max(qubits) + 1,len(qubits))
+ circ = QuantumCircuit(max(qubit) + 1, len(qubit))
circ.name = 't2circuit_' + str(circ_index) + '_0'
- for qind, qubit in enumerate(qubits):
-
- # First Y rotation in 90 degrees
- circ.ry(np.pi/2, qubit) # Bring to qubits to X Axis
- # circ = pad_id_gates(circ, qr, qubit, circ_length) # ids - waiting
-# circ.delay(delay, qr[qubit], self.experiment_options.unit)
- circ.delay(delay, qubit, 's')
- circ.rx(np.pi, qubit)
-
-# for echoid in range(n_echos - 1): # repeat
-# circ = pad_id_gates(circ, qr, qubit, 2 * delay) # ids
-# if phase_alt_echo and (not echoid % 2): # optionally
-# circ.x(qr[qubit]) # X
-# else:
-# circ.y(qr[qubit])
-
-# circ.delay(delay, qr[qubit], self.experiment_options.unit) # ids
- circ.delay(delay, qubit, 's')
- circ.ry(-np.pi/2, qubit) # Y90
- for qind, qubit in enumerate(qubits):
- circ.measure(qubit, qind) # measure
+ # First Y rotation in 90 degrees
+ circ.ry(np.pi / 2, qubit) # Bring to qubits to X Axis
+ # circ = pad_id_gates(circ, qr, qubit, circ_length) # ids - waiting
+ # circ.delay(delay, qr[qubit], self.experiment_options.unit)
+ circ.delay(delay, qubit, 's')
+ circ.rx(np.pi, qubit)
+
+ # for echoid in range(n_echos - 1): # repeat
+ # circ = pad_id_gates(circ, qr, qubit, 2 * delay) # ids
+ # if phase_alt_echo and (not echoid % 2): # optionally
+ # circ.x(qr[qubit]) # X
+ # else:
+ # circ.y(qr[qubit])
+
+ # circ.delay(delay, qr[qubit], self.experiment_options.unit) # ids
+ circ.delay(delay, qubit, 's')
+ circ.ry(-np.pi / 2, qubit) # Y90
+ circ.measure(qubit, 0) # measure
circ.metadata = {
"experiment_type": self._type,
"qubit": self.physical_qubits,
"xval": delay,
"unit": 's',
}
-# if self.experiment_options.unit == "dt":
-# circ.metadata["dt_factor"] = dt_factor
+ # if self.experiment_options.unit == "dt":
+ # circ.metadata["dt_factor"] = dt_factor
circuits.append(circ)
return circuits
\ No newline at end of file
From cb84b378c7fcacfec130eead4ec4095a61d23250 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 13 Sep 2021 11:42:10 +0300
Subject: [PATCH 07/93] added options instead of fields
---
.../library/characterization/T2Hahn.py | 22 +++++++++++++++++++
1 file changed, 22 insertions(+)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 22a63c5a71..3070531c80 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -24,6 +24,28 @@ class T2Hahn(BaseExperiment):
# Analysis class for experiment
# __analysis_class__ = T2Analysis # need to add T2Analysis
+ @classmethod
+ def _default_experiment_options(cls) -> Options:
+ """Default experiment options.
+
+ Experiment Options:
+ delays (Iterable[float]): Delay times of the experiments.
+ unit (str): Unit of the delay times. Supported units are
+ 's', 'ms', 'us', 'ns', 'ps', 'dt'.
+ osc_freq (float): Oscillation frequency offset in Hz.
+ n_echos (int); Number of echoes to preform.
+
+ """
+ options = super()._default_experiment_options()
+
+ options.delays = None
+ options.unit = "s"
+ options.osc_freq = 0.0
+ options.n_echoes = 1
+ options.phase_alt_echo = False
+
+ return options
+
def __init__(
self,
qubit: Union[int, Iterable[int]],
From a2be5f8d5d9277b3bb1766304ec33f332a4e181e Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 13 Sep 2021 16:05:52 +0300
Subject: [PATCH 08/93] Pylint and Black
---
.../library/characterization/T2Hahn.py | 250 ++++++++++--------
1 file changed, 136 insertions(+), 114 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 3070531c80..af15cb50e7 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -1,28 +1,60 @@
-from typing import Union, Iterable, Optional, List
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+T2Hahn Echo Experiment class.
+
+"""
+
+from typing import Union, Iterable, List, Optional
import numpy as np
-from numpy.random import Generator, default_rng
-
-from qiskit import QuantumCircuit, QiskitError, QuantumRegister, ClassicalRegister
-from qiskit.ignis.characterization.characterization_utils import pad_id_gates, time_to_ngates
-from qiskit.providers import Backend
-from qiskit.quantum_info import Clifford
+from qiskit import QuantumCircuit, QiskitError
+from qiskit.circuit import Measure
from qiskit.providers.options import Options
-from qiskit.circuit import Gate
-
-from qiskit_experiments.framework import BaseExperiment, ParallelExperiment
-from qiskit_experiments.curve_analysis.data_processing import probability
-from qiskit.utils import apply_prefix
-
from qiskit.providers import Backend
-from qiskit.test.mock import FakeParis
-from qiskit.providers.aer import AerSimulator
+
+from qiskit_experiments.framework import BaseExperiment
class T2Hahn(BaseExperiment):
+ r"""T2 Ramsey Experiment.
+
+ # section: overview
+
+ This experiment is used to estimate T2 noise of a single qubit.
+
+ See `Qiskit Textbook <https://qiskit.org/textbook/ch-quantum-hardware/\
+ calibrating-qubits-pulse.html>`_ for a more detailed explanation on
+ these properties.
+
+ This experiment consists of a series of circuits of the form
- # Analysis class for experiment
- # __analysis_class__ = T2Analysis # need to add T2Analysis
+ .. parsed-literal::
+
+ ┌─────────┐┌──────────┐┌───────┐┌──────────┐┌──────────┐┌─┐
+ q_0: ┤ RY(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RY(-π/2) ├┤M├
+ └─────────┘└──────────┘└───────┘└──────────┘└──────────┘└╥┘
+ c: 1/═════════════════════════════════════════════════════════╩═
+ 0
+
+ for each *t* from the specified delay times, where
+ :math:`\lambda =2 \pi \times {osc\_freq}`,
+ and the delays are specified by the user.
+ The circuits are run on the device or on a simulator backend.
+
+ # section: tutorial
+ :doc:`/tutorials/t2ramsey_characterization`
+
+ """
@classmethod
def _default_experiment_options(cls) -> Options:
@@ -31,137 +63,127 @@ def _default_experiment_options(cls) -> Options:
Experiment Options:
delays (Iterable[float]): Delay times of the experiments.
unit (str): Unit of the delay times. Supported units are
- 's', 'ms', 'us', 'ns', 'ps', 'dt'.
- osc_freq (float): Oscillation frequency offset in Hz.
+ 's'.
n_echos (int); Number of echoes to preform.
-
+ phase_alt_echo (bool): If to use alternate echoes (must have n_echoes greater than 1)
"""
options = super()._default_experiment_options()
+ options.qubit = []
options.delays = None
options.unit = "s"
- options.osc_freq = 0.0
- options.n_echoes = 1
+ options.n_echos = 1
options.phase_alt_echo = False
return options
def __init__(
- self,
- qubit: Union[int, Iterable[int]],
- delays: Union[List[int], np.array], # need to change name?
- gate_time: float,
- n_echos: int = 1,
- phase_alt_echo: bool = False,
+ self,
+ qubit: Union[int, Iterable[int]],
+ delays: Union[List[float], np.array], # need to change name?
+ n_echos: int = 1,
+ phase_alt_echo: bool = False,
):
- """Initialize a T2 experiment with Hahn echo.
-
+ """
+ **T2 - Hahn Echo class**
+ Initialize the T2 - Hahn Echo class
Args:
- num_of_gates:
- Each element of the list corresponds to a circuit.
- `num_of_gates[i]` is the number of identity gates in each section
- "t" of the pulse sequence in circuit no. i.
- Must be in an increasing order.
- gate_time: time of running a single identity gate.
- qubits: indices of the qubits whose
- T\ :sub:`2`:sup:`*`\ 's are to be measured.
- n_echos: number of echo gates (`X` or `Y`).
- phase_alt_echo: if True then alternate the echo between
- `X` and `Y`.
+ qubit: the qubit under test.
+ delays (List[float)): delay times of the experiments.
+ n_echos (int): Amount of Echoes to preform.
+ phase_alt_echo (bool): if to use alternate echo methods
+
+ Raises:
+ Error for invalid input.
"""
# Initialize base experiment
super().__init__(qubit)
- self._verify_parameters(qubit, delays, n_echos, gate_time)
- self._qubit = qubit
- self._delays = delays
- self._gate_time = gate_time
- self._n_echos = n_echos
- self._phase_alt_echo = phase_alt_echo
-
# Set configurable options
+ self.set_experiment_options(
+ delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo, qubit=qubit
+ )
+ self._verify_parameters()
- # self.set_experiment_options(delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo)
- # self.set_analysis_options(data_processor=probability(
- # outcome="0" * self.num_qubits)) # Need to rewrite after making the analysis class
+ def _verify_parameters(self):
+ """
+ Verify input correctness, raise QiskitError if needed.
+ Args:
+ qubit: the qubit under test.
- @staticmethod
- def _verify_parameters(self, qubit, delays, n_echos, gate_time):
- """Verify input correctness, raise QiskitError if needed"""
- if any(delay <= 0 for delay in delays):
+ Raises:
+ QiskitError : Error for invalid input.
+ """
+ if any(delay <= 0 for delay in self.experiment_options.delays):
raise QiskitError(
- f"The lengths list {delays} should only contain " "positive elements."
+ f"The lengths list {self.experiment_options.delays} should only contain "
+ "positive elements."
)
- if len(set(delays)) != len(delays):
+ if len(set(self.experiment_options.delays)) != len(self.experiment_options.delays):
raise QiskitError(
- f"The lengths list {delays} should not contain " "duplicate elements."
+ f"The lengths list {self.experiment_options.delays} should not contain "
+ "duplicate elements."
)
- if any(delays[idx - 1] >= delays[idx] for idx in range(1, len(delays))):
- raise QiskitError(f"The number of identity gates {delays} should " "be increasing.")
-
- # if any(qubit < 0 for qubit in qubits):
- # raise QiskitError(f"The index of the qubits {qubits} should " "be non-negative.")
- if isinstance(qubit, List):
- if len(qubit) != 1:
- raise QiskitError(f"The experiment if for 1 qubit. For multiple qubits, please use "
- f"parallel experiments.")
- if qubit[0] < 0:
- raise QiskitError(f"The index of the qubit {qubit[0]} should " "be non-negative.")
+
+ if any(
+ self.experiment_options.delays[idx - 1] >= self.experiment_options.delays[idx]
+ for idx in range(1, len(self.experiment_options.delays))
+ ):
+ raise QiskitError(
+ f"The number of identity gates {self.experiment_options.delays} should "
+ "be increasing."
+ )
+
+ if isinstance(self.experiment_options.qubit, list):
+ if len(self.experiment_options.qubit) != 1:
+ raise QiskitError(
+ "The experiment if for 1 qubit. For multiple qubits,"
+ " please use parallel experiments."
+ )
+ if self.experiment_options.qubit[0] < 0:
+ raise QiskitError(
+ f"The index of the qubit {self.experiment_options.qubit[0]} should "
+ "be non-negative."
+ )
else:
- if qubit < 0:
- raise QiskitError(f"The index of the qubit {qubit} should " "be non-negative.")
-
- if n_echos < 1:
- raise QiskitError(f"The number of echoes {n_echos} should " "be at least 1.")
-
- if gate_time <= 0:
- raise QiskitError(f"The gate time {gate_time} should " "be positive.")
-
- def circuits(self, backend, qubit: Union[List[int], np.array],
- n_echos: int = 1,
- phase_alt_echo: bool = False):
-
- conversion_factor = 1
- # if self.experiment_options.unit == "dt":
- # try:
- # dt_factor = getattr(backend._configuration, "dt")
- # conversion_factor = dt_factor
- # except AttributeError as no_dt:
- # raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
- # elif self.experiment_options.unit != "s":
- # apply_prefix(1, self.experiment_options.unit)
- # xdata = 2 * gate_time * np.array(num_of_gates) * n_echos
- qr = QuantumRegister(max(qubit) + 1)
- cr = ClassicalRegister(len(qubit))
+ if self.experiment_options.qubit < 0:
+ raise QiskitError(
+ f"The index of the qubit {self.experiment_options.qubit} should "
+ "be non-negative."
+ )
+
+ if self.experiment_options.n_echos < 1:
+ raise QiskitError(
+ f"The number of echoes {self.experiment_options.n_echos} should " "be at least 1."
+ )
+
+ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
+ """
+ Args:
+ backend: Optional, a backend object.
+
+ Returns:
+ The experiment circuits.
+
+ """
+
circuits = []
- for circ_index, delay in enumerate(self._delays):
+ qubit = self.experiment_options.qubit
+ for circ_index, delay in enumerate(self.experiment_options.delays):
circ = QuantumCircuit(max(qubit) + 1, len(qubit))
- circ.name = 't2circuit_' + str(circ_index) + '_0'
+ circ.name = "t2circuit_" + str(circ_index) + "_0"
# First Y rotation in 90 degrees
circ.ry(np.pi / 2, qubit) # Bring to qubits to X Axis
- # circ = pad_id_gates(circ, qr, qubit, circ_length) # ids - waiting
- # circ.delay(delay, qr[qubit], self.experiment_options.unit)
- circ.delay(delay, qubit, 's')
+ circ.delay(delay, qubit, self.experiment_options.unit)
circ.rx(np.pi, qubit)
-
- # for echoid in range(n_echos - 1): # repeat
- # circ = pad_id_gates(circ, qr, qubit, 2 * delay) # ids
- # if phase_alt_echo and (not echoid % 2): # optionally
- # circ.x(qr[qubit]) # X
- # else:
- # circ.y(qr[qubit])
-
- # circ.delay(delay, qr[qubit], self.experiment_options.unit) # ids
- circ.delay(delay, qubit, 's')
+ circ.delay(delay, qubit, self.experiment_options.unit)
circ.ry(-np.pi / 2, qubit) # Y90
- circ.measure(qubit, 0) # measure
+ circ.append(Measure(), qubit, [0]) # measure
circ.metadata = {
"experiment_type": self._type,
"qubit": self.physical_qubits,
"xval": delay,
- "unit": 's',
+ "unit": self.experiment_options.unit,
}
- # if self.experiment_options.unit == "dt":
- # circ.metadata["dt_factor"] = dt_factor
circuits.append(circ)
- return circuits
\ No newline at end of file
+ return circuits
From 2d95f331db1a771a818141b7f98949142a57e50a Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 13 Sep 2021 16:44:24 +0300
Subject: [PATCH 09/93] used base class attribute for qubit
used base class attribute to store the qubit and not through options
---
qiskit_experiments/library/characterization/T2Hahn.py | 7 +++----
1 file changed, 3 insertions(+), 4 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index af15cb50e7..1c0d09b9fa 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -69,7 +69,6 @@ def _default_experiment_options(cls) -> Options:
"""
options = super()._default_experiment_options()
- options.qubit = []
options.delays = None
options.unit = "s"
options.n_echos = 1
@@ -97,7 +96,7 @@ def __init__(
Error for invalid input.
"""
# Initialize base experiment
- super().__init__(qubit)
+ super().__init__([qubit])
# Set configurable options
self.set_experiment_options(
delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo, qubit=qubit
@@ -167,7 +166,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"""
circuits = []
- qubit = self.experiment_options.qubit
+ qubit = list(self._physical_qubits)
for circ_index, delay in enumerate(self.experiment_options.delays):
circ = QuantumCircuit(max(qubit) + 1, len(qubit))
circ.name = "t2circuit_" + str(circ_index) + "_0"
@@ -177,7 +176,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
circ.rx(np.pi, qubit)
circ.delay(delay, qubit, self.experiment_options.unit)
circ.ry(-np.pi / 2, qubit) # Y90
- circ.append(Measure(), qubit, [0]) # measure
+ circ.append(Measure(), self._physical_qubits, [0]) # measure
circ.metadata = {
"experiment_type": self._type,
"qubit": self.physical_qubits,
From be3d35260afd13f486f09743afd045e48daece16 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 22 Sep 2021 15:20:00 +0300
Subject: [PATCH 10/93] Updated code to be as in other experiments
---
.../library/characterization/T2Hahn.py | 39 +++++++------------
1 file changed, 15 insertions(+), 24 deletions(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 1c0d09b9fa..f623bcf03f 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -18,7 +18,6 @@
import numpy as np
from qiskit import QuantumCircuit, QiskitError
-from qiskit.circuit import Measure
from qiskit.providers.options import Options
from qiskit.providers import Backend
@@ -45,9 +44,7 @@ class T2Hahn(BaseExperiment):
└─────────┘└──────────┘└───────┘└──────────┘└──────────┘└╥┘
c: 1/═════════════════════════════════════════════════════════╩═
0
-
- for each *t* from the specified delay times, where
- :math:`\lambda =2 \pi \times {osc\_freq}`,
+ for each *t* from the specified delay times
and the delays are specified by the user.
The circuits are run on the device or on a simulator backend.
@@ -63,44 +60,38 @@ def _default_experiment_options(cls) -> Options:
Experiment Options:
delays (Iterable[float]): Delay times of the experiments.
unit (str): Unit of the delay times. Supported units are
- 's'.
- n_echos (int); Number of echoes to preform.
- phase_alt_echo (bool): If to use alternate echoes (must have n_echoes greater than 1)
+ 's', 'ms', 'us', 'ns', 'ps', 'dt'.
"""
options = super()._default_experiment_options()
options.delays = None
options.unit = "s"
- options.n_echos = 1
- options.phase_alt_echo = False
return options
def __init__(
self,
qubit: Union[int, Iterable[int]],
- delays: Union[List[float], np.array], # need to change name?
- n_echos: int = 1,
- phase_alt_echo: bool = False,
+ delays: Union[List[float], np.array],
+ unit: str = "s",
):
"""
**T2 - Hahn Echo class**
Initialize the T2 - Hahn Echo class
- Args:
- qubit: the qubit under test.
- delays (List[float)): delay times of the experiments.
- n_echos (int): Amount of Echoes to preform.
- phase_alt_echo (bool): if to use alternate echo methods
+ Args:
+ qubit: the qubit under test.
+ delays (List[float)): delay times of the experiments.
+ unit: Optional, time unit of `delays`.
+ Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. The unit is
+ used for both T2Ramsey and for the frequency.
Raises:
- Error for invalid input.
+ QiskitError : Error for invalid input.
"""
# Initialize base experiment
super().__init__([qubit])
# Set configurable options
- self.set_experiment_options(
- delays=delays, n_echos=n_echos, phase_alt_echo=phase_alt_echo, qubit=qubit
- )
+ self.set_experiment_options(delays=delays, unit=unit, qubit=qubit)
self._verify_parameters()
def _verify_parameters(self):
@@ -168,7 +159,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
circuits = []
qubit = list(self._physical_qubits)
for circ_index, delay in enumerate(self.experiment_options.delays):
- circ = QuantumCircuit(max(qubit) + 1, len(qubit))
+ circ = QuantumCircuit(1, 1)
circ.name = "t2circuit_" + str(circ_index) + "_0"
# First Y rotation in 90 degrees
circ.ry(np.pi / 2, qubit) # Bring to qubits to X Axis
@@ -176,10 +167,10 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
circ.rx(np.pi, qubit)
circ.delay(delay, qubit, self.experiment_options.unit)
circ.ry(-np.pi / 2, qubit) # Y90
- circ.append(Measure(), self._physical_qubits, [0]) # measure
+ circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
- "qubit": self.physical_qubits,
+ "qubit": self.physical_qubits[0],
"xval": delay,
"unit": self.experiment_options.unit,
}
From 0dc3777ae504bef2e066e0659f08512cbcd2f968 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 22 Sep 2021 15:24:36 +0300
Subject: [PATCH 11/93] Added the Analysis class
---
qiskit_experiments/library/characterization/T2Hahn.py | 2 ++
1 file changed, 2 insertions(+)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index f623bcf03f..23098e021c 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -20,6 +20,7 @@
from qiskit import QuantumCircuit, QiskitError
from qiskit.providers.options import Options
from qiskit.providers import Backend
+from .t2hahn_analysis import T2HahnAnalysis
from qiskit_experiments.framework import BaseExperiment
@@ -52,6 +53,7 @@ class T2Hahn(BaseExperiment):
:doc:`/tutorials/t2ramsey_characterization`
"""
+ __analysis_class__ = T2HahnAnalysis
@classmethod
def _default_experiment_options(cls) -> Options:
From 600173bdf5ae7f6fdaac87b647fb4bea1e2a969c Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Thu, 23 Sep 2021 14:44:35 +0300
Subject: [PATCH 12/93] Base analysis class draft + fixes
---
.../library/characterization/T2Hahn.py | 25 +-
.../characterization/t2hahn_analysis.py | 228 ++++++++++++++++++
2 files changed, 229 insertions(+), 24 deletions(-)
create mode 100644 qiskit_experiments/library/characterization/t2hahn_analysis.py
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index 23098e021c..ef01a4ab8a 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -93,7 +93,7 @@ def __init__(
# Initialize base experiment
super().__init__([qubit])
# Set configurable options
- self.set_experiment_options(delays=delays, unit=unit, qubit=qubit)
+ self.set_experiment_options(delays=delays, unit=unit)
self._verify_parameters()
def _verify_parameters(self):
@@ -125,29 +125,6 @@ def _verify_parameters(self):
"be increasing."
)
- if isinstance(self.experiment_options.qubit, list):
- if len(self.experiment_options.qubit) != 1:
- raise QiskitError(
- "The experiment if for 1 qubit. For multiple qubits,"
- " please use parallel experiments."
- )
- if self.experiment_options.qubit[0] < 0:
- raise QiskitError(
- f"The index of the qubit {self.experiment_options.qubit[0]} should "
- "be non-negative."
- )
- else:
- if self.experiment_options.qubit < 0:
- raise QiskitError(
- f"The index of the qubit {self.experiment_options.qubit} should "
- "be non-negative."
- )
-
- if self.experiment_options.n_echos < 1:
- raise QiskitError(
- f"The number of echoes {self.experiment_options.n_echos} should " "be at least 1."
- )
-
def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"""
Args:
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
new file mode 100644
index 0000000000..96f049b3d7
--- /dev/null
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -0,0 +1,228 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+T2Hahn analysis class.
+"""
+
+from typing import List, Optional, Tuple, Dict, Union, Any
+import dataclasses
+import numpy as np
+
+from qiskit.utils import apply_prefix
+from qiskit_experiments.framework import (
+ BaseAnalysis,
+ Options,
+ ExperimentData,
+ AnalysisResultData,
+ FitVal,
+)
+from qiskit_experiments.curve_analysis import curve_fit, plot_curve_fit, plot_errorbar, plot_scatter
+from qiskit_experiments.curve_analysis.curve_fit import process_curve_data
+from qiskit_experiments.curve_analysis.data_processing import level2_probability
+
+
+# pylint: disable = invalid-name
+class T2HahnAnalysis(BaseAnalysis):
+ r"""
+ T2 Hahn result analysis class.
+
+ # section: fit_model
+ This class is used to analyze the results of a T2 Hahn experiment.
+ The probability of measuring :math:`|+\rangle` state is assumed to be of the form
+
+ .. math::
+
+ f(t) = a\mathrm{e}^{-t / T_2^*} + b
+
+ # section: fit_parameters
+
+ defpar a:
+ desc: Amplitude. Height of the decay curve.
+ init_guess: 0.5
+ bounds: [-0.5, 1.5]
+
+ defpar b:
+ desc: Offset. Base line of the decay curve.
+ init_guess: 0.5
+ bounds: [-0.5, 1.5]
+
+ defpar T_2^*:
+ desc: Represents the rate of decay.
+ init_guess: the mean of the input delays.
+ bounds: [0, np.inf]
+
+ """
+
+ @classmethod
+ def _default_options(cls):
+ r"""Default analysis options.
+
+ Analysis Options:
+ user_p0 (List[Float]): user guesses for the fit parameters
+ :math:`(a, b, T_2^*)`.
+ user_bounds (Tuple[List[float], List[float]]): Lower and upper bounds
+ for the fit parameters.
+ plot (bool): Create a graph if and only if True.
+ """
+ return Options(user_p0=None, user_bounds=None)
+
+ # pylint: disable=arguments-differ, unused-argument
+ def _run_analysis(
+ self,
+ experiment_data: ExperimentData,
+ user_p0: Optional[Dict[str, float]] = None,
+ user_bounds: Optional[Tuple[List[float], List[float]]] = None,
+ plot: bool = False,
+ ax: Optional["AxesSubplot"] = None,
+ **kwargs,
+ ) -> Tuple[List[AnalysisResultData], List["matplotlib.figure.Figure"]]:
+ r"""Calculate T2Hahn experiment.
+
+ Args:
+ experiment_data (ExperimentData): the experiment data to analyze
+ user_p0: contains initial values given by the user, for the
+ fit parameters :math:`(a, t2hahn, b)`
+ user_bounds: lower and upper bounds on the parameters in p0,
+ given by the user.
+ The first tuple is the lower bounds,
+ The second tuple is the upper bounds.
+ For both params, the order is :math:`a, t2hahn, b`.
+ plot: if True, create the plot, otherwise, do not create the plot.
+ ax: the plot object
+ **kwargs: additional parameters for curve fit.
+
+ Returns:
+ The analysis result with the estimated :math:`t2hahn`
+ The graph of the function.
+ """
+
+ def T2_fit_fun(x, a, t2hahn, c):
+ """Decay cosine fit function"""
+ return a * np.exp(-x / t2hahn) + c
+
+ def _format_plot(ax, unit, fit_result, conversion_factor):
+ """Format curve fit plot"""
+ # Formatting
+ ax.tick_params(labelsize=14)
+ ax.set_xlabel("Delay (s)", fontsize=12)
+ ax.ticklabel_format(axis="x", style="sci", scilimits=(0, 0))
+ ax.set_ylabel("Probability of measuring 0", fontsize=12)
+ t2hahn = fit_result["popt"][1] / conversion_factor
+ t2hahn_err = fit_result["popt_err"][1] / conversion_factor
+ box_text = "$T_2Hahn$ = {:.2f} \u00B1 {:.2f} {}".format(t2hahn, t2hahn_err, unit)
+ bbox_props = dict(boxstyle="square,pad=0.3", fc="white", ec="black", lw=1)
+ ax.text(
+ 0.6,
+ 0.9,
+ box_text,
+ ha="center",
+ va="center",
+ size=12,
+ bbox=bbox_props,
+ transform=ax.transAxes,
+ )
+ return ax
+
+ # implementation of _run_analysis
+
+ data = experiment_data.data()
+ circ_metadata = data[0]["metadata"]
+ unit = circ_metadata["unit"]
+ conversion_factor = circ_metadata.get("dt_factor", None)
+ if conversion_factor is None:
+ conversion_factor = 1 if unit in ("s", "dt") else apply_prefix(1, unit)
+
+ xdata, ydata, sigma = process_curve_data(data, lambda datum: level2_probability(datum, "0"))
+
+ t2hahn_estimate = np.mean(xdata) # Maybe need to change?
+ p0, bounds = self._t2hahn_default_params(
+ conversion_factor, user_p0, user_bounds, t2hahn_estimate
+ )
+ xdata *= conversion_factor
+ fit_result = curve_fit(
+ T2_fit_fun, xdata, ydata, p0=list(p0.values()), sigma=sigma, bounds=bounds
+ )
+ fit_result = dataclasses.asdict(fit_result)
+ fit_result["circuit_unit"] = unit
+ if unit == "dt":
+ fit_result["dt"] = conversion_factor
+ quality = self._fit_quality(
+ fit_result["popt"], fit_result["popt_err"], fit_result["reduced_chisq"]
+ )
+ chisq = fit_result["reduced_chisq"]
+
+ if plot:
+ ax = plot_curve_fit(T2_fit_fun, fit_result, ax=ax)
+ ax = plot_scatter(xdata, ydata, ax=ax)
+ ax = plot_errorbar(xdata, ydata, sigma, ax=ax)
+ _format_plot(ax, unit, fit_result, conversion_factor)
+ figures = [ax.get_figure()]
+ else:
+ figures = None
+
+ # Output unit is 'sec', regardless of the unit used in the input
+ result_t2hahn = AnalysisResultData(
+ "T2hahn",
+ value=FitVal(fit_result["popt"][1], fit_result["popt_err"][1], "s"),
+ quality=quality,
+ chisq=chisq,
+ extra=fit_result,
+ )
+
+ return [result_t2hahn], figures
+
+ def _t2hahn_default_params(
+ self,
+ conversion_factor,
+ user_p0=None,
+ user_bounds=None,
+ t2hahn_input=None,
+ ) -> Tuple[Dict[str, Union[float, Any]], Union[List[List[Union[Union[float, int], Any]]], Any]]:
+ """Default fit parameters for oscillation data.
+
+ Note that :math:`T_2` unit is converted to 'sec' so the
+ output will be given in 'sec'.
+ """
+ if user_p0 is None:
+ a = 0.5
+ t2hahn = t2hahn_input * conversion_factor
+ b = 0.5
+ else:
+ a = user_p0["A"]
+ t2hahn = user_p0["T2hahn"] * conversion_factor
+ b = user_p0["B"]
+ p0 = {"a_guess": a, "T2": t2hahn, "b_guess": b}
+
+ if user_bounds is None:
+ a_bounds = [-0.5, 1.5]
+ t2hahn_bounds = [0, np.inf]
+ b_bounds = [-0.5, 1.5]
+ bounds = [
+ [a_bounds[i], t2hahn_bounds[i], b_bounds[i]]
+ for i in range(2)
+ ]
+ else:
+ bounds = user_bounds
+ return (p0, bounds)
+
+ @staticmethod
+ def _fit_quality(fit_out, fit_err, reduced_chisq):
+ # pylint: disable = too-many-boolean-expressions
+ if (
+ (reduced_chisq < 3)
+ and (fit_err[0] is None or fit_err[0] < 0.1 * fit_out[0])
+ and (fit_err[1] is None or fit_err[1] < 0.1 * fit_out[1])
+ and (fit_err[2] is None or fit_err[2] < 0.1 * fit_out[2])
+ ):
+ return "good"
+ else:
+ return "bad"
From 36854ccdf401fa3f95265761b5485842147cda57 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Thu, 23 Sep 2021 15:14:09 +0300
Subject: [PATCH 13/93] Update T2Hahn.py
---
qiskit_experiments/library/characterization/T2Hahn.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/T2Hahn.py
index ef01a4ab8a..754a65df77 100644
--- a/qiskit_experiments/library/characterization/T2Hahn.py
+++ b/qiskit_experiments/library/characterization/T2Hahn.py
@@ -139,7 +139,6 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
qubit = list(self._physical_qubits)
for circ_index, delay in enumerate(self.experiment_options.delays):
circ = QuantumCircuit(1, 1)
- circ.name = "t2circuit_" + str(circ_index) + "_0"
# First Y rotation in 90 degrees
circ.ry(np.pi / 2, qubit) # Bring to qubits to X Axis
circ.delay(delay, qubit, self.experiment_options.unit)
From e23ee47d66320b4f35abb26945a345e1820bfa3d Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 26 Sep 2021 14:16:30 +0300
Subject: [PATCH 14/93] Update t2hahn.py
---
.../library/characterization/{T2Hahn.py => t2hahn.py} | 0
1 file changed, 0 insertions(+), 0 deletions(-)
rename qiskit_experiments/library/characterization/{T2Hahn.py => t2hahn.py} (100%)
diff --git a/qiskit_experiments/library/characterization/T2Hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
similarity index 100%
rename from qiskit_experiments/library/characterization/T2Hahn.py
rename to qiskit_experiments/library/characterization/t2hahn.py
From 231203fc38d71ba1c0be16a7a139ebf57a41b772 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 29 Sep 2021 13:04:24 +0300
Subject: [PATCH 15/93] Update t2hahn.py
---
.../library/characterization/t2hahn.py | 12 ++++++++++++
1 file changed, 12 insertions(+)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 754a65df77..7e87419e43 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -18,6 +18,7 @@
import numpy as np
from qiskit import QuantumCircuit, QiskitError
+from qiskit.utils import apply_prefix
from qiskit.providers.options import Options
from qiskit.providers import Backend
from .t2hahn_analysis import T2HahnAnalysis
@@ -134,6 +135,15 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
The experiment circuits.
"""
+ conversion_factor = 1
+ if self.experiment_options.unit == "dt":
+ try:
+ dt_factor = getattr(backend._configuration, "dt")
+ conversion_factor = dt_factor
+ except AttributeError as no_dt:
+ raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
+ elif self.experiment_options.unit != "s":
+ conversion_factor = apply_prefix(1, self.experiment_options.unit)
circuits = []
qubit = list(self._physical_qubits)
@@ -152,6 +162,8 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"xval": delay,
"unit": self.experiment_options.unit,
}
+ if self.experiment_options.unit == "dt":
+ circ.metadata["dt_factor"] = dt_factor
circuits.append(circ)
return circuits
From b7dceaec6d6a43d10d841a38532621e634396315 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 29 Sep 2021 16:39:23 +0300
Subject: [PATCH 16/93] Added Backend template from t2ramsey
---
qiskit_experiments/test/t2hahn_backend.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 qiskit_experiments/test/t2hahn_backend.py
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
new file mode 100644
index 0000000000..e69de29bb2
From bfa67e276ccc9b9de5e39d3b31ff052250238fac Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 29 Sep 2021 16:42:09 +0300
Subject: [PATCH 17/93] Update t2hahn_backend.py
---
qiskit_experiments/test/t2hahn_backend.py | 155 ++++++++++++++++++++++
1 file changed, 155 insertions(+)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index e69de29bb2..380e54e9ca 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -0,0 +1,155 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+"""
+T2HahnBackend class.
+Temporary backend to be used for t2hahn experiment
+"""
+
+import numpy as np
+
+from qiskit.providers import BackendV1
+from qiskit.providers.models import QasmBackendConfiguration
+from qiskit.result import Result
+from qiskit_experiments.framework import Options
+from qiskit_experiments.test.utils import FakeJob
+
+# Fix seed for simulations
+SEED = 9000
+
+
+class T2HahnBackend(BackendV1):
+ """
+ A simple and primitive backend, to be run by the T2Hahn tests
+ """
+
+ def __init__(
+ self,
+ p0=None,
+ initial_prob_plus=None,
+ readout0to1=None,
+ readout1to0=None,
+ conversion_factor=1,
+ ):
+ """
+ Initialize the T2Hahn backend
+ """
+ conversion_factor_in_ns = conversion_factor * 1e9 if conversion_factor is not None else None
+ configuration = QasmBackendConfiguration(
+ backend_name="T2Hahn_simulator",
+ backend_version="0",
+ n_qubits=int(1e6),
+ basis_gates=["barrier", "h", "p", "delay", "measure"],
+ gates=[],
+ local=True,
+ simulator=True,
+ conditional=False,
+ open_pulse=False,
+ memory=False,
+ max_shots=int(1e6),
+ coupling_map=None,
+ dt=conversion_factor_in_ns,
+ )
+
+ self._t2hahn = p0["T2"]
+ self._a_param = p0["A"]
+ self._freq = p0["f"]
+ self._phi = p0["phi"]
+ self._b_param = p0["B"]
+ self._initial_prob_plus = initial_prob_plus
+ self._readout0to1 = readout0to1
+ self._readout1to0 = readout1to0
+ self._conversion_factor = conversion_factor
+ self._rng = np.random.default_rng(0)
+ super().__init__(configuration)
+
+ @classmethod
+ def _default_options(cls):
+ """Default options of the test backend."""
+ return Options(shots=1024)
+
+ # pylint: disable = arguments-differ
+ def run(self, run_input, **options):
+ """
+ Run the T2Hahn backend
+ """
+ self.options.update_options(**options)
+ shots = self.options.get("shots")
+ result = {
+ "backend_name": "T2Hahn backend",
+ "backend_version": "0",
+ "qobj_id": 0,
+ "job_id": 0,
+ "success": True,
+ "results": [],
+ }
+ for circ in run_input:
+ nqubits = circ.num_qubits
+ qubit_indices = {bit: idx for idx, bit in enumerate(circ.qubits)}
+ clbit_indices = {bit: idx for idx, bit in enumerate(circ.clbits)}
+ counts = dict()
+ if self._readout0to1 is None:
+ ro01 = np.zeros(nqubits)
+ else:
+ ro01 = self._readout0to1
+ if self._readout1to0 is None:
+ ro10 = np.zeros(nqubits)
+ else:
+ ro10 = self._readout1to0
+ for _ in range(shots):
+ if self._initial_prob_plus is None:
+ prob_plus = np.ones(nqubits)
+ else:
+ prob_plus = self._initial_prob_plus.copy()
+
+ clbits = np.zeros(circ.num_clbits, dtype=int)
+ for op, qargs, cargs in circ.data:
+ qubit = qubit_indices[qargs[0]]
+
+ if op.name == "delay":
+ delay = op.params[0]
+ t2hahn = self._t2hahn[qubit] * self._conversion_factor
+ freq = self._freq[qubit]
+
+ prob_plus[qubit] = (
+ self._a_param[qubit]
+ * np.exp(-delay / t2hahn)
+ * np.cos(2 * np.pi * freq * delay + self._phi[qubit])
+ + self._b_param[qubit]
+ )
+
+ if op.name == "measure":
+ # we measure in |+> basis which is the same as measuring |0>
+ meas_res = self._rng.binomial(
+ 1,
+ (1 - prob_plus[qubit]) * (1 - ro10[qubit])
+ + prob_plus[qubit] * ro01[qubit],
+ )
+ clbit = clbit_indices[cargs[0]]
+ clbits[clbit] = meas_res
+
+ clstr = ""
+ for clbit in clbits[::-1]:
+ clstr = clstr + str(clbit)
+
+ if clstr in counts:
+ counts[clstr] += 1
+ else:
+ counts[clstr] = 1
+ result["results"].append(
+ {
+ "shots": shots,
+ "success": True,
+ "header": {"metadata": circ.metadata},
+ "data": {"counts": counts},
+ }
+ )
+ return FakeJob(self, Result.from_dict(result))
From 3d8a1c74501e18e28f7af6c9bcfd7cea786c4519 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 3 Oct 2021 13:32:45 +0300
Subject: [PATCH 18/93] fixed doc string and input check function
---
.../library/characterization/t2hahn.py | 15 +++------------
1 file changed, 3 insertions(+), 12 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 7e87419e43..08cadee588 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -51,7 +51,7 @@ class T2Hahn(BaseExperiment):
The circuits are run on the device or on a simulator backend.
# section: tutorial
- :doc:`/tutorials/t2ramsey_characterization`
+ :doc:`/tutorials/t2hahn_characterization`
"""
__analysis_class__ = T2HahnAnalysis
@@ -106,10 +106,10 @@ def _verify_parameters(self):
Raises:
QiskitError : Error for invalid input.
"""
- if any(delay <= 0 for delay in self.experiment_options.delays):
+ if any(delay < 0 for delay in self.experiment_options.delays):
raise QiskitError(
f"The lengths list {self.experiment_options.delays} should only contain "
- "positive elements."
+ "non-negative elements."
)
if len(set(self.experiment_options.delays)) != len(self.experiment_options.delays):
raise QiskitError(
@@ -117,15 +117,6 @@ def _verify_parameters(self):
"duplicate elements."
)
- if any(
- self.experiment_options.delays[idx - 1] >= self.experiment_options.delays[idx]
- for idx in range(1, len(self.experiment_options.delays))
- ):
- raise QiskitError(
- f"The number of identity gates {self.experiment_options.delays} should "
- "be increasing."
- )
-
def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"""
Args:
From 1db4e9edc447e73d86e551227a560e94cb0e7f85 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 3 Oct 2021 13:34:18 +0300
Subject: [PATCH 19/93] the circuit is now working on qubit '0' exclusively
---
qiskit_experiments/library/characterization/t2hahn.py | 11 +++++------
1 file changed, 5 insertions(+), 6 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 08cadee588..a959ca42a9 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -137,15 +137,14 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
conversion_factor = apply_prefix(1, self.experiment_options.unit)
circuits = []
- qubit = list(self._physical_qubits)
for circ_index, delay in enumerate(self.experiment_options.delays):
circ = QuantumCircuit(1, 1)
# First Y rotation in 90 degrees
- circ.ry(np.pi / 2, qubit) # Bring to qubits to X Axis
- circ.delay(delay, qubit, self.experiment_options.unit)
- circ.rx(np.pi, qubit)
- circ.delay(delay, qubit, self.experiment_options.unit)
- circ.ry(-np.pi / 2, qubit) # Y90
+ circ.ry(np.pi / 2, 0) # Bring to qubits to X Axis
+ circ.delay(delay, 0, self.experiment_options.unit)
+ circ.rx(np.pi, 0)
+ circ.delay(delay, 0, self.experiment_options.unit)
+ circ.ry(-np.pi / 2, 0) # Y90
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
From f2e47995e7182201b51852e284acc4b26619c3e5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 3 Oct 2021 13:42:28 +0300
Subject: [PATCH 20/93] Update t2hahn.py
---
qiskit_experiments/library/characterization/t2hahn.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index a959ca42a9..706aa5dcab 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -15,15 +15,16 @@
"""
from typing import Union, Iterable, List, Optional
-
import numpy as np
+
from qiskit import QuantumCircuit, QiskitError
from qiskit.utils import apply_prefix
from qiskit.providers.options import Options
from qiskit.providers import Backend
+from qiskit_experiments.framework import BaseExperiment
from .t2hahn_analysis import T2HahnAnalysis
-from qiskit_experiments.framework import BaseExperiment
+
class T2Hahn(BaseExperiment):
@@ -83,7 +84,7 @@ def __init__(
Initialize the T2 - Hahn Echo class
Args:
qubit: the qubit under test.
- delays (List[float)): delay times of the experiments.
+ delays: delay times of the experiments.
unit: Optional, time unit of `delays`.
Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. The unit is
used for both T2Ramsey and for the frequency.
@@ -125,6 +126,8 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
Returns:
The experiment circuits.
+ Raises:
+ AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
conversion_factor = 1
if self.experiment_options.unit == "dt":
@@ -137,7 +140,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
conversion_factor = apply_prefix(1, self.experiment_options.unit)
circuits = []
- for circ_index, delay in enumerate(self.experiment_options.delays):
+ for delay in self.experiment_options.delays:
circ = QuantumCircuit(1, 1)
# First Y rotation in 90 degrees
circ.ry(np.pi / 2, 0) # Bring to qubits to X Axis
From ae4ce881660609768e594c557f08e1fde71110cb Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 4 Oct 2021 13:52:11 +0300
Subject: [PATCH 21/93] Added operation for 'RX' gate
---
qiskit_experiments/test/t2hahn_backend.py | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 380e54e9ca..e90bd05b59 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -126,6 +126,11 @@ def run(self, run_input, **options):
+ self._b_param[qubit]
)
+ if op.name == "rx":
+ prob_plus[qubit] = prob_plus[qubit] * np.cos(op.params[0]/2) - \
+ (1-prob_plus[qubit]) * np.sin(op.params[0]/2)
+ # prob_plus[qubit] = 1- prob_plus[qubit]
+
if op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
meas_res = self._rng.binomial(
From 1cf5e900d31f9e8d610abc7750257135e6859361 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 11:18:31 +0300
Subject: [PATCH 22/93] Removed duplicate length from verify parameters
---
qiskit_experiments/library/characterization/t2hahn.py | 5 -----
1 file changed, 5 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 706aa5dcab..cb66597695 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -112,11 +112,6 @@ def _verify_parameters(self):
f"The lengths list {self.experiment_options.delays} should only contain "
"non-negative elements."
)
- if len(set(self.experiment_options.delays)) != len(self.experiment_options.delays):
- raise QiskitError(
- f"The lengths list {self.experiment_options.delays} should not contain "
- "duplicate elements."
- )
def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"""
From 047a430b6dedb5e38de8a3db98ae5ea148c31dd8 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 11:23:17 +0300
Subject: [PATCH 23/93] changed documentation suggestions
---
qiskit_experiments/library/characterization/t2hahn.py | 6 ++----
1 file changed, 2 insertions(+), 4 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index cb66597695..1eab45729f 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -28,7 +28,7 @@
class T2Hahn(BaseExperiment):
- r"""T2 Ramsey Experiment.
+ r"""T2 Hahn Echo Experiment.
# section: overview
@@ -80,14 +80,12 @@ def __init__(
unit: str = "s",
):
"""
- **T2 - Hahn Echo class**
Initialize the T2 - Hahn Echo class
Args:
qubit: the qubit under test.
delays: delay times of the experiments.
unit: Optional, time unit of `delays`.
- Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. The unit is
- used for both T2Ramsey and for the frequency.
+ Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.
Raises:
QiskitError : Error for invalid input.
From 8c9ca60e75dc4fee038058105f715c06040ee78c Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 11:24:49 +0300
Subject: [PATCH 24/93] changed documanttion
---
qiskit_experiments/library/characterization/t2hahn_analysis.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 96f049b3d7..66d90b8005 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -36,7 +36,7 @@ class T2HahnAnalysis(BaseAnalysis):
T2 Hahn result analysis class.
# section: fit_model
- This class is used to analyze the results of a T2 Hahn experiment.
+ This class is used to analyze the results of a T2 Hahn Echo experiment.
The probability of measuring :math:`|+\rangle` state is assumed to be of the form
.. math::
From 95a28b21cd2b5859b73c881d185a0e337ce2ca29 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 11:27:11 +0300
Subject: [PATCH 25/93] Removed * from T_2
---
.../library/characterization/t2hahn_analysis.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 66d90b8005..155ddedb6f 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -41,7 +41,7 @@ class T2HahnAnalysis(BaseAnalysis):
.. math::
- f(t) = a\mathrm{e}^{-t / T_2^*} + b
+ f(t) = a\mathrm{e}^{-t / T_2} + b
# section: fit_parameters
@@ -55,7 +55,7 @@ class T2HahnAnalysis(BaseAnalysis):
init_guess: 0.5
bounds: [-0.5, 1.5]
- defpar T_2^*:
+ defpar T_2:
desc: Represents the rate of decay.
init_guess: the mean of the input delays.
bounds: [0, np.inf]
@@ -68,7 +68,7 @@ def _default_options(cls):
Analysis Options:
user_p0 (List[Float]): user guesses for the fit parameters
- :math:`(a, b, T_2^*)`.
+ :math:`(a, b, T_2)`.
user_bounds (Tuple[List[float], List[float]]): Lower and upper bounds
for the fit parameters.
plot (bool): Create a graph if and only if True.
From 3ab1b85cd424ce8032cdab75e00d5636b1a9d5d3 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 11:59:02 +0300
Subject: [PATCH 26/93] changed basis gate 'h', 'p' to 'ry', 'rx'
---
qiskit_experiments/test/t2hahn_backend.py | 21 ++++++++++++---------
1 file changed, 12 insertions(+), 9 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index e90bd05b59..f91db70db2 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -47,7 +47,7 @@ def __init__(
backend_name="T2Hahn_simulator",
backend_version="0",
n_qubits=int(1e6),
- basis_gates=["barrier", "h", "p", "delay", "measure"],
+ basis_gates=["barrier", "ry", "rx", "delay", "measure"],
gates=[],
local=True,
simulator=True,
@@ -106,9 +106,9 @@ def run(self, run_input, **options):
ro10 = self._readout1to0
for _ in range(shots):
if self._initial_prob_plus is None:
- prob_plus = np.ones(nqubits)
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": "0"}
else:
- prob_plus = self._initial_prob_plus.copy()
+ qubit_state = self._initial_prob_plus.copy()
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
@@ -119,12 +119,15 @@ def run(self, run_input, **options):
t2hahn = self._t2hahn[qubit] * self._conversion_factor
freq = self._freq[qubit]
- prob_plus[qubit] = (
- self._a_param[qubit]
- * np.exp(-delay / t2hahn)
- * np.cos(2 * np.pi * freq * delay + self._phi[qubit])
- + self._b_param[qubit]
- )
+ if qubit_state["XY plain"] == True:
+
+
+ qubit_state[qubit] = (
+ self._a_param[qubit]
+ * np.exp(-delay / t2hahn)
+ * np.cos(2 * np.pi * freq * delay + self._phi[qubit])
+ + self._b_param[qubit]
+ )
if op.name == "rx":
prob_plus[qubit] = prob_plus[qubit] * np.cos(op.params[0]/2) - \
From 17178d32587356c13d9229a1785dd1eb031c8b5d Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 24 Oct 2021 16:48:52 +0300
Subject: [PATCH 27/93] Update t2hahn_backend.py
---
qiskit_experiments/test/t2hahn_backend.py | 38 ++++++++++++++++-------
1 file changed, 26 insertions(+), 12 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index f91db70db2..d809a0dc74 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -68,7 +68,7 @@ def __init__(
self._readout0to1 = readout0to1
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
- self._rng = np.random.default_rng(0)
+ self._rng = np.random.default_rng(seed=SEED)
super().__init__(configuration)
@classmethod
@@ -106,7 +106,7 @@ def run(self, run_input, **options):
ro10 = self._readout1to0
for _ in range(shots):
if self._initial_prob_plus is None:
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": "0"}
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
else:
qubit_state = self._initial_prob_plus.copy()
@@ -120,19 +120,33 @@ def run(self, run_input, **options):
freq = self._freq[qubit]
if qubit_state["XY plain"] == True:
-
-
- qubit_state[qubit] = (
- self._a_param[qubit]
- * np.exp(-delay / t2hahn)
- * np.cos(2 * np.pi * freq * delay + self._phi[qubit])
- + self._b_param[qubit]
+ prob_noise = 1 - (
+ self._a_param[qubit]
+ * np.exp(-delay / t2hahn)
+ + self._b_param[qubit]
)
+ if self._rng.random() < prob_noise:
+ if self._rng.random() < 0.5:
+ qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
+ else:
+ qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
if op.name == "rx":
- prob_plus[qubit] = prob_plus[qubit] * np.cos(op.params[0]/2) - \
- (1-prob_plus[qubit]) * np.sin(op.params[0]/2)
- # prob_plus[qubit] = 1- prob_plus[qubit]
+ if qubit_state["XY plain"] == True:
+ qubit_state[qubit] = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
+ elif qubit_state["qubit state"] == 0:
+ qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ else:
+ qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
+
+ # #Need to change
+ # if op.name == "ry":
+ # if qubit_state["XY plain"] == True:
+ # qubit_state[qubit] = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
+ # elif qubit_state["qubit state"] == 0:
+ # qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ # else:
+ # qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
if op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
From bcaf3803f42e2b3d03cb0854eaebec5999962fc9 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 25 Oct 2021 11:17:54 +0300
Subject: [PATCH 28/93] Added Ry, Rx and measure gates
---
qiskit_experiments/test/t2hahn_backend.py | 45 ++++++++++++-----------
1 file changed, 24 insertions(+), 21 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index d809a0dc74..08098f9f0b 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -114,12 +114,13 @@ def run(self, run_input, **options):
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
+ # The noise will only be applied if we are in the XY plain.
if op.name == "delay":
delay = op.params[0]
t2hahn = self._t2hahn[qubit] * self._conversion_factor
freq = self._freq[qubit]
- if qubit_state["XY plain"] == True:
+ if qubit_state["XY plain"]:
prob_noise = 1 - (
self._a_param[qubit]
* np.exp(-delay / t2hahn)
@@ -127,34 +128,36 @@ def run(self, run_input, **options):
)
if self._rng.random() < prob_noise:
if self._rng.random() < 0.5:
- qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
else:
- qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
if op.name == "rx":
- if qubit_state["XY plain"] == True:
- qubit_state[qubit] = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
+ if qubit_state["XY plain"]:
+ qubit_state = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
elif qubit_state["qubit state"] == 0:
- qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
else:
- qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
-
- # #Need to change
- # if op.name == "ry":
- # if qubit_state["XY plain"] == True:
- # qubit_state[qubit] = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
- # elif qubit_state["qubit state"] == 0:
- # qubit_state[qubit] = {"qubit state": 1, "XY plain": False, "Theta": 0}
- # else:
- # qubit_state[qubit] = {"qubit state": 0, "XY plain": False, "Theta": 0}
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
+
+ #Need to change
+ if op.name == "ry":
+ if qubit_state["XY plain"]:
+ if qubit_state["Theta"] == 0:
+ qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ else:
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
+ elif qubit_state["qubit state"] == 0:
+ qubit_state = {"qubit state": 1, "XY plain": True, "Theta": 0}
+ else:
+ qubit_state = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
if op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
- meas_res = self._rng.binomial(
- 1,
- (1 - prob_plus[qubit]) * (1 - ro10[qubit])
- + prob_plus[qubit] * ro01[qubit],
- )
+ if qubit_state["XY plain"]:
+ meas_res = (self._rng.random() < 0.5)
+ else:
+ meas_res = qubit_state["qubit state"]
clbit = clbit_indices[cargs[0]]
clbits[clbit] = meas_res
From e9a0e3eac6f186c49ab1d4a065886cb794c923dd Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 25 Oct 2021 13:46:29 +0300
Subject: [PATCH 29/93] Changed every t2ramsy to t2hahn (WIP)
---
qiskit_experiments/test/t2hahn_backend.py | 1 -
test/test_t2hahn.py | 195 ++++++++++++++++++++++
2 files changed, 195 insertions(+), 1 deletion(-)
create mode 100644 test/test_t2hahn.py
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 08098f9f0b..9db3d3a6d3 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -140,7 +140,6 @@ def run(self, run_input, **options):
else:
qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
- #Need to change
if op.name == "ry":
if qubit_state["XY plain"]:
if qubit_state["Theta"] == 0:
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
new file mode 100644
index 0000000000..43c4e37f6c
--- /dev/null
+++ b/test/test_t2hahn.py
@@ -0,0 +1,195 @@
+# This code is part of Qiskit.
+#
+# (C) Copyright IBM 2021.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""
+Test T2Hahn experiment
+"""
+import numpy as np
+
+from qiskit.utils import apply_prefix
+from qiskit.test import QiskitTestCase
+from qiskit_experiments.framework import ParallelExperiment
+from qiskit_experiments.library.characterization import t2hahn as T2Hahn
+from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+
+
+class TestT2Hahn(QiskitTestCase):
+ """Test T2Hahn experiment"""
+
+ __tolerance__ = 0.1
+
+ def test_t2hahn_run_end2end(self):
+ """
+ Run the T2Hahn backend on all possible units
+ """
+ for unit in ["s", "ms", "us", "ns", "dt"]:
+ if unit in ("s", "dt"):
+ dt_factor = 1
+ else:
+ dt_factor = apply_prefix(1, unit)
+ osc_freq = 0.1 / dt_factor
+ estimated_t2hahn = 20
+ estimated_freq = osc_freq * 1.001
+ # Set up the circuits
+ qubit = 0
+ if unit == "dt": # dt requires integer values for delay
+ delays = list(range(1, 46))
+ else:
+ delays = np.append(
+ (np.linspace(1.0, 15.0, num=15)).astype(float),
+ (np.linspace(16.0, 45.0, num=59)).astype(float),
+ )
+ exp = T2Hahn(qubit, delays, unit=unit, osc_freq=osc_freq)
+ default_p0 = {
+ "A": 0.5,
+ "T2star": estimated_t2hahn,
+ "f": estimated_freq,
+ "phi": 0,
+ "B": 0.5,
+ }
+ for user_p0 in [default_p0, None]:
+ exp.set_analysis_options(user_p0=user_p0, plot=True)
+ backend = T2HahnBackend(
+ p0={
+ "A": [0.5],
+ "T2star": [estimated_t2hahn],
+ "f": [estimated_freq],
+ "phi": [0.0],
+ "B": [0.5],
+ },
+ initial_prob_plus=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ conversion_factor=dt_factor,
+ )
+
+ expdata = exp.run(backend=backend, shots=2000)
+ expdata.block_for_results() # Wait for job/analysis to finish.
+ result = expdata.analysis_results()
+ self.assertAlmostEqual(
+ result[0].value.value,
+ estimated_t2hahn * dt_factor,
+ delta=TestT2Hahn.__tolerance__ * result[0].value.value,
+ )
+ self.assertAlmostEqual(
+ result[1].value.value,
+ estimated_freq,
+ delta=TestT2Hahn.__tolerance__ * result[1].value.value,
+ )
+ for res in result:
+ self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
+
+ def test_t2hahn_parallel(self):
+ """
+ Test parallel experiments of T2Hahn using a simulator.
+ """
+ t2hahn = [30, 25]
+ estimated_freq = [0.1, 0.12]
+ delays = [list(range(1, 60)), list(range(1, 50))]
+
+ osc_freq = [0.11, 0.11]
+
+ exp0 = T2Hahn(0, delays[0], osc_freq=osc_freq[0])
+ exp2 = T2Hahn(2, delays[1], osc_freq=osc_freq[1])
+ par_exp = ParallelExperiment([exp0, exp2])
+
+ p0 = {
+ "A": [0.5, None, 0.5],
+ "T2star": [t2hahn[0], None, t2hahn[1]],
+ "f": [estimated_freq[0], None, estimated_freq[1]],
+ "phi": [0, None, 0],
+ "B": [0.5, None, 0.5],
+ }
+
+ backend = T2HahnBackend(p0)
+ expdata = par_exp.run(backend=backend, shots=1000)
+ expdata.block_for_results()
+
+ for i in range(2):
+ sub_res = expdata.component_experiment_data(i).analysis_results()
+ self.assertAlmostEqual(
+ sub_res[0].value.value,
+ t2hahn[i],
+ delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
+ )
+ self.assertAlmostEqual(
+ sub_res[1].value.value,
+ estimated_freq[i],
+ delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
+ )
+ for res in sub_res:
+ self.assertEqual(
+ res.quality,
+ "good",
+ "Result quality bad for experiment on qubit " + str(i),
+ )
+
+ def test_t2hahn_concat_2_experiments(self):
+ """
+ Concatenate the data from 2 separate experiments
+ """
+ unit = "s"
+ estimated_t2hahn = 30
+ estimated_freq = 0.09
+ # First experiment
+ qubit = 0
+ delays0 = list(range(1, 60, 2))
+ osc_freq = 0.08
+
+ exp0 = T2Hahn(qubit, delays0, unit=unit, osc_freq=osc_freq)
+ default_p0 = {
+ "A": 0.5,
+ "T2star": estimated_t2hahn,
+ "f": estimated_freq,
+ "phi": 0,
+ "B": 0.5,
+ }
+ exp0.set_analysis_options(user_p0=default_p0)
+ backend = T2HahnBackend(
+ p0={
+ "A": [0.5],
+ "T2star": [estimated_t2hahn],
+ "f": [estimated_freq],
+ "phi": [0.0],
+ "B": [0.5],
+ },
+ initial_prob_plus=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ conversion_factor=1,
+ )
+
+ # run circuits
+ expdata0 = exp0.run(backend=backend, shots=1000)
+ expdata0.block_for_results()
+ results0 = expdata0.analysis_results()
+
+ # second experiment
+ delays1 = list(range(2, 65, 2))
+ exp1 = T2Hahn(qubit, delays1, unit=unit)
+ exp1.set_analysis_options(user_p0=default_p0)
+ expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
+ expdata1.block_for_results()
+ results1 = expdata1.analysis_results()
+
+ self.assertAlmostEqual(
+ results1[0].value.value,
+ estimated_t2hahn,
+ delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ )
+ self.assertAlmostEqual(
+ results1[1].value.value,
+ estimated_freq,
+ delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ )
+ self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
+ self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
From 9c9548b878f6a0b0ba90f9e7679274aabd56f210 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 25 Oct 2021 16:49:27 +0300
Subject: [PATCH 30/93] changed "T2Hahn" to "T2"
---
qiskit_experiments/library/characterization/t2hahn_analysis.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 155ddedb6f..40c8a0ac98 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -198,7 +198,7 @@ def _t2hahn_default_params(
b = 0.5
else:
a = user_p0["A"]
- t2hahn = user_p0["T2hahn"] * conversion_factor
+ t2hahn = user_p0["T2"] * conversion_factor
b = user_p0["B"]
p0 = {"a_guess": a, "T2": t2hahn, "b_guess": b}
From 1f89a4deade655934d3ba3631b0adcd98bb74233 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 25 Oct 2021 18:15:23 +0300
Subject: [PATCH 31/93] added tests (still not working)
---
qiskit_experiments/test/t2hahn_backend.py | 38 ++++++++---------------
test/test_t2hahn.py | 28 ++++++++++-------
2 files changed, 30 insertions(+), 36 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 9db3d3a6d3..1013b66ff9 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -32,12 +32,12 @@ class T2HahnBackend(BackendV1):
"""
def __init__(
- self,
- p0=None,
- initial_prob_plus=None,
- readout0to1=None,
- readout1to0=None,
- conversion_factor=1,
+ self,
+ p0=None,
+ initial_prob_plus=None,
+ readout0to1=None,
+ readout1to0=None,
+ conversion_factor=1,
):
"""
Initialize the T2Hahn backend
@@ -64,7 +64,7 @@ def __init__(
self._freq = p0["f"]
self._phi = p0["phi"]
self._b_param = p0["B"]
- self._initial_prob_plus = initial_prob_plus
+ self._initial_prob_plus = None
self._readout0to1 = readout0to1
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
@@ -96,20 +96,9 @@ def run(self, run_input, **options):
qubit_indices = {bit: idx for idx, bit in enumerate(circ.qubits)}
clbit_indices = {bit: idx for idx, bit in enumerate(circ.clbits)}
counts = dict()
- if self._readout0to1 is None:
- ro01 = np.zeros(nqubits)
- else:
- ro01 = self._readout0to1
- if self._readout1to0 is None:
- ro10 = np.zeros(nqubits)
- else:
- ro10 = self._readout1to0
- for _ in range(shots):
- if self._initial_prob_plus is None:
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
- else:
- qubit_state = self._initial_prob_plus.copy()
+ for _ in range(shots):
+ qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
@@ -159,15 +148,14 @@ def run(self, run_input, **options):
meas_res = qubit_state["qubit state"]
clbit = clbit_indices[cargs[0]]
clbits[clbit] = meas_res
-
clstr = ""
for clbit in clbits[::-1]:
clstr = clstr + str(clbit)
- if clstr in counts:
- counts[clstr] += 1
- else:
- counts[clstr] = 1
+ if clstr in counts:
+ counts[clstr] += 1
+ else:
+ counts[clstr] = 1
result["results"].append(
{
"shots": shots,
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 43c4e37f6c..31f57730d1 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -18,9 +18,11 @@
from qiskit.utils import apply_prefix
from qiskit.test import QiskitTestCase
from qiskit_experiments.framework import ParallelExperiment
-from qiskit_experiments.library.characterization import t2hahn as T2Hahn
+from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+import unittest
+
class TestT2Hahn(QiskitTestCase):
"""Test T2Hahn experiment"""
@@ -40,7 +42,7 @@ def test_t2hahn_run_end2end(self):
estimated_t2hahn = 20
estimated_freq = osc_freq * 1.001
# Set up the circuits
- qubit = 0
+ qubit = 1
if unit == "dt": # dt requires integer values for delay
delays = list(range(1, 46))
else:
@@ -48,10 +50,10 @@ def test_t2hahn_run_end2end(self):
(np.linspace(1.0, 15.0, num=15)).astype(float),
(np.linspace(16.0, 45.0, num=59)).astype(float),
)
- exp = T2Hahn(qubit, delays, unit=unit, osc_freq=osc_freq)
+ exp = T2Hahn(qubit, delays, unit=unit)
default_p0 = {
"A": 0.5,
- "T2star": estimated_t2hahn,
+ "T2": estimated_t2hahn,
"f": estimated_freq,
"phi": 0,
"B": 0.5,
@@ -61,7 +63,7 @@ def test_t2hahn_run_end2end(self):
backend = T2HahnBackend(
p0={
"A": [0.5],
- "T2star": [estimated_t2hahn],
+ "T2": [estimated_t2hahn],
"f": [estimated_freq],
"phi": [0.0],
"B": [0.5],
@@ -98,13 +100,13 @@ def test_t2hahn_parallel(self):
osc_freq = [0.11, 0.11]
- exp0 = T2Hahn(0, delays[0], osc_freq=osc_freq[0])
- exp2 = T2Hahn(2, delays[1], osc_freq=osc_freq[1])
+ exp0 = T2Hahn(0, delays[0])
+ exp2 = T2Hahn(2, delays[1])
par_exp = ParallelExperiment([exp0, exp2])
p0 = {
"A": [0.5, None, 0.5],
- "T2star": [t2hahn[0], None, t2hahn[1]],
+ "T2": [t2hahn[0], None, t2hahn[1]],
"f": [estimated_freq[0], None, estimated_freq[1]],
"phi": [0, None, 0],
"B": [0.5, None, 0.5],
@@ -145,10 +147,10 @@ def test_t2hahn_concat_2_experiments(self):
delays0 = list(range(1, 60, 2))
osc_freq = 0.08
- exp0 = T2Hahn(qubit, delays0, unit=unit, osc_freq=osc_freq)
+ exp0 = T2Hahn(qubit, delays0, unit=unit)
default_p0 = {
"A": 0.5,
- "T2star": estimated_t2hahn,
+ "T2": estimated_t2hahn,
"f": estimated_freq,
"phi": 0,
"B": 0.5,
@@ -157,7 +159,7 @@ def test_t2hahn_concat_2_experiments(self):
backend = T2HahnBackend(
p0={
"A": [0.5],
- "T2star": [estimated_t2hahn],
+ "T2": [estimated_t2hahn],
"f": [estimated_freq],
"phi": [0.0],
"B": [0.5],
@@ -193,3 +195,7 @@ def test_t2hahn_concat_2_experiments(self):
)
self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+
+
+if __name__ == "__main__":
+ unittest.main()
From cfc6bdcf8ad5344848075927dde67c4dc8d2dbd4 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 7 Nov 2021 17:43:31 +0200
Subject: [PATCH 32/93] Delay now applied once
By using the model for this backhand if I do two delays we get the following probability for measuring '0':
P('0') = (1-P(err))^2 + 0.5 * P(err) * (2 - P(err) )
we can see that the fitting isn't the same as one we expected (as P(err) is approximate by e^(-t/[tau]) so this probability isn't the same. hence' we will use 1 noise for delay so we will get the approximated fitting)
---
qiskit_experiments/test/t2hahn_backend.py | 5 +-
test/test_t2hahn.py | 279 +++++++++++++---------
2 files changed, 173 insertions(+), 111 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 1013b66ff9..d18ee60889 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -99,16 +99,17 @@ def run(self, run_input, **options):
for _ in range(shots):
qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
+ delayCheck = True
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
# The noise will only be applied if we are in the XY plain.
- if op.name == "delay":
+ if op.name == "delay" and delayCheck:
+ delayCheck = False
delay = op.params[0]
t2hahn = self._t2hahn[qubit] * self._conversion_factor
freq = self._freq[qubit]
-
if qubit_state["XY plain"]:
prob_noise = 1 - (
self._a_param[qubit]
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 31f57730d1..a6827bc3d7 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -24,12 +24,16 @@
import unittest
+
+# Imports for computer
+from qiskit import IBMQ
+
class TestT2Hahn(QiskitTestCase):
"""Test T2Hahn experiment"""
__tolerance__ = 0.1
- def test_t2hahn_run_end2end(self):
+ def _test_t2hahn_run_end2end(self):
"""
Run the T2Hahn backend on all possible units
"""
@@ -42,7 +46,7 @@ def test_t2hahn_run_end2end(self):
estimated_t2hahn = 20
estimated_freq = osc_freq * 1.001
# Set up the circuits
- qubit = 1
+ qubit = 0
if unit == "dt": # dt requires integer values for delay
delays = list(range(1, 46))
else:
@@ -74,7 +78,7 @@ def test_t2hahn_run_end2end(self):
conversion_factor=dt_factor,
)
- expdata = exp.run(backend=backend, shots=2000)
+ expdata = exp.run(backend=backend, shots=4000)
expdata.block_for_results() # Wait for job/analysis to finish.
result = expdata.analysis_results()
self.assertAlmostEqual(
@@ -82,120 +86,177 @@ def test_t2hahn_run_end2end(self):
estimated_t2hahn * dt_factor,
delta=TestT2Hahn.__tolerance__ * result[0].value.value,
)
- self.assertAlmostEqual(
- result[1].value.value,
- estimated_freq,
- delta=TestT2Hahn.__tolerance__ * result[1].value.value,
- )
+ # self.assertAlmostEqual(
+ # result[1].value.value,
+ # estimated_freq,
+ # delta=TestT2Hahn.__tolerance__ * result[1].value.value,
+ # )
for res in result:
self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
- def test_t2hahn_parallel(self):
+ def test_t2hahn_run_end2end_qc_backhands(self):
"""
- Test parallel experiments of T2Hahn using a simulator.
+ Run the T2Hahn backend on all possible units
"""
- t2hahn = [30, 25]
- estimated_freq = [0.1, 0.12]
- delays = [list(range(1, 60)), list(range(1, 50))]
-
- osc_freq = [0.11, 0.11]
-
- exp0 = T2Hahn(0, delays[0])
- exp2 = T2Hahn(2, delays[1])
- par_exp = ParallelExperiment([exp0, exp2])
-
- p0 = {
- "A": [0.5, None, 0.5],
- "T2": [t2hahn[0], None, t2hahn[1]],
- "f": [estimated_freq[0], None, estimated_freq[1]],
- "phi": [0, None, 0],
- "B": [0.5, None, 0.5],
- }
-
- backend = T2HahnBackend(p0)
- expdata = par_exp.run(backend=backend, shots=1000)
- expdata.block_for_results()
-
- for i in range(2):
- sub_res = expdata.component_experiment_data(i).analysis_results()
- self.assertAlmostEqual(
- sub_res[0].value.value,
- t2hahn[i],
- delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
- )
- self.assertAlmostEqual(
- sub_res[1].value.value,
- estimated_freq[i],
- delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
- )
- for res in sub_res:
- self.assertEqual(
- res.quality,
- "good",
- "Result quality bad for experiment on qubit " + str(i),
+ print("\n REAL COMPUTER DATA \n")
+ provider = IBMQ.get_provider(hub='ibm-q')
+ backend = provider.get_backend('ibmq_armonk')
+ for unit in ["s", "ms", "us", "ns", "dt"]:
+ if unit in ("s", "dt"):
+ dt_factor = 1
+ else:
+ dt_factor = apply_prefix(1, unit)
+ osc_freq = 0.1 / dt_factor
+ estimated_t2hahn = backend.properties().qubits[0][1].value
+ estimated_freq = osc_freq * 1.001
+ # Set up the circuits
+ qubit = 0
+ if unit == "dt": # dt requires integer values for delay
+ delays = list(range(1, 46))
+ else:
+ delays = np.append(
+ (np.linspace(1.0, 15.0, num=15)).astype(float),
+ (np.linspace(16.0, 45.0, num=59)).astype(float),
)
+ exp = T2Hahn(qubit, delays, unit=unit)
+ default_p0 = {
+ "A": 0.5,
+ "T2": estimated_t2hahn,
+ "f": estimated_freq,
+ "phi": 0,
+ "B": 0.5,
+ }
+ for user_p0 in [default_p0, None]:
+ exp.set_analysis_options(user_p0=user_p0, plot=True)
- def test_t2hahn_concat_2_experiments(self):
- """
- Concatenate the data from 2 separate experiments
- """
- unit = "s"
- estimated_t2hahn = 30
- estimated_freq = 0.09
- # First experiment
- qubit = 0
- delays0 = list(range(1, 60, 2))
- osc_freq = 0.08
-
- exp0 = T2Hahn(qubit, delays0, unit=unit)
- default_p0 = {
- "A": 0.5,
- "T2": estimated_t2hahn,
- "f": estimated_freq,
- "phi": 0,
- "B": 0.5,
- }
- exp0.set_analysis_options(user_p0=default_p0)
- backend = T2HahnBackend(
- p0={
- "A": [0.5],
- "T2": [estimated_t2hahn],
- "f": [estimated_freq],
- "phi": [0.0],
- "B": [0.5],
- },
- initial_prob_plus=[0.0],
- readout0to1=[0.02],
- readout1to0=[0.02],
- conversion_factor=1,
- )
-
- # run circuits
- expdata0 = exp0.run(backend=backend, shots=1000)
- expdata0.block_for_results()
- results0 = expdata0.analysis_results()
-
- # second experiment
- delays1 = list(range(2, 65, 2))
- exp1 = T2Hahn(qubit, delays1, unit=unit)
- exp1.set_analysis_options(user_p0=default_p0)
- expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
- expdata1.block_for_results()
- results1 = expdata1.analysis_results()
-
- self.assertAlmostEqual(
- results1[0].value.value,
- estimated_t2hahn,
- delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
- )
- self.assertAlmostEqual(
- results1[1].value.value,
- estimated_freq,
- delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
- )
- self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
- self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+ print("before run")
+ expdata = exp.run(backend=backend, shots=1000)
+ print("after run")
+ expdata.block_for_results() # Wait for job/analysis to finish.
+ print("before analysis")
+ result = expdata.analysis_results()
+ print("after analysis")
+ self.assertAlmostEqual(
+ result[0].value.value,
+ estimated_t2hahn * dt_factor,
+ delta=TestT2Hahn.__tolerance__ * result[0].value.value,
+ )
+ for res in result:
+ self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
+ #
+ # def test_t2hahn_parallel(self):
+ # """
+ # Test parallel experiments of T2Hahn using a simulator.
+ # """
+ # t2hahn = [30, 25]
+ # estimated_freq = [0.1, 0.12]
+ # delays = [list(range(1, 60)), list(range(1, 50))]
+ #
+ # osc_freq = [0.11, 0.11]
+ #
+ # exp0 = T2Hahn(0, delays[0])
+ # exp2 = T2Hahn(2, delays[1])
+ # par_exp = ParallelExperiment([exp0, exp2])
+ #
+ # p0 = {
+ # "A": [0.5, None, 0.5],
+ # "T2": [t2hahn[0], None, t2hahn[1]],
+ # "f": [estimated_freq[0], None, estimated_freq[1]],
+ # "phi": [0, None, 0],
+ # "B": [0.5, None, 0.5],
+ # }
+ #
+ # backend = T2HahnBackend(p0)
+ # expdata = par_exp.run(backend=backend, shots=1000)
+ # expdata.block_for_results()
+ #
+ # for i in range(2):
+ # sub_res = expdata.component_experiment_data(i).analysis_results()
+ # self.assertAlmostEqual(
+ # sub_res[0].value.value,
+ # t2hahn[i],
+ # delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
+ # )
+ # # self.assertAlmostEqual(
+ # # sub_res[1].value.value,
+ # # estimated_freq[i],
+ # # delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
+ # # )
+ # for res in sub_res:
+ # self.assertEqual(
+ # res.quality,
+ # "good",
+ # "Result quality bad for experiment on qubit " + str(i),
+ # )
+ #
+ # def test_t2hahn_concat_2_experiments(self):
+ # """
+ # Concatenate the data from 2 separate experiments
+ # """
+ # unit = "s"
+ # estimated_t2hahn = 30
+ # estimated_freq = 0.09
+ # # First experiment
+ # qubit = 0
+ # delays0 = list(range(1, 60, 2))
+ # osc_freq = 0.08
+ #
+ # exp0 = T2Hahn(qubit, delays0, unit=unit)
+ # default_p0 = {
+ # "A": 0.5,
+ # "T2": estimated_t2hahn,
+ # "f": estimated_freq,
+ # "phi": 0,
+ # "B": 0.5,
+ # }
+ # exp0.set_analysis_options(user_p0=default_p0)
+ # backend = T2HahnBackend(
+ # p0={
+ # "A": [0.5],
+ # "T2": [estimated_t2hahn],
+ # "f": [estimated_freq],
+ # "phi": [0.0],
+ # "B": [0.5],
+ # },
+ # initial_prob_plus=[0.0],
+ # readout0to1=[0.02],
+ # readout1to0=[0.02],
+ # conversion_factor=1,
+ # )
+ #
+ # # run circuits
+ # expdata0 = exp0.run(backend=backend, shots=1000)
+ # expdata0.block_for_results()
+ # results0 = expdata0.analysis_results()
+ #
+ # # second experiment
+ # delays1 = list(range(2, 65, 2))
+ # exp1 = T2Hahn(qubit, delays1, unit=unit)
+ # exp1.set_analysis_options(user_p0=default_p0)
+ # expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
+ # expdata1.block_for_results()
+ # results1 = expdata1.analysis_results()
+ #
+ # self.assertAlmostEqual(
+ # results1[0].value.value,
+ # estimated_t2hahn,
+ # delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ # )
+ # self.assertAlmostEqual(
+ # results1[1].value.value,
+ # estimated_freq,
+ # delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ # )
+ # self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
+ # self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
if __name__ == "__main__":
+ # from qiskit import IBMQ
+
+ TOKEN = "0a4bf707be8c1bc15bfaf12e51e73a52dcf51acab45a5ed26ef84b672fa956285b7d68f97c2200faeea91149b7149c81466a8a97025c03c55a7a3883065328e0"
+ IBMQ.save_account(TOKEN)
+ IBMQ.load_account()
+ IBMQ.providers()
+
unittest.main()
From 8818a3403fa17c87c4bdb1508158a4bdebe18ccf Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 8 Nov 2021 17:29:14 +0200
Subject: [PATCH 33/93] added tests for T2hahn Echo
---
.../characterization/t2hahn_analysis.py | 10 +-
qiskit_experiments/test/t2hahn_backend.py | 9 +-
test/test_t2hahn.py | 234 ++++++------------
3 files changed, 82 insertions(+), 171 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 40c8a0ac98..316516ad8a 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -107,7 +107,7 @@ def _run_analysis(
def T2_fit_fun(x, a, t2hahn, c):
"""Decay cosine fit function"""
- return a * np.exp(-x / t2hahn) + c
+ return a * np.exp(-2 * x / t2hahn) + c
def _format_plot(ax, unit, fit_result, conversion_factor):
"""Format curve fit plot"""
@@ -206,10 +206,10 @@ def _t2hahn_default_params(
a_bounds = [-0.5, 1.5]
t2hahn_bounds = [0, np.inf]
b_bounds = [-0.5, 1.5]
- bounds = [
- [a_bounds[i], t2hahn_bounds[i], b_bounds[i]]
- for i in range(2)
- ]
+ bounds = (
+ [a_bounds[0], t2hahn_bounds[0], b_bounds[0]],
+ [a_bounds[1], t2hahn_bounds[1], b_bounds[1]]
+ )
else:
bounds = user_bounds
return (p0, bounds)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index d18ee60889..dd47885fb2 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -61,7 +61,6 @@ def __init__(
self._t2hahn = p0["T2"]
self._a_param = p0["A"]
- self._freq = p0["f"]
self._phi = p0["phi"]
self._b_param = p0["B"]
self._initial_prob_plus = None
@@ -106,16 +105,10 @@ def run(self, run_input, **options):
# The noise will only be applied if we are in the XY plain.
if op.name == "delay" and delayCheck:
- delayCheck = False
delay = op.params[0]
t2hahn = self._t2hahn[qubit] * self._conversion_factor
- freq = self._freq[qubit]
if qubit_state["XY plain"]:
- prob_noise = 1 - (
- self._a_param[qubit]
- * np.exp(-delay / t2hahn)
- + self._b_param[qubit]
- )
+ prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
if self._rng.random() < 0.5:
qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index a6827bc3d7..24f16aacf6 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -21,10 +21,6 @@
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-import unittest
-
-
-
# Imports for computer
from qiskit import IBMQ
@@ -33,7 +29,7 @@ class TestT2Hahn(QiskitTestCase):
__tolerance__ = 0.1
- def _test_t2hahn_run_end2end(self):
+ def test_t2hahn_run_end2end(self):
"""
Run the T2Hahn backend on all possible units
"""
@@ -44,7 +40,6 @@ def _test_t2hahn_run_end2end(self):
dt_factor = apply_prefix(1, unit)
osc_freq = 0.1 / dt_factor
estimated_t2hahn = 20
- estimated_freq = osc_freq * 1.001
# Set up the circuits
qubit = 0
if unit == "dt": # dt requires integer values for delay
@@ -58,7 +53,6 @@ def _test_t2hahn_run_end2end(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "f": estimated_freq,
"phi": 0,
"B": 0.5,
}
@@ -68,7 +62,6 @@ def _test_t2hahn_run_end2end(self):
p0={
"A": [0.5],
"T2": [estimated_t2hahn],
- "f": [estimated_freq],
"phi": [0.0],
"B": [0.5],
},
@@ -78,64 +71,9 @@ def _test_t2hahn_run_end2end(self):
conversion_factor=dt_factor,
)
- expdata = exp.run(backend=backend, shots=4000)
- expdata.block_for_results() # Wait for job/analysis to finish.
- result = expdata.analysis_results()
- self.assertAlmostEqual(
- result[0].value.value,
- estimated_t2hahn * dt_factor,
- delta=TestT2Hahn.__tolerance__ * result[0].value.value,
- )
- # self.assertAlmostEqual(
- # result[1].value.value,
- # estimated_freq,
- # delta=TestT2Hahn.__tolerance__ * result[1].value.value,
- # )
- for res in result:
- self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
-
- def test_t2hahn_run_end2end_qc_backhands(self):
- """
- Run the T2Hahn backend on all possible units
- """
- print("\n REAL COMPUTER DATA \n")
- provider = IBMQ.get_provider(hub='ibm-q')
- backend = provider.get_backend('ibmq_armonk')
- for unit in ["s", "ms", "us", "ns", "dt"]:
- if unit in ("s", "dt"):
- dt_factor = 1
- else:
- dt_factor = apply_prefix(1, unit)
- osc_freq = 0.1 / dt_factor
- estimated_t2hahn = backend.properties().qubits[0][1].value
- estimated_freq = osc_freq * 1.001
- # Set up the circuits
- qubit = 0
- if unit == "dt": # dt requires integer values for delay
- delays = list(range(1, 46))
- else:
- delays = np.append(
- (np.linspace(1.0, 15.0, num=15)).astype(float),
- (np.linspace(16.0, 45.0, num=59)).astype(float),
- )
- exp = T2Hahn(qubit, delays, unit=unit)
- default_p0 = {
- "A": 0.5,
- "T2": estimated_t2hahn,
- "f": estimated_freq,
- "phi": 0,
- "B": 0.5,
- }
- for user_p0 in [default_p0, None]:
- exp.set_analysis_options(user_p0=user_p0, plot=True)
-
- print("before run")
expdata = exp.run(backend=backend, shots=1000)
- print("after run")
expdata.block_for_results() # Wait for job/analysis to finish.
- print("before analysis")
result = expdata.analysis_results()
- print("after analysis")
self.assertAlmostEqual(
result[0].value.value,
estimated_t2hahn * dt_factor,
@@ -143,7 +81,7 @@ def test_t2hahn_run_end2end_qc_backhands(self):
)
for res in result:
self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
- #
+
# def test_t2hahn_parallel(self):
# """
# Test parallel experiments of T2Hahn using a simulator.
@@ -165,98 +103,78 @@ def test_t2hahn_run_end2end_qc_backhands(self):
# "phi": [0, None, 0],
# "B": [0.5, None, 0.5],
# }
+ # properties_backend = [p0, p0]
#
- # backend = T2HahnBackend(p0)
- # expdata = par_exp.run(backend=backend, shots=1000)
- # expdata.block_for_results()
- #
- # for i in range(2):
- # sub_res = expdata.component_experiment_data(i).analysis_results()
- # self.assertAlmostEqual(
- # sub_res[0].value.value,
- # t2hahn[i],
- # delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
- # )
- # # self.assertAlmostEqual(
- # # sub_res[1].value.value,
- # # estimated_freq[i],
- # # delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
- # # )
- # for res in sub_res:
- # self.assertEqual(
- # res.quality,
- # "good",
- # "Result quality bad for experiment on qubit " + str(i),
- # )
- #
- # def test_t2hahn_concat_2_experiments(self):
- # """
- # Concatenate the data from 2 separate experiments
- # """
- # unit = "s"
- # estimated_t2hahn = 30
- # estimated_freq = 0.09
- # # First experiment
- # qubit = 0
- # delays0 = list(range(1, 60, 2))
- # osc_freq = 0.08
- #
- # exp0 = T2Hahn(qubit, delays0, unit=unit)
- # default_p0 = {
- # "A": 0.5,
- # "T2": estimated_t2hahn,
- # "f": estimated_freq,
- # "phi": 0,
- # "B": 0.5,
- # }
- # exp0.set_analysis_options(user_p0=default_p0)
- # backend = T2HahnBackend(
- # p0={
- # "A": [0.5],
- # "T2": [estimated_t2hahn],
- # "f": [estimated_freq],
- # "phi": [0.0],
- # "B": [0.5],
- # },
- # initial_prob_plus=[0.0],
- # readout0to1=[0.02],
- # readout1to0=[0.02],
- # conversion_factor=1,
- # )
- #
- # # run circuits
- # expdata0 = exp0.run(backend=backend, shots=1000)
- # expdata0.block_for_results()
- # results0 = expdata0.analysis_results()
- #
- # # second experiment
- # delays1 = list(range(2, 65, 2))
- # exp1 = T2Hahn(qubit, delays1, unit=unit)
- # exp1.set_analysis_options(user_p0=default_p0)
- # expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
- # expdata1.block_for_results()
- # results1 = expdata1.analysis_results()
- #
- # self.assertAlmostEqual(
- # results1[0].value.value,
- # estimated_t2hahn,
- # delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
- # )
- # self.assertAlmostEqual(
- # results1[1].value.value,
- # estimated_freq,
- # delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
- # )
- # self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
- # self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
-
-
-if __name__ == "__main__":
- # from qiskit import IBMQ
-
- TOKEN = "0a4bf707be8c1bc15bfaf12e51e73a52dcf51acab45a5ed26ef84b672fa956285b7d68f97c2200faeea91149b7149c81466a8a97025c03c55a7a3883065328e0"
- IBMQ.save_account(TOKEN)
- IBMQ.load_account()
- IBMQ.providers()
-
- unittest.main()
+ # backend = T2HahnBackend(properties_backend)
+ # parallel_data = par_exp.run(backend=backend, shots=1000).block_for_results()
+ # # parallel_data.block_for_results()
+ #
+ # for idx, sub_res in enumerate(parallel_data.analysis_results()):
+ # self.assertAlmostEqual(
+ # sub_res[0].value.value,
+ # t2hahn[idx],
+ # delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
+ # )
+ # # self.assertAlmostEqual(
+ # # sub_res[1].value.value,
+ # # estimated_freq[i],
+ # # delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
+ # # )
+ # for res in sub_res:
+ # self.assertEqual(
+ # res.quality,
+ # "good",
+ # "Result quality bad for experiment on qubit " + str(idx),
+ # )
+
+ def test_t2hahn_concat_2_experiments(self):
+ """
+ Concatenate the data from 2 separate experiments
+ """
+ unit = "s"
+ estimated_t2hahn = 30
+ # First experiment
+ qubit = 0
+ delays0 = list(range(1, 60, 2))
+
+ exp0 = T2Hahn(qubit, delays0, unit=unit)
+ default_p0 = {
+ "A": 0.5,
+ "T2": estimated_t2hahn,
+ "phi": 0,
+ "B": 0.5,
+ }
+ exp0.set_analysis_options(user_p0=default_p0)
+ backend = T2HahnBackend(
+ p0={
+ "A": [0.5],
+ "T2": [estimated_t2hahn],
+ "phi": [0.0],
+ "B": [0.5],
+ },
+ initial_prob_plus=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ conversion_factor=1,
+ )
+
+ # run circuits
+ expdata0 = exp0.run(backend=backend, shots=1000)
+ expdata0.block_for_results()
+ results0 = expdata0.analysis_results()
+
+ # second experiment
+ delays1 = list(range(2, 65, 2))
+ exp1 = T2Hahn(qubit, delays1, unit=unit)
+ exp1.set_analysis_options(user_p0=default_p0)
+ expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
+ expdata1.block_for_results()
+ results1 = expdata1.analysis_results()
+
+ self.assertAlmostEqual(
+ results1[0].value.value,
+ estimated_t2hahn,
+ delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ )
+ self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
+ self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
From 57bc0014406e4a56f970b4d4ec548acf5cfc0f9c Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 8 Nov 2021 17:36:20 +0200
Subject: [PATCH 34/93] Applied black
---
.../library/characterization/t2hahn.py | 2 -
.../characterization/t2hahn_analysis.py | 4 +-
qiskit_experiments/test/t2hahn_backend.py | 14 +++---
test/test_t2hahn.py | 46 +------------------
4 files changed, 10 insertions(+), 56 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 1eab45729f..2775a28f91 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -25,8 +25,6 @@
from .t2hahn_analysis import T2HahnAnalysis
-
-
class T2Hahn(BaseExperiment):
r"""T2 Hahn Echo Experiment.
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 316516ad8a..f8be91e3f1 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -41,7 +41,7 @@ class T2HahnAnalysis(BaseAnalysis):
.. math::
- f(t) = a\mathrm{e}^{-t / T_2} + b
+ f(t) = a\mathrm{e}^{-2*t / T_2} + b
# section: fit_parameters
@@ -208,7 +208,7 @@ def _t2hahn_default_params(
b_bounds = [-0.5, 1.5]
bounds = (
[a_bounds[0], t2hahn_bounds[0], b_bounds[0]],
- [a_bounds[1], t2hahn_bounds[1], b_bounds[1]]
+ [a_bounds[1], t2hahn_bounds[1], b_bounds[1]],
)
else:
bounds = user_bounds
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index dd47885fb2..73ae0c4dee 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -32,12 +32,12 @@ class T2HahnBackend(BackendV1):
"""
def __init__(
- self,
- p0=None,
- initial_prob_plus=None,
- readout0to1=None,
- readout1to0=None,
- conversion_factor=1,
+ self,
+ p0=None,
+ initial_prob_plus=None,
+ readout0to1=None,
+ readout1to0=None,
+ conversion_factor=1,
):
"""
Initialize the T2Hahn backend
@@ -137,7 +137,7 @@ def run(self, run_input, **options):
if op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
if qubit_state["XY plain"]:
- meas_res = (self._rng.random() < 0.5)
+ meas_res = self._rng.random() < 0.5
else:
meas_res = qubit_state["qubit state"]
clbit = clbit_indices[cargs[0]]
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 24f16aacf6..f0cff9d35f 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -24,6 +24,7 @@
# Imports for computer
from qiskit import IBMQ
+
class TestT2Hahn(QiskitTestCase):
"""Test T2Hahn experiment"""
@@ -82,51 +83,6 @@ def test_t2hahn_run_end2end(self):
for res in result:
self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
- # def test_t2hahn_parallel(self):
- # """
- # Test parallel experiments of T2Hahn using a simulator.
- # """
- # t2hahn = [30, 25]
- # estimated_freq = [0.1, 0.12]
- # delays = [list(range(1, 60)), list(range(1, 50))]
- #
- # osc_freq = [0.11, 0.11]
- #
- # exp0 = T2Hahn(0, delays[0])
- # exp2 = T2Hahn(2, delays[1])
- # par_exp = ParallelExperiment([exp0, exp2])
- #
- # p0 = {
- # "A": [0.5, None, 0.5],
- # "T2": [t2hahn[0], None, t2hahn[1]],
- # "f": [estimated_freq[0], None, estimated_freq[1]],
- # "phi": [0, None, 0],
- # "B": [0.5, None, 0.5],
- # }
- # properties_backend = [p0, p0]
- #
- # backend = T2HahnBackend(properties_backend)
- # parallel_data = par_exp.run(backend=backend, shots=1000).block_for_results()
- # # parallel_data.block_for_results()
- #
- # for idx, sub_res in enumerate(parallel_data.analysis_results()):
- # self.assertAlmostEqual(
- # sub_res[0].value.value,
- # t2hahn[idx],
- # delta=TestT2Hahn.__tolerance__ * sub_res[0].value.value,
- # )
- # # self.assertAlmostEqual(
- # # sub_res[1].value.value,
- # # estimated_freq[i],
- # # delta=TestT2Hahn.__tolerance__ * sub_res[1].value.value,
- # # )
- # for res in sub_res:
- # self.assertEqual(
- # res.quality,
- # "good",
- # "Result quality bad for experiment on qubit " + str(idx),
- # )
-
def test_t2hahn_concat_2_experiments(self):
"""
Concatenate the data from 2 separate experiments
From a91ed7d89d9bfbffe0954130a8d30741c791f53c Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 10 Nov 2021 10:38:07 +0200
Subject: [PATCH 35/93] Update t2hahn_backend.py
---
qiskit_experiments/test/t2hahn_backend.py | 22 ++++++++++++++++++----
1 file changed, 18 insertions(+), 4 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 73ae0c4dee..71bf519074 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -68,6 +68,7 @@ def __init__(
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
self._rng = np.random.default_rng(seed=SEED)
+ self._measurement_error = 0.05
super().__init__(configuration)
@classmethod
@@ -75,6 +76,21 @@ def _default_options(cls):
"""Default options of the test backend."""
return Options(shots=1024)
+ def _measurement_gate(self, qubit_state):
+
+ if qubit_state["XY plain"]:
+ meas_res = self._rng.random() < 0.5
+ else:
+ meas_res = qubit_state["qubit state"]
+
+ # Measurement error implementation
+ if self._rng.random() < self._measurement_error:
+ if meas_res:
+ meas_res = 0
+ else:
+ meas_res = 1
+ return meas_res
+
# pylint: disable = arguments-differ
def run(self, run_input, **options):
"""
@@ -136,12 +152,10 @@ def run(self, run_input, **options):
if op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
- if qubit_state["XY plain"]:
- meas_res = self._rng.random() < 0.5
- else:
- meas_res = qubit_state["qubit state"]
+ meas_res = self._measurement_gate(qubit_state)
clbit = clbit_indices[cargs[0]]
clbits[clbit] = meas_res
+
clstr = ""
for clbit in clbits[::-1]:
clstr = clstr + str(clbit)
From 3039f1b445eb18d36b83bab676d3aa5799d0bbbe Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 10 Nov 2021 10:49:01 +0200
Subject: [PATCH 36/93] Update t2hahn_backend.py
---
qiskit_experiments/test/t2hahn_backend.py | 16 ++++++++++++----
1 file changed, 12 insertions(+), 4 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 71bf519074..5550079300 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -77,17 +77,25 @@ def _default_options(cls):
return Options(shots=1024)
def _measurement_gate(self, qubit_state):
-
+ """
+ implementing measurement on qubit with read-out error.
+ Args:
+ qubit_state(dict): The state of the qubit at the end of the circuit.
+
+ Returns:
+ int: The result of the measurement after applying read-out error.
+ """
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
else:
meas_res = qubit_state["qubit state"]
# Measurement error implementation
- if self._rng.random() < self._measurement_error:
- if meas_res:
+ if meas_res:
+ if self._readout1to0 is not None and self._rng.random() < self._readout1to0:
meas_res = 0
- else:
+ else:
+ if self._readout0to1 is not None and self._rng.random() < self._readout0to1:
meas_res = 1
return meas_res
From 75cbbaed596e60f7c6378b86b57e4f1b968b55ba Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 10 Nov 2021 17:18:47 +0200
Subject: [PATCH 37/93] Changed the gate op to be a function and added time
evolution of the state
---
qiskit_experiments/test/t2hahn_backend.py | 92 +++++++++++++++--------
test/test_t2hahn.py | 4 +-
2 files changed, 64 insertions(+), 32 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 5550079300..22eea8839a 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -61,7 +61,7 @@ def __init__(
self._t2hahn = p0["T2"]
self._a_param = p0["A"]
- self._phi = p0["phi"]
+ self._frequency = p0["frequency"]
self._b_param = p0["B"]
self._initial_prob_plus = None
self._readout0to1 = readout0to1
@@ -76,6 +76,59 @@ def _default_options(cls):
"""Default options of the test backend."""
return Options(shots=1024)
+ def _delay_gate(self, qubit_state, delay, t2hahn):
+ if qubit_state["XY plain"]:
+ prob_noise = 1 - (np.exp(-delay / t2hahn))
+ if self._rng.random() < prob_noise:
+ if self._rng.random() < 0.5:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ phase = self._frequency * delay
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": phase}
+ else:
+ new_qubit_state = qubit_state
+ return new_qubit_state
+
+
+ def _rx_gate(self, qubit_state):
+ if qubit_state["XY plain"]:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ elif qubit_state["qubit state"] == 0:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ return new_qubit_state
+
+ def _ry_gate(self, qubit_state):
+ if qubit_state["XY plain"]:
+ if qubit_state["Theta"] == 0:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ elif qubit_state["Theta"] == np.pi:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": True, "Theta": qubit_state["Theta"]}
+ elif qubit_state["YZ plain"]:
+ new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
+ "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ elif qubit_state["qubit state"] == 0:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": np.pi}
+ return new_qubit_state
+
def _measurement_gate(self, qubit_state):
"""
implementing measurement on qubit with read-out error.
@@ -121,44 +174,23 @@ def run(self, run_input, **options):
counts = dict()
for _ in range(shots):
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
- delayCheck = True
+ qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
# The noise will only be applied if we are in the XY plain.
- if op.name == "delay" and delayCheck:
+ if op.name == "delay":
delay = op.params[0]
t2hahn = self._t2hahn[qubit] * self._conversion_factor
- if qubit_state["XY plain"]:
- prob_noise = 1 - (np.exp(-delay / t2hahn))
- if self._rng.random() < prob_noise:
- if self._rng.random() < 0.5:
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
- else:
- qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
+ qubit_state = self._delay_gate(qubit_state, delay, t2hahn)
if op.name == "rx":
- if qubit_state["XY plain"]:
- qubit_state = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
- elif qubit_state["qubit state"] == 0:
- qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
- else:
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
-
- if op.name == "ry":
- if qubit_state["XY plain"]:
- if qubit_state["Theta"] == 0:
- qubit_state = {"qubit state": 1, "XY plain": False, "Theta": 0}
- else:
- qubit_state = {"qubit state": 0, "XY plain": False, "Theta": 0}
- elif qubit_state["qubit state"] == 0:
- qubit_state = {"qubit state": 1, "XY plain": True, "Theta": 0}
- else:
- qubit_state = {"qubit state": 0, "XY plain": True, "Theta": np.pi}
-
- if op.name == "measure":
+ qubit_state = self._rx_gate(qubit_state)
+ elif op.name == "ry":
+ qubit_state = self._ry_gate(qubit_state)
+ elif op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
meas_res = self._measurement_gate(qubit_state)
clbit = clbit_indices[cargs[0]]
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index f0cff9d35f..131bd1632f 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -54,7 +54,7 @@ def test_t2hahn_run_end2end(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "phi": 0,
+ "_frequency": 1,
"B": 0.5,
}
for user_p0 in [default_p0, None]:
@@ -97,7 +97,7 @@ def test_t2hahn_concat_2_experiments(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "phi": 0,
+ "frequency": 1,
"B": 0.5,
}
exp0.set_analysis_options(user_p0=default_p0)
From ea0068d656d28e311dca196ac5fa634d1fb7bfba Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 10 Nov 2021 18:00:23 +0200
Subject: [PATCH 38/93] Added output types
---
qiskit_experiments/test/t2hahn_backend.py | 9 ++++-----
1 file changed, 4 insertions(+), 5 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 22eea8839a..0b67437cca 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -76,7 +76,7 @@ def _default_options(cls):
"""Default options of the test backend."""
return Options(shots=1024)
- def _delay_gate(self, qubit_state, delay, t2hahn):
+ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
if qubit_state["XY plain"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
@@ -94,8 +94,7 @@ def _delay_gate(self, qubit_state, delay, t2hahn):
new_qubit_state = qubit_state
return new_qubit_state
-
- def _rx_gate(self, qubit_state):
+ def _rx_gate(self, qubit_state: dict) -> dict:
if qubit_state["XY plain"]:
new_qubit_state = {"qubit state": 0, "XY plain": False,
"YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
@@ -107,7 +106,7 @@ def _rx_gate(self, qubit_state):
"YZ plain": False, "Theta": 0}
return new_qubit_state
- def _ry_gate(self, qubit_state):
+ def _ry_gate(self, qubit_state: dict) -> dict:
if qubit_state["XY plain"]:
if qubit_state["Theta"] == 0:
new_qubit_state = {"qubit state": 1, "XY plain": False,
@@ -129,7 +128,7 @@ def _ry_gate(self, qubit_state):
"YZ plain": False, "Theta": np.pi}
return new_qubit_state
- def _measurement_gate(self, qubit_state):
+ def _measurement_gate(self, qubit_state: dict) -> int:
"""
implementing measurement on qubit with read-out error.
Args:
From 96da63ceab276928734a34ce15f3cec6d3e7a65c Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 14 Nov 2021 13:36:47 +0200
Subject: [PATCH 39/93] Added measurement on "ZY" plane in Z basis
---
qiskit_experiments/test/t2hahn_backend.py | 7 +++++++
1 file changed, 7 insertions(+)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 0b67437cca..18dccba896 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -139,6 +139,13 @@ def _measurement_gate(self, qubit_state: dict) -> int:
"""
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
+ elif qubit_state["YZ plain"]:
+ z_projection = np.cos(qubit_state["Theta"])
+ probability = abs(z_projection) ** 2
+ if self._rng.random() > probability:
+ meas_res = self._rng.random() < 0.5
+ else:
+ meas_res = (z_projection < 0)
else:
meas_res = qubit_state["qubit state"]
From 29683f980e18e1cead65db04c7ebfc8503e8a2cb Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 15 Nov 2021 14:09:18 +0200
Subject: [PATCH 40/93] Added angle parameter for rotation and fixed bugs
Changed last Ry gate angle to pi/2 (instead of -pi/2 because the number of echoes we do is odd)
Added angle to the rotations gate for precision.
added initialization error
---
.../library/characterization/t2hahn.py | 4 +-
qiskit_experiments/test/t2hahn_backend.py | 107 +++++++++++-------
test/test_t2hahn.py | 14 ++-
3 files changed, 79 insertions(+), 46 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 2775a28f91..e68f1be8e8 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -41,7 +41,7 @@ class T2Hahn(BaseExperiment):
.. parsed-literal::
┌─────────┐┌──────────┐┌───────┐┌──────────┐┌──────────┐┌─┐
- q_0: ┤ RY(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RY(-π/2) ├┤M├
+ q_0: ┤ RY(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RY(π/2) ├┤M├
└─────────┘└──────────┘└───────┘└──────────┘└──────────┘└╥┘
c: 1/═════════════════════════════════════════════════════════╩═
0
@@ -138,7 +138,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
circ.delay(delay, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay, 0, self.experiment_options.unit)
- circ.ry(-np.pi / 2, 0) # Y90
+ circ.ry(np.pi / 2, 0) # Y90
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 18dccba896..8c901da133 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -15,7 +15,7 @@
"""
import numpy as np
-
+from numpy import isclose
from qiskit.providers import BackendV1
from qiskit.providers.models import QasmBackendConfiguration
from qiskit.result import Result
@@ -34,7 +34,7 @@ class T2HahnBackend(BackendV1):
def __init__(
self,
p0=None,
- initial_prob_plus=None,
+ initialization_error=None,
readout0to1=None,
readout1to0=None,
conversion_factor=1,
@@ -63,7 +63,10 @@ def __init__(
self._a_param = p0["A"]
self._frequency = p0["frequency"]
self._b_param = p0["B"]
- self._initial_prob_plus = None
+ if initialization_error is not None:
+ self._initialization_error = initialization_error
+ else:
+ self._initialization_error = None
self._readout0to1 = readout0to1
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
@@ -76,6 +79,13 @@ def _default_options(cls):
"""Default options of the test backend."""
return Options(shots=1024)
+ def _qubit_initialization(self) -> dict:
+ if self._initialization_error is None:
+ return {"qubit state": 0, "XY plain": False, "YZ plain": False, "Theta": 0}
+ else:
+ return {"qubit state": (self._rng.random() < self._initialization_error[0]),
+ "XY plain": False, "YZ plain": False, "Theta": 0}
+
def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
if qubit_state["XY plain"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
@@ -87,45 +97,65 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
new_qubit_state = {"qubit state": 1, "XY plain": False,
"YZ plain": False, "Theta": 0}
else:
- phase = self._frequency * delay
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": phase}
+ phase = self._frequency[0] * delay
+ new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
+ "YZ plain": False, "Theta": qubit_state["Theta"] + phase}
else:
new_qubit_state = qubit_state
return new_qubit_state
- def _rx_gate(self, qubit_state: dict) -> dict:
+ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
if qubit_state["XY plain"]:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ if isclose(angle, np.pi):
+ new_qubit_state = {"qubit state": 0, "XY plain": True,
+ "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
elif qubit_state["qubit state"] == 0:
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
- else:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
- return new_qubit_state
-
- def _ry_gate(self, qubit_state: dict) -> dict:
- if qubit_state["XY plain"]:
- if qubit_state["Theta"] == 0:
+ if isclose(angle, np.pi):
new_qubit_state = {"qubit state": 1, "XY plain": False,
"YZ plain": False, "Theta": 0}
- elif qubit_state["Theta"] == np.pi:
+ else:
+ if isclose(angle, np.pi):
new_qubit_state = {"qubit state": 0, "XY plain": False,
"YZ plain": False, "Theta": 0}
- else:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": True, "Theta": qubit_state["Theta"]}
+ return new_qubit_state
+
+ def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
+ if qubit_state["XY plain"]:
+ if isclose(angle, np.pi/2):
+ if qubit_state["Theta"] == 0:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ elif qubit_state["Theta"] == np.pi:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": True, "Theta": np.pi - qubit_state["Theta"]}
+ elif isclose(angle, -np.pi/2):
+ if qubit_state["Theta"] == 0:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ elif qubit_state["Theta"] == np.pi:
+ new_qubit_state = {"qubit state": 1, "XY plain": False,
+ "YZ plain": False, "Theta": 0}
+ else:
+ new_qubit_state = {"qubit state": 0, "XY plain": False,
+ "YZ plain": True, "Theta": qubit_state["Theta"]}
elif qubit_state["YZ plain"]:
- new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
- "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ if isclose(angle, np.pi / 2):
+ new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
+ "YZ plain": False, "Theta": qubit_state["Theta"]}
+ elif isclose(angle, -np.pi / 2):
+ new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
+ "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
elif qubit_state["qubit state"] == 0:
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
+ new_qubit_state = {"qubit state": 0, "XY plain": True,
+ "YZ plain": False, "Theta": np.abs((np.pi/2 - angle))}
else:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": np.pi}
+ if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
+ new_qubit_state = {"qubit state": 0, "XY plain": True,
+ "YZ plain": False, "Theta": np.pi/2 + angle}
return new_qubit_state
def _measurement_gate(self, qubit_state: dict) -> int:
@@ -150,12 +180,13 @@ def _measurement_gate(self, qubit_state: dict) -> int:
meas_res = qubit_state["qubit state"]
# Measurement error implementation
- if meas_res:
- if self._readout1to0 is not None and self._rng.random() < self._readout1to0:
+ if meas_res and self._readout1to0 is not None:
+ if self._rng.random() < self._readout1to0[0]:
meas_res = 0
- else:
- if self._readout0to1 is not None and self._rng.random() < self._readout0to1:
+ elif self._readout0to1 is not None:
+ if self._rng.random() < self._readout0to1[0]:
meas_res = 1
+
return meas_res
# pylint: disable = arguments-differ
@@ -180,8 +211,7 @@ def run(self, run_input, **options):
counts = dict()
for _ in range(shots):
- qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ qubit_state = self._qubit_initialization()
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
@@ -191,11 +221,10 @@ def run(self, run_input, **options):
delay = op.params[0]
t2hahn = self._t2hahn[qubit] * self._conversion_factor
qubit_state = self._delay_gate(qubit_state, delay, t2hahn)
-
- if op.name == "rx":
- qubit_state = self._rx_gate(qubit_state)
+ elif op.name == "rx":
+ qubit_state = self._rx_gate(qubit_state, op.params[0])
elif op.name == "ry":
- qubit_state = self._ry_gate(qubit_state)
+ qubit_state = self._ry_gate(qubit_state, op.params[0])
elif op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
meas_res = self._measurement_gate(qubit_state)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 131bd1632f..8aa2e57b77 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -20,6 +20,7 @@
from qiskit_experiments.framework import ParallelExperiment
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+import unittest
# Imports for computer
from qiskit import IBMQ
@@ -54,7 +55,7 @@ def test_t2hahn_run_end2end(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "_frequency": 1,
+ "frequency": 1,
"B": 0.5,
}
for user_p0 in [default_p0, None]:
@@ -63,10 +64,10 @@ def test_t2hahn_run_end2end(self):
p0={
"A": [0.5],
"T2": [estimated_t2hahn],
- "phi": [0.0],
+ "frequency": [1],
"B": [0.5],
},
- initial_prob_plus=[0.0],
+ initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
conversion_factor=dt_factor,
@@ -105,10 +106,10 @@ def test_t2hahn_concat_2_experiments(self):
p0={
"A": [0.5],
"T2": [estimated_t2hahn],
- "phi": [0.0],
+ "frequency": [1],
"B": [0.5],
},
- initial_prob_plus=[0.0],
+ initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
conversion_factor=1,
@@ -134,3 +135,6 @@ def test_t2hahn_concat_2_experiments(self):
)
self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
From 5e14b8a929fbc3d02e921da40bf1fdf34a23e50a Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 15 Nov 2021 14:16:06 +0200
Subject: [PATCH 41/93] changed "==" to "np.isclose()"
---
qiskit_experiments/test/t2hahn_backend.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 8c901da133..359a66f8ec 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -109,7 +109,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
if isclose(angle, np.pi):
new_qubit_state = {"qubit state": 0, "XY plain": True,
"YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
- elif qubit_state["qubit state"] == 0:
+ elif isclose(qubit_state["qubit state"], 0):
if isclose(angle, np.pi):
new_qubit_state = {"qubit state": 1, "XY plain": False,
"YZ plain": False, "Theta": 0}
@@ -122,20 +122,20 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
if qubit_state["XY plain"]:
if isclose(angle, np.pi/2):
- if qubit_state["Theta"] == 0:
+ if isclose(qubit_state["Theta"], 0):
new_qubit_state = {"qubit state": 1, "XY plain": False,
"YZ plain": False, "Theta": 0}
- elif qubit_state["Theta"] == np.pi:
+ elif isclose(qubit_state["Theta"], np.pi):
new_qubit_state = {"qubit state": 0, "XY plain": False,
"YZ plain": False, "Theta": 0}
else:
new_qubit_state = {"qubit state": 0, "XY plain": False,
"YZ plain": True, "Theta": np.pi - qubit_state["Theta"]}
elif isclose(angle, -np.pi/2):
- if qubit_state["Theta"] == 0:
+ if isclose(qubit_state["Theta"], 0):
new_qubit_state = {"qubit state": 0, "XY plain": False,
"YZ plain": False, "Theta": 0}
- elif qubit_state["Theta"] == np.pi:
+ elif isclose(qubit_state["Theta"] , np.pi):
new_qubit_state = {"qubit state": 1, "XY plain": False,
"YZ plain": False, "Theta": 0}
else:
@@ -148,7 +148,7 @@ def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
elif isclose(angle, -np.pi / 2):
new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
"YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
- elif qubit_state["qubit state"] == 0:
+ elif isclose(qubit_state["qubit state"], 0):
if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
new_qubit_state = {"qubit state": 0, "XY plain": True,
"YZ plain": False, "Theta": np.abs((np.pi/2 - angle))}
From 8afaade0d61db9d8b2c37ab3dbc7a95390713052 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 15 Nov 2021 14:41:49 +0200
Subject: [PATCH 42/93] Pass pylint
---
.../library/characterization/t2hahn.py | 9 +++------
qiskit_experiments/test/t2hahn_backend.py | 3 +--
test/test_t2hahn.py | 14 ++++----------
3 files changed, 8 insertions(+), 18 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index e68f1be8e8..a5b0b0b1ad 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -18,7 +18,6 @@
import numpy as np
from qiskit import QuantumCircuit, QiskitError
-from qiskit.utils import apply_prefix
from qiskit.providers.options import Options
from qiskit.providers import Backend
from qiskit_experiments.framework import BaseExperiment
@@ -69,6 +68,7 @@ def _default_experiment_options(cls) -> Options:
options.delays = None
options.unit = "s"
+
return options
def __init__(
@@ -120,15 +120,12 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
Raises:
AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
- conversion_factor = 1
if self.experiment_options.unit == "dt":
try:
dt_factor = getattr(backend._configuration, "dt")
- conversion_factor = dt_factor
except AttributeError as no_dt:
raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
- elif self.experiment_options.unit != "s":
- conversion_factor = apply_prefix(1, self.experiment_options.unit)
+
circuits = []
for delay in self.experiment_options.delays:
@@ -138,7 +135,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
circ.delay(delay, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay, 0, self.experiment_options.unit)
- circ.ry(np.pi / 2, 0) # Y90
+ circ.ry(np.pi / 2, 0) # Y90 again since the num of echoes is odd
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 359a66f8ec..1b64803a47 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -157,7 +157,7 @@ def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
new_qubit_state = {"qubit state": 0, "XY plain": True,
"YZ plain": False, "Theta": np.pi/2 + angle}
return new_qubit_state
-
+
def _measurement_gate(self, qubit_state: dict) -> int:
"""
implementing measurement on qubit with read-out error.
@@ -205,7 +205,6 @@ def run(self, run_input, **options):
"results": [],
}
for circ in run_input:
- nqubits = circ.num_qubits
qubit_indices = {bit: idx for idx, bit in enumerate(circ.qubits)}
clbit_indices = {bit: idx for idx, bit in enumerate(circ.clbits)}
counts = dict()
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 8aa2e57b77..88cb0de54f 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -17,13 +17,9 @@
from qiskit.utils import apply_prefix
from qiskit.test import QiskitTestCase
-from qiskit_experiments.framework import ParallelExperiment
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-import unittest
-# Imports for computer
-from qiskit import IBMQ
class TestT2Hahn(QiskitTestCase):
@@ -55,7 +51,7 @@ def test_t2hahn_run_end2end(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "frequency": 1,
+ "frequency": osc_freq,
"B": 0.5,
}
for user_p0 in [default_p0, None]:
@@ -64,7 +60,7 @@ def test_t2hahn_run_end2end(self):
p0={
"A": [0.5],
"T2": [estimated_t2hahn],
- "frequency": [1],
+ "frequency": [osc_freq],
"B": [0.5],
},
initialization_error=[0.0],
@@ -93,12 +89,13 @@ def test_t2hahn_concat_2_experiments(self):
# First experiment
qubit = 0
delays0 = list(range(1, 60, 2))
+ osc_freq = 0.08
exp0 = T2Hahn(qubit, delays0, unit=unit)
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "frequency": 1,
+ "frequency": osc_freq,
"B": 0.5,
}
exp0.set_analysis_options(user_p0=default_p0)
@@ -135,6 +132,3 @@ def test_t2hahn_concat_2_experiments(self):
)
self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
From 3cfff227d0be11d3eaa0c5edb0e838be54b05c2a Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 15 Nov 2021 14:53:51 +0200
Subject: [PATCH 43/93] Pylint + black
---
.../library/characterization/t2hahn.py | 2 -
qiskit_experiments/test/t2hahn_backend.py | 144 +++++++++++++-----
test/test_t2hahn.py | 1 -
3 files changed, 106 insertions(+), 41 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index a5b0b0b1ad..d3a34b833d 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -68,7 +68,6 @@ def _default_experiment_options(cls) -> Options:
options.delays = None
options.unit = "s"
-
return options
def __init__(
@@ -126,7 +125,6 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
except AttributeError as no_dt:
raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
-
circuits = []
for delay in self.experiment_options.delays:
circ = QuantumCircuit(1, 1)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 1b64803a47..84b125eec6 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -83,23 +83,39 @@ def _qubit_initialization(self) -> dict:
if self._initialization_error is None:
return {"qubit state": 0, "XY plain": False, "YZ plain": False, "Theta": 0}
else:
- return {"qubit state": (self._rng.random() < self._initialization_error[0]),
- "XY plain": False, "YZ plain": False, "Theta": 0}
+ return {
+ "qubit state": (self._rng.random() < self._initialization_error[0]),
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
if qubit_state["XY plain"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
if self._rng.random() < 0.5:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
else:
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 1,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
else:
phase = self._frequency[0] * delay
- new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
- "YZ plain": False, "Theta": qubit_state["Theta"] + phase}
+ new_qubit_state = {
+ "qubit state": qubit_state["qubit state"],
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": qubit_state["Theta"] + phase,
+ }
else:
new_qubit_state = qubit_state
return new_qubit_state
@@ -107,55 +123,107 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
if qubit_state["XY plain"]:
if isclose(angle, np.pi):
- new_qubit_state = {"qubit state": 0, "XY plain": True,
- "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": np.pi - qubit_state["Theta"],
+ }
elif isclose(qubit_state["qubit state"], 0):
if isclose(angle, np.pi):
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 1,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
else:
if isclose(angle, np.pi):
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
return new_qubit_state
def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
if qubit_state["XY plain"]:
- if isclose(angle, np.pi/2):
+ if isclose(angle, np.pi / 2):
if isclose(qubit_state["Theta"], 0):
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 1,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
elif isclose(qubit_state["Theta"], np.pi):
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
else:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": True, "Theta": np.pi - qubit_state["Theta"]}
- elif isclose(angle, -np.pi/2):
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": True,
+ "Theta": np.pi - qubit_state["Theta"],
+ }
+ elif isclose(angle, -np.pi / 2):
if isclose(qubit_state["Theta"], 0):
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": False, "Theta": 0}
- elif isclose(qubit_state["Theta"] , np.pi):
- new_qubit_state = {"qubit state": 1, "XY plain": False,
- "YZ plain": False, "Theta": 0}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
+ elif isclose(qubit_state["Theta"], np.pi):
+ new_qubit_state = {
+ "qubit state": 1,
+ "XY plain": False,
+ "YZ plain": False,
+ "Theta": 0,
+ }
else:
- new_qubit_state = {"qubit state": 0, "XY plain": False,
- "YZ plain": True, "Theta": qubit_state["Theta"]}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": False,
+ "YZ plain": True,
+ "Theta": qubit_state["Theta"],
+ }
elif qubit_state["YZ plain"]:
if isclose(angle, np.pi / 2):
- new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
- "YZ plain": False, "Theta": qubit_state["Theta"]}
+ new_qubit_state = {
+ "qubit state": qubit_state["qubit state"],
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": qubit_state["Theta"],
+ }
elif isclose(angle, -np.pi / 2):
- new_qubit_state = {"qubit state": qubit_state["qubit state"], "XY plain": True,
- "YZ plain": False, "Theta": np.pi - qubit_state["Theta"]}
+ new_qubit_state = {
+ "qubit state": qubit_state["qubit state"],
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": np.pi - qubit_state["Theta"],
+ }
elif isclose(qubit_state["qubit state"], 0):
if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
- new_qubit_state = {"qubit state": 0, "XY plain": True,
- "YZ plain": False, "Theta": np.abs((np.pi/2 - angle))}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": np.abs((np.pi / 2 - angle)),
+ }
else:
if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
- new_qubit_state = {"qubit state": 0, "XY plain": True,
- "YZ plain": False, "Theta": np.pi/2 + angle}
+ new_qubit_state = {
+ "qubit state": 0,
+ "XY plain": True,
+ "YZ plain": False,
+ "Theta": np.pi / 2 + angle,
+ }
return new_qubit_state
def _measurement_gate(self, qubit_state: dict) -> int:
@@ -175,7 +243,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
if self._rng.random() > probability:
meas_res = self._rng.random() < 0.5
else:
- meas_res = (z_projection < 0)
+ meas_res = z_projection < 0
else:
meas_res = qubit_state["qubit state"]
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 88cb0de54f..c90a7a52bd 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -21,7 +21,6 @@
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-
class TestT2Hahn(QiskitTestCase):
"""Test T2Hahn experiment"""
From 39ce1f4f9a31426713a9bd79472c8024e9da03e2 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 15 Nov 2021 15:15:25 +0200
Subject: [PATCH 44/93] cleaned code
---
qiskit_experiments/library/characterization/t2hahn.py | 10 ++++------
1 file changed, 4 insertions(+), 6 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index d3a34b833d..c61f958194 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -119,11 +119,6 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
Raises:
AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
- if self.experiment_options.unit == "dt":
- try:
- dt_factor = getattr(backend._configuration, "dt")
- except AttributeError as no_dt:
- raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
circuits = []
for delay in self.experiment_options.delays:
@@ -142,7 +137,10 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"unit": self.experiment_options.unit,
}
if self.experiment_options.unit == "dt":
- circ.metadata["dt_factor"] = dt_factor
+ try:
+ circ.metadata["dt_factor"] = getattr(backend._configuration, "dt")
+ except AttributeError as no_dt:
+ raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
circuits.append(circ)
return circuits
From 5564384a08f37d33029f0e8120ceb891a4024108 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 17 Nov 2021 09:22:09 +0200
Subject: [PATCH 45/93] incomplete change to backend
---
.../library/characterization/t2hahn.py | 55 +++-
.../characterization/t2hahn_analysis.py | 264 ++++--------------
test/test_t2hahn.py | 68 ++---
3 files changed, 141 insertions(+), 246 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index c61f958194..f9a04e8999 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -18,6 +18,9 @@
import numpy as np
from qiskit import QuantumCircuit, QiskitError
+from qiskit.utils import apply_prefix
+from qiskit.providers.backend import Backend
+from qiskit.test.mock import FakeBackend
from qiskit.providers.options import Options
from qiskit.providers import Backend
from qiskit_experiments.framework import BaseExperiment
@@ -39,10 +42,10 @@ class T2Hahn(BaseExperiment):
.. parsed-literal::
- ┌─────────┐┌──────────┐┌───────┐┌──────────┐┌──────────┐┌─┐
+ ┌─────────┐┌──────────┐┌───────┐┌──────────┐┌─────────┐┌─┐
q_0: ┤ RY(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RY(π/2) ├┤M├
- └─────────┘└──────────┘└───────┘└──────────┘└──────────┘└╥┘
- c: 1/═════════════════════════════════════════════════════════╩═
+ └─────────┘└──────────┘└───────┘└──────────┘└─────────┘└╥┘
+ c: 1/════════════════════════════════════════════════════════╩═
0
for each *t* from the specified delay times
and the delays are specified by the user.
@@ -67,6 +70,8 @@ def _default_experiment_options(cls) -> Options:
options.delays = None
options.unit = "s"
+ options.conversion_factor = None
+ options.osc_freq = 0.0
return options
@@ -74,6 +79,7 @@ def __init__(
self,
qubit: Union[int, Iterable[int]],
delays: Union[List[float], np.array],
+ backend: Optional[Backend] = None,
unit: str = "s",
):
"""
@@ -88,7 +94,7 @@ def __init__(
QiskitError : Error for invalid input.
"""
# Initialize base experiment
- super().__init__([qubit])
+ super().__init__([qubit], backend=backend)
# Set configurable options
self.set_experiment_options(delays=delays, unit=unit)
self._verify_parameters()
@@ -108,7 +114,33 @@ def _verify_parameters(self):
"non-negative elements."
)
- def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
+ def _set_backend(self, backend: Backend):
+ super()._set_backend(backend)
+
+ # Scheduling parameters
+ if not self._backend.configuration().simulator and not isinstance(backend, FakeBackend):
+ timing_constraints = getattr(self.transpile_options, "timing_constraints", {})
+ if "acquire_alignment" not in timing_constraints:
+ timing_constraints["acquire_alignment"] = 16
+ scheduling_method = getattr(self.transpile_options, "scheduling_method", "alap")
+ self.set_transpile_options(
+ timing_constraints=timing_constraints, scheduling_method=scheduling_method
+ )
+
+ # Set conversion factor
+ if self.experiment_options.unit == "dt":
+ try:
+ dt_factor = getattr(self.backend.configuration(), "dt")
+ conversion_factor = dt_factor
+ except AttributeError as no_dt:
+ raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
+ elif self.experiment_options.unit != "s":
+ conversion_factor = apply_prefix(1, self.experiment_options.unit)
+ else:
+ conversion_factor = 1
+ self.set_experiment_options(conversion_factor=conversion_factor)
+
+ def circuits(self) -> List[QuantumCircuit]:
"""
Args:
backend: Optional, a backend object.
@@ -120,9 +152,15 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
+ prefactor = self.experiment_options.conversion_factor
+ if prefactor is None:
+ raise ValueError("Conversion factor is not set.")
+
circuits = []
- for delay in self.experiment_options.delays:
+ for delay in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
+ delay = np.round(delay, decimals=10)
circ = QuantumCircuit(1, 1)
+
# First Y rotation in 90 degrees
circ.ry(np.pi / 2, 0) # Bring to qubits to X Axis
circ.delay(delay, 0, self.experiment_options.unit)
@@ -136,11 +174,6 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]:
"xval": delay,
"unit": self.experiment_options.unit,
}
- if self.experiment_options.unit == "dt":
- try:
- circ.metadata["dt_factor"] = getattr(backend._configuration, "dt")
- except AttributeError as no_dt:
- raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
circuits.append(circ)
return circuits
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index f8be91e3f1..2b3860276d 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -10,219 +10,79 @@
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
-T2Hahn analysis class.
+T2Ramsey Experiment class.
"""
+from typing import Union, List
-from typing import List, Optional, Tuple, Dict, Union, Any
-import dataclasses
-import numpy as np
+from qiskit_experiments.data_processing import DataProcessor, Probability
+import qiskit_experiments.curve_analysis as curve
-from qiskit.utils import apply_prefix
-from qiskit_experiments.framework import (
- BaseAnalysis,
- Options,
- ExperimentData,
- AnalysisResultData,
- FitVal,
-)
-from qiskit_experiments.curve_analysis import curve_fit, plot_curve_fit, plot_errorbar, plot_scatter
-from qiskit_experiments.curve_analysis.curve_fit import process_curve_data
-from qiskit_experiments.curve_analysis.data_processing import level2_probability
+from qiskit_experiments.framework import Options
-# pylint: disable = invalid-name
-class T2HahnAnalysis(BaseAnalysis):
- r"""
- T2 Hahn result analysis class.
- # section: fit_model
- This class is used to analyze the results of a T2 Hahn Echo experiment.
- The probability of measuring :math:`|+\rangle` state is assumed to be of the form
+class T2HahnAnalysis(curve):
+ """T2 Ramsey result analysis class.
- .. math::
-
- f(t) = a\mathrm{e}^{-2*t / T_2} + b
-
- # section: fit_parameters
-
- defpar a:
- desc: Amplitude. Height of the decay curve.
- init_guess: 0.5
- bounds: [-0.5, 1.5]
-
- defpar b:
- desc: Offset. Base line of the decay curve.
- init_guess: 0.5
- bounds: [-0.5, 1.5]
-
- defpar T_2:
- desc: Represents the rate of decay.
- init_guess: the mean of the input delays.
- bounds: [0, np.inf]
+ # section: see_also
+ qiskit_experiments.curve_analysis.standard_analysis.oscillation.DumpedOscillationAnalysis
"""
+ __series__ = [
+ curve.SeriesDef(
+ fit_func=lambda x, amp, base, tau:
+ curve.exponential_decay(x, amp=amp, lamb=tau, baseline=base),
+ ),
+ ]
@classmethod
- def _default_options(cls):
- r"""Default analysis options.
-
- Analysis Options:
- user_p0 (List[Float]): user guesses for the fit parameters
- :math:`(a, b, T_2)`.
- user_bounds (Tuple[List[float], List[float]]): Lower and upper bounds
- for the fit parameters.
- plot (bool): Create a graph if and only if True.
- """
- return Options(user_p0=None, user_bounds=None)
-
- # pylint: disable=arguments-differ, unused-argument
- def _run_analysis(
- self,
- experiment_data: ExperimentData,
- user_p0: Optional[Dict[str, float]] = None,
- user_bounds: Optional[Tuple[List[float], List[float]]] = None,
- plot: bool = False,
- ax: Optional["AxesSubplot"] = None,
- **kwargs,
- ) -> Tuple[List[AnalysisResultData], List["matplotlib.figure.Figure"]]:
- r"""Calculate T2Hahn experiment.
-
- Args:
- experiment_data (ExperimentData): the experiment data to analyze
- user_p0: contains initial values given by the user, for the
- fit parameters :math:`(a, t2hahn, b)`
- user_bounds: lower and upper bounds on the parameters in p0,
- given by the user.
- The first tuple is the lower bounds,
- The second tuple is the upper bounds.
- For both params, the order is :math:`a, t2hahn, b`.
- plot: if True, create the plot, otherwise, do not create the plot.
- ax: the plot object
- **kwargs: additional parameters for curve fit.
-
- Returns:
- The analysis result with the estimated :math:`t2hahn`
- The graph of the function.
- """
-
- def T2_fit_fun(x, a, t2hahn, c):
- """Decay cosine fit function"""
- return a * np.exp(-2 * x / t2hahn) + c
-
- def _format_plot(ax, unit, fit_result, conversion_factor):
- """Format curve fit plot"""
- # Formatting
- ax.tick_params(labelsize=14)
- ax.set_xlabel("Delay (s)", fontsize=12)
- ax.ticklabel_format(axis="x", style="sci", scilimits=(0, 0))
- ax.set_ylabel("Probability of measuring 0", fontsize=12)
- t2hahn = fit_result["popt"][1] / conversion_factor
- t2hahn_err = fit_result["popt_err"][1] / conversion_factor
- box_text = "$T_2Hahn$ = {:.2f} \u00B1 {:.2f} {}".format(t2hahn, t2hahn_err, unit)
- bbox_props = dict(boxstyle="square,pad=0.3", fc="white", ec="black", lw=1)
- ax.text(
- 0.6,
- 0.9,
- box_text,
- ha="center",
- va="center",
- size=12,
- bbox=bbox_props,
- transform=ax.transAxes,
- )
- return ax
-
- # implementation of _run_analysis
-
- data = experiment_data.data()
- circ_metadata = data[0]["metadata"]
- unit = circ_metadata["unit"]
- conversion_factor = circ_metadata.get("dt_factor", None)
- if conversion_factor is None:
- conversion_factor = 1 if unit in ("s", "dt") else apply_prefix(1, unit)
-
- xdata, ydata, sigma = process_curve_data(data, lambda datum: level2_probability(datum, "0"))
-
- t2hahn_estimate = np.mean(xdata) # Maybe need to change?
- p0, bounds = self._t2hahn_default_params(
- conversion_factor, user_p0, user_bounds, t2hahn_estimate
+ def _default_options(cls) -> Options:
+ """Default analysis options."""
+ options = super()._default_options()
+ options.data_processor = DataProcessor(
+ input_key="counts", data_actions=[Probability(outcome="0")]
)
- xdata *= conversion_factor
- fit_result = curve_fit(
- T2_fit_fun, xdata, ydata, p0=list(p0.values()), sigma=sigma, bounds=bounds
- )
- fit_result = dataclasses.asdict(fit_result)
- fit_result["circuit_unit"] = unit
- if unit == "dt":
- fit_result["dt"] = conversion_factor
- quality = self._fit_quality(
- fit_result["popt"], fit_result["popt_err"], fit_result["reduced_chisq"]
- )
- chisq = fit_result["reduced_chisq"]
-
- if plot:
- ax = plot_curve_fit(T2_fit_fun, fit_result, ax=ax)
- ax = plot_scatter(xdata, ydata, ax=ax)
- ax = plot_errorbar(xdata, ydata, sigma, ax=ax)
- _format_plot(ax, unit, fit_result, conversion_factor)
- figures = [ax.get_figure()]
- else:
- figures = None
-
- # Output unit is 'sec', regardless of the unit used in the input
- result_t2hahn = AnalysisResultData(
- "T2hahn",
- value=FitVal(fit_result["popt"][1], fit_result["popt_err"][1], "s"),
- quality=quality,
- chisq=chisq,
- extra=fit_result,
- )
-
- return [result_t2hahn], figures
-
- def _t2hahn_default_params(
- self,
- conversion_factor,
- user_p0=None,
- user_bounds=None,
- t2hahn_input=None,
- ) -> Tuple[Dict[str, Union[float, Any]], Union[List[List[Union[Union[float, int], Any]]], Any]]:
- """Default fit parameters for oscillation data.
-
- Note that :math:`T_2` unit is converted to 'sec' so the
- output will be given in 'sec'.
+ options.xlabel = "Delay"
+ options.ylabel = "P(0)"
+ options.xval_unit = "s"
+ options.result_parameters = [
+ curve.ParameterRepr("tau", "T2", "s"),
+ ]
+
+ return options
+
+ def _generate_fit_guesses(
+ self, user_opt: curve.FitOptions
+ ) -> Union[curve.FitOptions, List[curve.FitOptions]]:
+ """Apply conversion factor to tau."""
+ conversion_factor = self._experiment_options()["conversion_factor"]
+
+ if user_opt.p0["tau"] is not None:
+ user_opt.p0["tau"] *= conversion_factor
+
+ return super()._generate_fit_guesses(user_opt)
+
+ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
+ """Algorithmic criteria for whether the fit is good or bad.
+
+ A good fit has:
+ - a reduced chi-squared lower than three
+ - relative error of amp is less than 10 percent
+ - relative error of tau is less than 10 percent
+ - relative error of freq is less than 10 percent
"""
- if user_p0 is None:
- a = 0.5
- t2hahn = t2hahn_input * conversion_factor
- b = 0.5
- else:
- a = user_p0["A"]
- t2hahn = user_p0["T2"] * conversion_factor
- b = user_p0["B"]
- p0 = {"a_guess": a, "T2": t2hahn, "b_guess": b}
-
- if user_bounds is None:
- a_bounds = [-0.5, 1.5]
- t2hahn_bounds = [0, np.inf]
- b_bounds = [-0.5, 1.5]
- bounds = (
- [a_bounds[0], t2hahn_bounds[0], b_bounds[0]],
- [a_bounds[1], t2hahn_bounds[1], b_bounds[1]],
- )
- else:
- bounds = user_bounds
- return (p0, bounds)
-
- @staticmethod
- def _fit_quality(fit_out, fit_err, reduced_chisq):
- # pylint: disable = too-many-boolean-expressions
- if (
- (reduced_chisq < 3)
- and (fit_err[0] is None or fit_err[0] < 0.1 * fit_out[0])
- and (fit_err[1] is None or fit_err[1] < 0.1 * fit_out[1])
- and (fit_err[2] is None or fit_err[2] < 0.1 * fit_out[2])
- ):
+ amp = fit_data.fitval("amp")
+ tau = fit_data.fitval("tau")
+ freq = fit_data.fitval("freq")
+
+ criteria = [
+ fit_data.reduced_chisq < 3,
+ amp.stderr is None or amp.stderr < 0.1 * amp.value,
+ tau.stderr is None or tau.stderr < 0.1 * tau.value,
+ freq.stderr is None or freq.stderr < 0.1 * freq.value,
+ ]
+
+ if all(criteria):
return "good"
- else:
- return "bad"
+
+ return "bad"
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index c90a7a52bd..b7bc16cf84 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -53,31 +53,29 @@ def test_t2hahn_run_end2end(self):
"frequency": osc_freq,
"B": 0.5,
}
- for user_p0 in [default_p0, None]:
+ backend = T2HahnBackend(
+ p0={
+ "A": [0.5],
+ "T2": [estimated_t2hahn],
+ "frequency": [osc_freq],
+ "B": [0.5],
+ },
+ initialization_error=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ conversion_factor=dt_factor,
+ )
+ for user_p0 in [default_p0, dict()]:
exp.set_analysis_options(user_p0=user_p0, plot=True)
- backend = T2HahnBackend(
- p0={
- "A": [0.5],
- "T2": [estimated_t2hahn],
- "frequency": [osc_freq],
- "B": [0.5],
- },
- initialization_error=[0.0],
- readout0to1=[0.02],
- readout1to0=[0.02],
- conversion_factor=dt_factor,
+ expdata = exp.run(backend=backend, shots=1000)
+ expdata.block_for_results() # Wait for job/analysis to finish.
+ result = expdata.analysis_results("T2")
+ self.assertAlmostEqual(
+ result[0].value.value,
+ estimated_t2hahn * dt_factor,
+ delta=TestT2Hahn.__tolerance__ * result[0].value.value,
)
-
- expdata = exp.run(backend=backend, shots=1000)
- expdata.block_for_results() # Wait for job/analysis to finish.
- result = expdata.analysis_results()
- self.assertAlmostEqual(
- result[0].value.value,
- estimated_t2hahn * dt_factor,
- delta=TestT2Hahn.__tolerance__ * result[0].value.value,
- )
- for res in result:
- self.assertEqual(res.quality, "good", "Result quality bad for unit " + str(unit))
+ self.assertEqual(result.quality, "good", "Result quality bad for unit " + str(unit))
def test_t2hahn_concat_2_experiments(self):
"""
@@ -92,7 +90,7 @@ def test_t2hahn_concat_2_experiments(self):
exp0 = T2Hahn(qubit, delays0, unit=unit)
default_p0 = {
- "A": 0.5,
+ "amp": 0.5,
"T2": estimated_t2hahn,
"frequency": osc_freq,
"B": 0.5,
@@ -100,9 +98,9 @@ def test_t2hahn_concat_2_experiments(self):
exp0.set_analysis_options(user_p0=default_p0)
backend = T2HahnBackend(
p0={
- "A": [0.5],
+ "amp": [0.5],
"T2": [estimated_t2hahn],
- "frequency": [1],
+ "frequency": [osc_freq],
"B": [0.5],
},
initialization_error=[0.0],
@@ -114,20 +112,24 @@ def test_t2hahn_concat_2_experiments(self):
# run circuits
expdata0 = exp0.run(backend=backend, shots=1000)
expdata0.block_for_results()
- results0 = expdata0.analysis_results()
+
+ res_t2_0 = expdata0.analysis_results("T2")
# second experiment
delays1 = list(range(2, 65, 2))
exp1 = T2Hahn(qubit, delays1, unit=unit)
exp1.set_analysis_options(user_p0=default_p0)
- expdata1 = exp1.run(backend=backend, experiment_data=expdata0, shots=1000)
- expdata1.block_for_results()
- results1 = expdata1.analysis_results()
+ expdata1 = exp1.run(backend=backend, analysis=False, shots=1000).block_for_results()
+ expdata1.add_data(expdata0.data())
+ exp1.run_analysis(expdata1).block_for_results()
+
+ res_t2_1 = expdata1.analysis_results("T2")
self.assertAlmostEqual(
- results1[0].value.value,
+ res_t2_1[0].value.value,
estimated_t2hahn,
- delta=TestT2Hahn.__tolerance__ * results1[0].value.value,
+ delta=TestT2Hahn.__tolerance__ * res_t2_1[0].value.value,
)
- self.assertLessEqual(results1[0].value.stderr, results0[0].value.stderr)
+
+ self.assertLessEqual(res_t2_1[0].value.stderr, res_t2_0[0].value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
From 401246343f12d786bb60b59439502d5632f33968 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 17 Nov 2021 09:53:57 +0200
Subject: [PATCH 46/93] deleted unnecessary fit parameters and changed input
for backend
---
.../library/characterization/t2hahn.py | 2 --
qiskit_experiments/test/t2hahn_backend.py | 14 +++++---------
test/test_t2hahn.py | 18 ++++--------------
3 files changed, 9 insertions(+), 25 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index f9a04e8999..869c624908 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -102,8 +102,6 @@ def __init__(
def _verify_parameters(self):
"""
Verify input correctness, raise QiskitError if needed.
- Args:
- qubit: the qubit under test.
Raises:
QiskitError : Error for invalid input.
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 84b125eec6..acf9726c83 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -33,7 +33,8 @@ class T2HahnBackend(BackendV1):
def __init__(
self,
- p0=None,
+ t2hahn=None,
+ frequency=None,
initialization_error=None,
readout0to1=None,
readout1to0=None,
@@ -59,14 +60,9 @@ def __init__(
dt=conversion_factor_in_ns,
)
- self._t2hahn = p0["T2"]
- self._a_param = p0["A"]
- self._frequency = p0["frequency"]
- self._b_param = p0["B"]
- if initialization_error is not None:
- self._initialization_error = initialization_error
- else:
- self._initialization_error = None
+ self._t2hahn = t2hahn
+ self._frequency = frequency
+ self._initialization_error = initialization_error
self._readout0to1 = readout0to1
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index b7bc16cf84..81d1003553 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -50,16 +50,11 @@ def test_t2hahn_run_end2end(self):
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
- "frequency": osc_freq,
"B": 0.5,
}
backend = T2HahnBackend(
- p0={
- "A": [0.5],
- "T2": [estimated_t2hahn],
- "frequency": [osc_freq],
- "B": [0.5],
- },
+ t2hahn=[estimated_t2hahn],
+ frequency=[osc_freq],
initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
@@ -92,17 +87,12 @@ def test_t2hahn_concat_2_experiments(self):
default_p0 = {
"amp": 0.5,
"T2": estimated_t2hahn,
- "frequency": osc_freq,
"B": 0.5,
}
exp0.set_analysis_options(user_p0=default_p0)
backend = T2HahnBackend(
- p0={
- "amp": [0.5],
- "T2": [estimated_t2hahn],
- "frequency": [osc_freq],
- "B": [0.5],
- },
+ t2hahn=[estimated_t2hahn],
+ frequency=[osc_freq],
initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
From 3512a29458c146a122f649b626f0d8d922eb994f Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 21 Nov 2021 11:16:05 +0200
Subject: [PATCH 47/93] changes to use only Rx without Ry
---
.../library/characterization/t2hahn.py | 6 +-
.../characterization/t2hahn_analysis.py | 43 +++---
qiskit_experiments/test/t2hahn_backend.py | 129 ++++--------------
test/test_t2hahn.py | 4 +
4 files changed, 48 insertions(+), 134 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 869c624908..214c3508d5 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -43,7 +43,7 @@ class T2Hahn(BaseExperiment):
.. parsed-literal::
┌─────────┐┌──────────┐┌───────┐┌──────────┐┌─────────┐┌─┐
- q_0: ┤ RY(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RY(π/2) ├┤M├
+ q_0: ┤ Rx(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RX(π/2) ├┤M├
└─────────┘└──────────┘└───────┘└──────────┘└─────────┘└╥┘
c: 1/════════════════════════════════════════════════════════╩═
0
@@ -160,11 +160,11 @@ def circuits(self) -> List[QuantumCircuit]:
circ = QuantumCircuit(1, 1)
# First Y rotation in 90 degrees
- circ.ry(np.pi / 2, 0) # Bring to qubits to X Axis
+ circ.rx(np.pi / 2, 0) # Bring to qubits to X Axis
circ.delay(delay, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay, 0, self.experiment_options.unit)
- circ.ry(np.pi / 2, 0) # Y90 again since the num of echoes is odd
+ circ.rx(np.pi / 2, 0) # Y90 again since the num of echoes is odd
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/t2hahn_analysis.py
index 2b3860276d..32ffbebe1c 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/t2hahn_analysis.py
@@ -10,44 +10,31 @@
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
-T2Ramsey Experiment class.
+T2 Hahn echo Analysis class.
"""
from typing import Union, List
-from qiskit_experiments.data_processing import DataProcessor, Probability
import qiskit_experiments.curve_analysis as curve
-
from qiskit_experiments.framework import Options
-class T2HahnAnalysis(curve):
- """T2 Ramsey result analysis class.
+class T2HahnAnalysis(curve.DecayAnalysis):
+ r"""A class to analyze T1 experiments.
# section: see_also
- qiskit_experiments.curve_analysis.standard_analysis.oscillation.DumpedOscillationAnalysis
+ qiskit_experiments.curve_analysis.standard_analysis.decay.DecayAnalysis
"""
- __series__ = [
- curve.SeriesDef(
- fit_func=lambda x, amp, base, tau:
- curve.exponential_decay(x, amp=amp, lamb=tau, baseline=base),
- ),
- ]
@classmethod
def _default_options(cls) -> Options:
"""Default analysis options."""
options = super()._default_options()
- options.data_processor = DataProcessor(
- input_key="counts", data_actions=[Probability(outcome="0")]
- )
options.xlabel = "Delay"
- options.ylabel = "P(0)"
+ options.ylabel = "P(1)"
options.xval_unit = "s"
- options.result_parameters = [
- curve.ParameterRepr("tau", "T2", "s"),
- ]
+ options.result_parameters = [curve.ParameterRepr("tau", "T2", "s")]
return options
@@ -67,19 +54,23 @@ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
A good fit has:
- a reduced chi-squared lower than three
- - relative error of amp is less than 10 percent
- - relative error of tau is less than 10 percent
- - relative error of freq is less than 10 percent
+ - absolute amp is within [0.9, 1.1]
+ - base is less than 0.1
+ - amp error is less than 0.1
+ - tau error is less than its value
+ - base error is less than 0.1
"""
amp = fit_data.fitval("amp")
tau = fit_data.fitval("tau")
- freq = fit_data.fitval("freq")
+ base = fit_data.fitval("base")
criteria = [
fit_data.reduced_chisq < 3,
- amp.stderr is None or amp.stderr < 0.1 * amp.value,
- tau.stderr is None or tau.stderr < 0.1 * tau.value,
- freq.stderr is None or freq.stderr < 0.1 * freq.value,
+ abs(amp.value - 1.0) < 0.1,
+ abs(base.value) < 0.1,
+ amp.stderr is None or amp.stderr < 0.1,
+ tau.stderr is None or tau.stderr < tau.value,
+ base.stderr is None or base.stderr < 0.1,
]
if all(criteria):
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index acf9726c83..0b4689528b 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -16,6 +16,7 @@
import numpy as np
from numpy import isclose
+from qiskit import QiskitError
from qiskit.providers import BackendV1
from qiskit.providers.models import QasmBackendConfiguration
from qiskit.result import Result
@@ -76,13 +77,12 @@ def _default_options(cls):
return Options(shots=1024)
def _qubit_initialization(self) -> dict:
- if self._initialization_error is None:
- return {"qubit state": 0, "XY plain": False, "YZ plain": False, "Theta": 0}
+ if self._initialization_error is not None and (self._rng.random() < self._initialization_error[0]):
+ return {"XY plain": False, "ZX plain": True, "Theta": np.pi}
else:
return {
- "qubit state": (self._rng.random() < self._initialization_error[0]),
"XY plain": False,
- "YZ plain": False,
+ "ZX plain": True,
"Theta": 0,
}
@@ -92,27 +92,28 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
if self._rng.random() < prob_noise:
if self._rng.random() < 0.5:
new_qubit_state = {
- "qubit state": 0,
"XY plain": False,
- "YZ plain": False,
+ "ZX plain": True,
"Theta": 0,
}
else:
new_qubit_state = {
- "qubit state": 1,
"XY plain": False,
- "YZ plain": False,
- "Theta": 0,
+ "ZX plain": True,
+ "Theta": np.pi,
}
else:
phase = self._frequency[0] * delay
new_qubit_state = {
- "qubit state": qubit_state["qubit state"],
"XY plain": True,
- "YZ plain": False,
+ "ZX plain": False,
"Theta": qubit_state["Theta"] + phase,
}
else:
+ raise QiskitError(
+ f"Currently delay operator support only for the qubit is in XY plain "
+ "while in this instance it's not."
+ )
new_qubit_state = qubit_state
return new_qubit_state
@@ -120,108 +121,30 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
if qubit_state["XY plain"]:
if isclose(angle, np.pi):
new_qubit_state = {
- "qubit state": 0,
"XY plain": True,
- "YZ plain": False,
+ "ZX plain": False,
"Theta": np.pi - qubit_state["Theta"],
}
- elif isclose(qubit_state["qubit state"], 0):
- if isclose(angle, np.pi):
- new_qubit_state = {
- "qubit state": 1,
- "XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- else:
- if isclose(angle, np.pi):
+ elif isclose(angle, np.pi/2):
new_qubit_state = {
- "qubit state": 0,
"XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- return new_qubit_state
-
- def _ry_gate(self, qubit_state: dict, angle: float) -> dict:
- if qubit_state["XY plain"]:
- if isclose(angle, np.pi / 2):
- if isclose(qubit_state["Theta"], 0):
- new_qubit_state = {
- "qubit state": 1,
- "XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- elif isclose(qubit_state["Theta"], np.pi):
- new_qubit_state = {
- "qubit state": 0,
- "XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- else:
- new_qubit_state = {
- "qubit state": 0,
- "XY plain": False,
- "YZ plain": True,
- "Theta": np.pi - qubit_state["Theta"],
- }
- elif isclose(angle, -np.pi / 2):
- if isclose(qubit_state["Theta"], 0):
- new_qubit_state = {
- "qubit state": 0,
- "XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- elif isclose(qubit_state["Theta"], np.pi):
- new_qubit_state = {
- "qubit state": 1,
- "XY plain": False,
- "YZ plain": False,
- "Theta": 0,
- }
- else:
- new_qubit_state = {
- "qubit state": 0,
- "XY plain": False,
- "YZ plain": True,
- "Theta": qubit_state["Theta"],
- }
- elif qubit_state["YZ plain"]:
- if isclose(angle, np.pi / 2):
- new_qubit_state = {
- "qubit state": qubit_state["qubit state"],
- "XY plain": True,
- "YZ plain": False,
- "Theta": qubit_state["Theta"],
- }
- elif isclose(angle, -np.pi / 2):
- new_qubit_state = {
- "qubit state": qubit_state["qubit state"],
- "XY plain": True,
- "YZ plain": False,
+ "ZX plain": True,
"Theta": np.pi - qubit_state["Theta"],
}
- elif isclose(qubit_state["qubit state"], 0):
- if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
- new_qubit_state = {
- "qubit state": 0,
- "XY plain": True,
- "YZ plain": False,
- "Theta": np.abs((np.pi / 2 - angle)),
- }
else:
- if isclose(angle, np.pi / 2) or isclose(angle, -np.pi / 2):
+ if isclose(angle, np.pi/2):
+ new_theta = qubit_state["Theta"] + 3 * np.pi/2 # its theta -pi/2 but we added 2*pi
+ new_theta = new_theta % np.pi
new_qubit_state = {
- "qubit state": 0,
"XY plain": True,
- "YZ plain": False,
- "Theta": np.pi / 2 + angle,
+ "ZX plain": False,
+ "Theta": new_theta,
}
+ else:
+ new_qubit_state =
return new_qubit_state
+
def _measurement_gate(self, qubit_state: dict) -> int:
"""
implementing measurement on qubit with read-out error.
@@ -233,15 +156,13 @@ def _measurement_gate(self, qubit_state: dict) -> int:
"""
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
- elif qubit_state["YZ plain"]:
+ else:
z_projection = np.cos(qubit_state["Theta"])
probability = abs(z_projection) ** 2
if self._rng.random() > probability:
meas_res = self._rng.random() < 0.5
else:
meas_res = z_projection < 0
- else:
- meas_res = qubit_state["qubit state"]
# Measurement error implementation
if meas_res and self._readout1to0 is not None:
@@ -286,8 +207,6 @@ def run(self, run_input, **options):
qubit_state = self._delay_gate(qubit_state, delay, t2hahn)
elif op.name == "rx":
qubit_state = self._rx_gate(qubit_state, op.params[0])
- elif op.name == "ry":
- qubit_state = self._ry_gate(qubit_state, op.params[0])
elif op.name == "measure":
# we measure in |+> basis which is the same as measuring |0>
meas_res = self._measurement_gate(qubit_state)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 81d1003553..5202a36074 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -19,6 +19,7 @@
from qiskit.test import QiskitTestCase
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+import unittest
class TestT2Hahn(QiskitTestCase):
@@ -123,3 +124,6 @@ def test_t2hahn_concat_2_experiments(self):
self.assertLessEqual(res_t2_1[0].value.stderr, res_t2_0[0].value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
From cada6405abd5248703e0b4ed6caa8ce432ca989f Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 22 Nov 2021 19:28:25 +0200
Subject: [PATCH 48/93] Experiment Working Up to tau that is not correct
---
.../{ => analysis}/t2hahn_analysis.py | 16 +++---
.../library/characterization/t2hahn.py | 44 ++++++++-------
qiskit_experiments/test/t2hahn_backend.py | 53 +++++++++++++++----
test/test_t2hahn.py | 20 +++----
4 files changed, 89 insertions(+), 44 deletions(-)
rename qiskit_experiments/library/characterization/{ => analysis}/t2hahn_analysis.py (83%)
diff --git a/qiskit_experiments/library/characterization/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
similarity index 83%
rename from qiskit_experiments/library/characterization/t2hahn_analysis.py
rename to qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index 32ffbebe1c..faeb0e7b97 100644
--- a/qiskit_experiments/library/characterization/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -15,12 +15,13 @@
from typing import Union, List
import qiskit_experiments.curve_analysis as curve
+from qiskit_experiments.data_processing import DataProcessor, Probability
from qiskit_experiments.framework import Options
class T2HahnAnalysis(curve.DecayAnalysis):
- r"""A class to analyze T1 experiments.
+ r"""A class to analyze T2Hahn experiments.
# section: see_also
qiskit_experiments.curve_analysis.standard_analysis.decay.DecayAnalysis
@@ -31,8 +32,11 @@ class T2HahnAnalysis(curve.DecayAnalysis):
def _default_options(cls) -> Options:
"""Default analysis options."""
options = super()._default_options()
+ options.data_processor = DataProcessor(
+ input_key="counts", data_actions=[Probability(outcome="0")]
+ )
options.xlabel = "Delay"
- options.ylabel = "P(1)"
+ options.ylabel = "P(0)"
options.xval_unit = "s"
options.result_parameters = [curve.ParameterRepr("tau", "T2", "s")]
@@ -54,8 +58,8 @@ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
A good fit has:
- a reduced chi-squared lower than three
- - absolute amp is within [0.9, 1.1]
- - base is less than 0.1
+ - absolute amp is within [0.4, 0.6]
+ - base is less is within [0.4, 0.6]
- amp error is less than 0.1
- tau error is less than its value
- base error is less than 0.1
@@ -66,8 +70,8 @@ def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
criteria = [
fit_data.reduced_chisq < 3,
- abs(amp.value - 1.0) < 0.1,
- abs(base.value) < 0.1,
+ abs(amp.value - 0.5) < 0.1,
+ abs(base.value - 0.5) < 0.1,
amp.stderr is None or amp.stderr < 0.1,
tau.stderr is None or tau.stderr < tau.value,
base.stderr is None or base.stderr < 0.1,
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 214c3508d5..6ce3feff5f 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -14,22 +14,22 @@
"""
-from typing import Union, Iterable, List, Optional
+from typing import List, Optional, Union
import numpy as np
-from qiskit import QuantumCircuit, QiskitError
from qiskit.utils import apply_prefix
+from qiskit import QuantumCircuit, QiskitError
from qiskit.providers.backend import Backend
from qiskit.test.mock import FakeBackend
-from qiskit.providers.options import Options
-from qiskit.providers import Backend
-from qiskit_experiments.framework import BaseExperiment
-from .t2hahn_analysis import T2HahnAnalysis
+
+from qiskit_experiments.framework import BaseExperiment, Options
+from qiskit_experiments.library.characterization.analysis.t2hahn_analysis import T2HahnAnalysis
class T2Hahn(BaseExperiment):
r"""T2 Hahn Echo Experiment.
+
# section: overview
This experiment is used to estimate T2 noise of a single qubit.
@@ -40,8 +40,10 @@ class T2Hahn(BaseExperiment):
This experiment consists of a series of circuits of the form
+
.. parsed-literal::
+
┌─────────┐┌──────────┐┌───────┐┌──────────┐┌─────────┐┌─┐
q_0: ┤ Rx(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RX(π/2) ├┤M├
└─────────┘└──────────┘└───────┘└──────────┘└─────────┘└╥┘
@@ -76,17 +78,19 @@ def _default_experiment_options(cls) -> Options:
return options
def __init__(
- self,
- qubit: Union[int, Iterable[int]],
- delays: Union[List[float], np.array],
- backend: Optional[Backend] = None,
- unit: str = "s",
+ self,
+ qubit: int,
+ delays: Union[List[float], np.array],
+ backend: Optional[Backend] = None,
+ unit: str = "s",
):
"""
Initialize the T2 - Hahn Echo class
+
Args:
- qubit: the qubit under test.
+ qubit: the qubit whose T2 is to be estimated
delays: delay times of the experiments.
+ backend: Optional, the backend to run the experiment on.
unit: Optional, time unit of `delays`.
Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.
@@ -95,7 +99,8 @@ def __init__(
"""
# Initialize base experiment
super().__init__([qubit], backend=backend)
- # Set configurable options
+
+ # Set experiment options
self.set_experiment_options(delays=delays, unit=unit)
self._verify_parameters()
@@ -140,23 +145,25 @@ def _set_backend(self, backend: Backend):
def circuits(self) -> List[QuantumCircuit]:
"""
- Args:
- backend: Optional, a backend object.
+ Return a list of experiment circuits
Returns:
- The experiment circuits.
+ The experiment circuits
Raises:
AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
-
+ if self.backend:
+ self._set_backend(self.backend)
prefactor = self.experiment_options.conversion_factor
+
if prefactor is None:
raise ValueError("Conversion factor is not set.")
circuits = []
for delay in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
delay = np.round(delay, decimals=10)
+
circ = QuantumCircuit(1, 1)
# First Y rotation in 90 degrees
@@ -170,8 +177,9 @@ def circuits(self) -> List[QuantumCircuit]:
"experiment_type": self._type,
"qubit": self.physical_qubits[0],
"xval": delay,
- "unit": self.experiment_options.unit,
+ "unit": "s",
}
+
circuits.append(circ)
return circuits
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 0b4689528b..17b2ce34af 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -87,6 +87,17 @@ def _qubit_initialization(self) -> dict:
}
def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
+ """
+ Apply delay gate to the qubit. From the delay time we can calculate the probability
+ that an error has accrued.
+ Args:
+ qubit_state(dict): The state of the qubit before operating the gate.
+ delay(float): The time in which there are no operation on the qubit.
+ t2hahn(float): The T2 parameter of the backhand for probability calculation.
+
+ Returns:
+ dict: The state of the qubit after operating the gate.
+ """
if qubit_state["XY plain"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
@@ -104,44 +115,66 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
}
else:
phase = self._frequency[0] * delay
+ new_theta = qubit_state["Theta"] + phase
+ new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": True,
"ZX plain": False,
- "Theta": qubit_state["Theta"] + phase,
+ "Theta": new_theta
}
else:
- raise QiskitError(
- f"Currently delay operator support only for the qubit is in XY plain "
- "while in this instance it's not."
- )
new_qubit_state = qubit_state
+ # new_qubit_state = qubit_state
return new_qubit_state
def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
+ """
+ Apply Rx gate.
+ Args:
+ qubit_state(dict): The state of the qubit before operating the gate.
+ angle(float): The angle of the rotation.
+
+ Returns:
+ dict: The state of the qubit after operating the gate.
+ """
if qubit_state["XY plain"]:
if isclose(angle, np.pi):
+ new_theta = - qubit_state["Theta"]
+ new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": True,
"ZX plain": False,
- "Theta": np.pi - qubit_state["Theta"],
+ "Theta": new_theta,
}
elif isclose(angle, np.pi/2):
+ new_theta = angle - qubit_state["Theta"]
+ new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": False,
"ZX plain": True,
- "Theta": np.pi - qubit_state["Theta"],
+ "Theta": new_theta,
}
+ else:
+ print("Error - This angle isn't supported. We only support multipication of pi/2")
else:
if isclose(angle, np.pi/2):
new_theta = qubit_state["Theta"] + 3 * np.pi/2 # its theta -pi/2 but we added 2*pi
- new_theta = new_theta % np.pi
+ new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": True,
"ZX plain": False,
"Theta": new_theta,
}
+ elif isclose(angle, np.pi):
+ new_theta = qubit_state["Theta"] + np.pi
+ new_theta = new_theta % (2 * np.pi)
+ new_qubit_state = {
+ "XY plain": False,
+ "ZX plain": True,
+ "Theta": new_theta,
+ }
else:
- new_qubit_state =
+ print("Error - This angle isn't supported. We only support multiplication of pi/2")
return new_qubit_state
@@ -195,7 +228,7 @@ def run(self, run_input, **options):
counts = dict()
for _ in range(shots):
- qubit_state = self._qubit_initialization()
+ qubit_state = self._qubit_initialization() # for parrallel need to make an array
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 5202a36074..842966bdad 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -57,23 +57,23 @@ def test_t2hahn_run_end2end(self):
t2hahn=[estimated_t2hahn],
frequency=[osc_freq],
initialization_error=[0.0],
- readout0to1=[0.02],
- readout1to0=[0.02],
+ readout0to1=[0.0],
+ readout1to0=[0.0],
conversion_factor=dt_factor,
)
+
for user_p0 in [default_p0, dict()]:
- exp.set_analysis_options(user_p0=user_p0, plot=True)
+ # exp.set_analysis_options(user_p0=user_p0, plot=True)
+ exp.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
expdata = exp.run(backend=backend, shots=1000)
expdata.block_for_results() # Wait for job/analysis to finish.
result = expdata.analysis_results("T2")
- self.assertAlmostEqual(
- result[0].value.value,
- estimated_t2hahn * dt_factor,
- delta=TestT2Hahn.__tolerance__ * result[0].value.value,
- )
- self.assertEqual(result.quality, "good", "Result quality bad for unit " + str(unit))
+ fitval = result.value
+ self.assertEqual(result.quality, "good")
+ self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
+ self.assertEqual(fitval.unit, "s")
- def test_t2hahn_concat_2_experiments(self):
+ def _test_t2hahn_concat_2_experiments(self):
"""
Concatenate the data from 2 separate experiments
"""
From 5f989d0d28be99b4aff9030556729ea5340509c9 Mon Sep 17 00:00:00 2001
From: ItamarGoldman <51112651+ItamarGoldman@users.noreply.github.com>
Date: Wed, 24 Nov 2021 14:22:01 +0200
Subject: [PATCH 49/93] Update comment Y90 to X90
Co-authored-by: Yael Ben-Haim <yaelbh@il.ibm.com>
---
qiskit_experiments/library/characterization/t2hahn.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 6ce3feff5f..7c2f69f48d 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -171,7 +171,7 @@ def circuits(self) -> List[QuantumCircuit]:
circ.delay(delay, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay, 0, self.experiment_options.unit)
- circ.rx(np.pi / 2, 0) # Y90 again since the num of echoes is odd
+ circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
From 99fd9d57bc12234d4b6879346738da4d7c57e806 Mon Sep 17 00:00:00 2001
From: ItamarGoldman <51112651+ItamarGoldman@users.noreply.github.com>
Date: Wed, 24 Nov 2021 14:25:09 +0200
Subject: [PATCH 50/93] Deleted extra space from measurement function in the
backend
Co-authored-by: Yael Ben-Haim <yaelbh@il.ibm.com>
---
qiskit_experiments/test/t2hahn_backend.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 17b2ce34af..8250e0a679 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -185,7 +185,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
qubit_state(dict): The state of the qubit at the end of the circuit.
Returns:
- int: The result of the measurement after applying read-out error.
+ int: The result of the measurement after applying read-out error.
"""
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
From 73f734cd5dd2ee1eaf233a187acc6ae15cec906b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 24 Nov 2021 14:32:40 +0200
Subject: [PATCH 51/93] changed the backend as Yael review comments
---
qiskit_experiments/library/characterization/t2hahn.py | 2 +-
qiskit_experiments/test/t2hahn_backend.py | 6 ++----
2 files changed, 3 insertions(+), 5 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 6ce3feff5f..7c2f69f48d 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -171,7 +171,7 @@ def circuits(self) -> List[QuantumCircuit]:
circ.delay(delay, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay, 0, self.experiment_options.unit)
- circ.rx(np.pi / 2, 0) # Y90 again since the num of echoes is odd
+ circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 17b2ce34af..78f4f4efdf 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -68,7 +68,6 @@ def __init__(
self._readout1to0 = readout1to0
self._conversion_factor = conversion_factor
self._rng = np.random.default_rng(seed=SEED)
- self._measurement_error = 0.05
super().__init__(configuration)
@classmethod
@@ -185,7 +184,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
qubit_state(dict): The state of the qubit at the end of the circuit.
Returns:
- int: The result of the measurement after applying read-out error.
+ int: The result of the measurement after applying read-out error.
"""
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
@@ -201,7 +200,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
if meas_res and self._readout1to0 is not None:
if self._rng.random() < self._readout1to0[0]:
meas_res = 0
- elif self._readout0to1 is not None:
+ elif not meas_res and self._readout0to1 is not None:
if self._rng.random() < self._readout0to1[0]:
meas_res = 1
@@ -241,7 +240,6 @@ def run(self, run_input, **options):
elif op.name == "rx":
qubit_state = self._rx_gate(qubit_state, op.params[0])
elif op.name == "measure":
- # we measure in |+> basis which is the same as measuring |0>
meas_res = self._measurement_gate(qubit_state)
clbit = clbit_indices[cargs[0]]
clbits[clbit] = meas_res
From 588c6c2656dffcb9474a4719c42c75a0099b7a6b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 29 Nov 2021 16:58:20 +0200
Subject: [PATCH 52/93] Test are working
Test are working.
Need to add:
* Parallel Experiment
Could be a problem:
The meta data right now is for the cumulative delay time.
---
.../library/characterization/t2hahn.py | 29 ++++++++++------
test/test_t2hahn.py | 33 ++++++++++---------
2 files changed, 36 insertions(+), 26 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 7c2f69f48d..19fde2f482 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -51,6 +51,8 @@ class T2Hahn(BaseExperiment):
0
for each *t* from the specified delay times
and the delays are specified by the user.
+ The delays that are specified are delay for each delay gate while
+ the delay in the metadata is the total delay which is delay * (num_echoes +1)
The circuits are run on the device or on a simulator backend.
# section: tutorial
@@ -74,7 +76,7 @@ def _default_experiment_options(cls) -> Options:
options.unit = "s"
options.conversion_factor = None
options.osc_freq = 0.0
-
+ options.num_echoes = 1
return options
def __init__(
@@ -89,7 +91,7 @@ def __init__(
Args:
qubit: the qubit whose T2 is to be estimated
- delays: delay times of the experiments.
+ delays: Total delay times of the experiments.
backend: Optional, the backend to run the experiment on.
unit: Optional, time unit of `delays`.
Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.
@@ -161,22 +163,29 @@ def circuits(self) -> List[QuantumCircuit]:
raise ValueError("Conversion factor is not set.")
circuits = []
- for delay in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
- delay = np.round(delay, decimals=10)
+ for delay_gate in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
+ total_delay = delay_gate * (self.experiment_options.num_echoes + 1)
+ # delay_gate = delay
+
+ delay_gate = np.round(delay_gate, decimals=10)
circ = QuantumCircuit(1, 1)
- # First Y rotation in 90 degrees
+ # First X rotation in 90 degrees
circ.rx(np.pi / 2, 0) # Bring to qubits to X Axis
- circ.delay(delay, 0, self.experiment_options.unit)
- circ.rx(np.pi, 0)
- circ.delay(delay, 0, self.experiment_options.unit)
- circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
+ for idx in range(self.experiment_options.num_echoes):
+ circ.delay(delay_gate, 0, self.experiment_options.unit)
+ circ.rx(np.pi, 0)
+ circ.delay(delay_gate, 0, self.experiment_options.unit)
+ if self.experiment_options.num_echoes % 2 == 1:
+ circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
+ else:
+ circ.rx(-np.pi / 2, 0) # X90 again since the num of echoes is even
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
"qubit": self.physical_qubits[0],
- "xval": delay,
+ "xval": total_delay,
"unit": "s",
}
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 842966bdad..ab9a4e9765 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -31,7 +31,7 @@ def test_t2hahn_run_end2end(self):
"""
Run the T2Hahn backend on all possible units
"""
- for unit in ["s", "ms", "us", "ns", "dt"]:
+ for unit in ["s"]:
if unit in ("s", "dt"):
dt_factor = 1
else:
@@ -57,8 +57,8 @@ def test_t2hahn_run_end2end(self):
t2hahn=[estimated_t2hahn],
frequency=[osc_freq],
initialization_error=[0.0],
- readout0to1=[0.0],
- readout1to0=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
conversion_factor=dt_factor,
)
@@ -73,7 +73,7 @@ def test_t2hahn_run_end2end(self):
self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
self.assertEqual(fitval.unit, "s")
- def _test_t2hahn_concat_2_experiments(self):
+ def test_t2hahn_concat_2_experiments(self):
"""
Concatenate the data from 2 separate experiments
"""
@@ -83,14 +83,10 @@ def _test_t2hahn_concat_2_experiments(self):
qubit = 0
delays0 = list(range(1, 60, 2))
osc_freq = 0.08
+ dt_factor = 1
exp0 = T2Hahn(qubit, delays0, unit=unit)
- default_p0 = {
- "amp": 0.5,
- "T2": estimated_t2hahn,
- "B": 0.5,
- }
- exp0.set_analysis_options(user_p0=default_p0)
+ exp0.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
backend = T2HahnBackend(
t2hahn=[estimated_t2hahn],
frequency=[osc_freq],
@@ -101,28 +97,33 @@ def _test_t2hahn_concat_2_experiments(self):
)
# run circuits
- expdata0 = exp0.run(backend=backend, shots=1000)
+ expdata0 = exp0.run(backend=backend, shots=1000).block_for_results()
expdata0.block_for_results()
- res_t2_0 = expdata0.analysis_results("T2")
+ res_t2_0 = expdata0.analysis_results('T2')
# second experiment
delays1 = list(range(2, 65, 2))
exp1 = T2Hahn(qubit, delays1, unit=unit)
- exp1.set_analysis_options(user_p0=default_p0)
+ exp1.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
expdata1 = exp1.run(backend=backend, analysis=False, shots=1000).block_for_results()
expdata1.add_data(expdata0.data())
exp1.run_analysis(expdata1).block_for_results()
res_t2_1 = expdata1.analysis_results("T2")
+ fitval = res_t2_1.value
+ self.assertEqual(res_t2_1.quality, "good")
+ self.assertAlmostEqual(res_t2_1.value.value, estimated_t2hahn, delta=3)
+ self.assertEqual(fitval.unit, "s")
+
self.assertAlmostEqual(
- res_t2_1[0].value.value,
+ res_t2_1.value.value,
estimated_t2hahn,
- delta=TestT2Hahn.__tolerance__ * res_t2_1[0].value.value,
+ delta=TestT2Hahn.__tolerance__ * res_t2_1.value.value,
)
- self.assertLessEqual(res_t2_1[0].value.stderr, res_t2_0[0].value.stderr)
+ self.assertLessEqual(res_t2_1.value.stderr, res_t2_0.value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
if __name__ == '__main__':
From d85fdf2965fec723898b87edd42bf27bc3d9acd8 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 29 Nov 2021 17:04:06 +0200
Subject: [PATCH 53/93] Passed Black and Lint
---
.../library/characterization/t2hahn.py | 16 +++++++-------
test/test_t2hahn.py | 21 ++++++++++---------
2 files changed, 19 insertions(+), 18 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 19fde2f482..3d7a8d5e0f 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -80,11 +80,11 @@ def _default_experiment_options(cls) -> Options:
return options
def __init__(
- self,
- qubit: int,
- delays: Union[List[float], np.array],
- backend: Optional[Backend] = None,
- unit: str = "s",
+ self,
+ qubit: int,
+ delays: Union[List[float], np.array],
+ backend: Optional[Backend] = None,
+ unit: str = "s",
):
"""
Initialize the T2 - Hahn Echo class
@@ -92,7 +92,7 @@ def __init__(
Args:
qubit: the qubit whose T2 is to be estimated
delays: Total delay times of the experiments.
- backend: Optional, the backend to run the experiment on.
+ backend: Optional, the backend to run the experiment on.
unit: Optional, time unit of `delays`.
Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.
@@ -153,7 +153,7 @@ def circuits(self) -> List[QuantumCircuit]:
The experiment circuits
Raises:
- AttributeError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
+ ValueError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
if self.backend:
self._set_backend(self.backend)
@@ -173,7 +173,7 @@ def circuits(self) -> List[QuantumCircuit]:
# First X rotation in 90 degrees
circ.rx(np.pi / 2, 0) # Bring to qubits to X Axis
- for idx in range(self.experiment_options.num_echoes):
+ for _ in range(self.experiment_options.num_echoes):
circ.delay(delay_gate, 0, self.experiment_options.unit)
circ.rx(np.pi, 0)
circ.delay(delay_gate, 0, self.experiment_options.unit)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index ab9a4e9765..6556f7e303 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -19,7 +19,6 @@
from qiskit.test import QiskitTestCase
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-import unittest
class TestT2Hahn(QiskitTestCase):
@@ -62,9 +61,10 @@ def test_t2hahn_run_end2end(self):
conversion_factor=dt_factor,
)
- for user_p0 in [default_p0, dict()]:
- # exp.set_analysis_options(user_p0=user_p0, plot=True)
- exp.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
+ for _ in [default_p0, dict()]:
+ exp.set_analysis_options(
+ p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
+ )
expdata = exp.run(backend=backend, shots=1000)
expdata.block_for_results() # Wait for job/analysis to finish.
result = expdata.analysis_results("T2")
@@ -86,7 +86,9 @@ def test_t2hahn_concat_2_experiments(self):
dt_factor = 1
exp0 = T2Hahn(qubit, delays0, unit=unit)
- exp0.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
+ exp0.set_analysis_options(
+ p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
+ )
backend = T2HahnBackend(
t2hahn=[estimated_t2hahn],
frequency=[osc_freq],
@@ -100,12 +102,14 @@ def test_t2hahn_concat_2_experiments(self):
expdata0 = exp0.run(backend=backend, shots=1000).block_for_results()
expdata0.block_for_results()
- res_t2_0 = expdata0.analysis_results('T2')
+ res_t2_0 = expdata0.analysis_results("T2")
# second experiment
delays1 = list(range(2, 65, 2))
exp1 = T2Hahn(qubit, delays1, unit=unit)
- exp1.set_analysis_options(p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True)
+ exp1.set_analysis_options(
+ p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
+ )
expdata1 = exp1.run(backend=backend, analysis=False, shots=1000).block_for_results()
expdata1.add_data(expdata0.data())
exp1.run_analysis(expdata1).block_for_results()
@@ -125,6 +129,3 @@ def test_t2hahn_concat_2_experiments(self):
self.assertLessEqual(res_t2_1.value.stderr, res_t2_0.value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
From 6f1f04d79b491337065237f775cb58a55591bffd Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 29 Nov 2021 17:07:59 +0200
Subject: [PATCH 54/93] Update t2hahn_backend.py
---
qiskit_experiments/test/t2hahn_backend.py | 1 -
1 file changed, 1 deletion(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 78f4f4efdf..55cb7d200c 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -123,7 +123,6 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
}
else:
new_qubit_state = qubit_state
- # new_qubit_state = qubit_state
return new_qubit_state
def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
From 643f7e7b0b88f7452eb91897bd4105381e4ffba0 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 29 Nov 2021 17:10:02 +0200
Subject: [PATCH 55/93] deleted the abs as the projection is a real number
---
qiskit_experiments/test/t2hahn_backend.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 55cb7d200c..9ad65e4907 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -189,7 +189,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
meas_res = self._rng.random() < 0.5
else:
z_projection = np.cos(qubit_state["Theta"])
- probability = abs(z_projection) ** 2
+ probability = (z_projection ** 2)
if self._rng.random() > probability:
meas_res = self._rng.random() < 0.5
else:
From 62b8163527b13b55f22c13cf29a1bf824c1f63b0 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 29 Nov 2021 17:15:31 +0200
Subject: [PATCH 56/93] Black and pylint
---
qiskit_experiments/test/t2hahn_backend.py | 24 +++++++++++------------
1 file changed, 11 insertions(+), 13 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 9ad65e4907..ccb70107b3 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -16,7 +16,6 @@
import numpy as np
from numpy import isclose
-from qiskit import QiskitError
from qiskit.providers import BackendV1
from qiskit.providers.models import QasmBackendConfiguration
from qiskit.result import Result
@@ -76,7 +75,9 @@ def _default_options(cls):
return Options(shots=1024)
def _qubit_initialization(self) -> dict:
- if self._initialization_error is not None and (self._rng.random() < self._initialization_error[0]):
+ if self._initialization_error is not None and (
+ self._rng.random() < self._initialization_error[0]
+ ):
return {"XY plain": False, "ZX plain": True, "Theta": np.pi}
else:
return {
@@ -116,11 +117,7 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
phase = self._frequency[0] * delay
new_theta = qubit_state["Theta"] + phase
new_theta = new_theta % (2 * np.pi)
- new_qubit_state = {
- "XY plain": True,
- "ZX plain": False,
- "Theta": new_theta
- }
+ new_qubit_state = {"XY plain": True, "ZX plain": False, "Theta": new_theta}
else:
new_qubit_state = qubit_state
return new_qubit_state
@@ -137,14 +134,14 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"""
if qubit_state["XY plain"]:
if isclose(angle, np.pi):
- new_theta = - qubit_state["Theta"]
+ new_theta = -qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": True,
"ZX plain": False,
"Theta": new_theta,
}
- elif isclose(angle, np.pi/2):
+ elif isclose(angle, np.pi / 2):
new_theta = angle - qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
@@ -155,8 +152,10 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
else:
print("Error - This angle isn't supported. We only support multipication of pi/2")
else:
- if isclose(angle, np.pi/2):
- new_theta = qubit_state["Theta"] + 3 * np.pi/2 # its theta -pi/2 but we added 2*pi
+ if isclose(angle, np.pi / 2):
+ new_theta = (
+ qubit_state["Theta"] + 3 * np.pi / 2
+ ) # its theta -pi/2 but we added 2*pi
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plain": True,
@@ -175,7 +174,6 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
print("Error - This angle isn't supported. We only support multiplication of pi/2")
return new_qubit_state
-
def _measurement_gate(self, qubit_state: dict) -> int:
"""
implementing measurement on qubit with read-out error.
@@ -189,7 +187,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
meas_res = self._rng.random() < 0.5
else:
z_projection = np.cos(qubit_state["Theta"])
- probability = (z_projection ** 2)
+ probability = z_projection ** 2
if self._rng.random() > probability:
meas_res = self._rng.random() < 0.5
else:
From 961a9ddc372d878eb43366fba3920e1a39bc3bf2 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 5 Dec 2021 12:36:20 +0200
Subject: [PATCH 57/93] Added comments
---
qiskit_experiments/test/t2hahn_backend.py | 5 +++++
1 file changed, 5 insertions(+)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index ccb70107b3..c2f8ac129b 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -183,9 +183,14 @@ def _measurement_gate(self, qubit_state: dict) -> int:
Returns:
int: The result of the measurement after applying read-out error.
"""
+ # Here we are calculating the probability for measurement result depending on the
+ # where the qubit is on the bloch sphere.
if qubit_state["XY plain"]:
meas_res = self._rng.random() < 0.5
else:
+ # Since we are not in the XY plain, we need to calculate the probability for
+ # measuring output. First, we calculate the probability and later we are
+ # tossing to see if the event did happened.
z_projection = np.cos(qubit_state["Theta"])
probability = z_projection ** 2
if self._rng.random() > probability:
From 7bd81d163d4d3d2b7d1029fa3ca01a51525dac53 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 5 Dec 2021 22:15:32 +0200
Subject: [PATCH 58/93] Added tutorial
---
docs/tutorials/t2hahn_characterization.ipynb | 447 ++++++++++++++++++
.../library/characterization/t2hahn.py | 4 +-
qiskit_experiments/test/t2hahn_backend.py | 2 +-
3 files changed, 450 insertions(+), 3 deletions(-)
create mode 100644 docs/tutorials/t2hahn_characterization.ipynb
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
new file mode 100644
index 0000000000..1e8d1a044f
--- /dev/null
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -0,0 +1,447 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# T<sub>2</sub> Hahn Characterization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The purpose of the $T_2$ Hahn Echo experiment is to determine $T_2$ qubit property. \n",
+ "\n",
+ "In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for the transverse magnetization to fall to approximately 37% ($\\frac{1}{e}$) of its initial value.\n",
+ "\n",
+ "Since the qubit exposed to other noises (like $T_1$), we are using a Rx pulse for decoupling and to solve our inaccuracy for the qubit frequncy estimation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "import qiskit\n",
+ "from qiskit.utils import apply_prefix\n",
+ "from qiskit_experiments.library.characterization.t2hahn import T2Hahn"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The circuit used for the experiment comprises the following:\n",
+ "\n",
+ " 1. Rx gate\n",
+ " 2. delay\n",
+ " 3. measurement\n",
+ "\n",
+ "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi. By varying the extension of the delays, we get a series of decaying measurments. We can draw the graph of the resulting function, and can analytically extract the desired values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set the computation units to microseconds\n",
+ "unit = \"us\" # microseconds\n",
+ "qubit = 0\n",
+ "# set the desired delays\n",
+ "conversion_factor = 1e-6\n",
+ "delays = list(range(1, 50, 1) )\n",
+ "delays = [float(_) * conversion_factor for _ in delays]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐┌─────────┐┌─┐\n",
+ "q_0: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
+ " └─────────┘└─────────────────┘└───────┘└─────────────────┘└─────────┘└╥┘\n",
+ "c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
+ " 0 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "exp1 = T2Hahn(qubit, delays)\n",
+ "print(exp1.circuits()[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We run the experiment on a simple, simulated backend, created specifically for this experiment's tutorial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
+ "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
+ "from qiskit_experiments.test.utils import FakeJob\n",
+ "\n",
+ "\n",
+ "estimated_t2hahn = 20\n",
+ "# The behavior of the backend is determined by the following parameters\n",
+ "backend = T2HahnBackend(\n",
+ " t2hahn=[20],\n",
+ " frequency=[100100],\n",
+ " initialization_error=[0.0],\n",
+ " readout0to1=[0.02],\n",
+ " readout1to0=[0.02],\n",
+ " conversion_factor=conversion_factor,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The resulting graph will have the form:\n",
+ "$f(t) = a \\cdot e^{-t/T_2}+ b$\n",
+ "where *t* is the delay and $T_2$ is the decay factor.\n",
+ "`conversion_factor` is a scaling factor that depends on the measurement units used. It is 1E-6 here, because the unit is microseconds."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dt_factor = apply_prefix(1, unit)\n",
+ "expdata1 = exp1.run(backend=backend, shots=2000)\n",
+ "expdata1.block_for_results() # Wait for job/analysis to finish.\n",
+ "\n",
+ "# Display the figure\n",
+ "display(expdata1.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.80948315e-01 4.91279801e-01 2.17599437e-05] ± [4.99342220e-03 3.32804486e-03 6.35068642e-07]\n",
+ "- χ²: 1.0442511540259622\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 2.1759943722224376e-05 ± 6.350686415844018e-07 s\n",
+ "- χ²: 1.0442511540259622\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print results\n",
+ "for result in expdata1.analysis_results():\n",
+ " print(result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Additional fitter result data is stored in the `result.extra` field"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{}"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "expdata1.analysis_results(\"T2\").extra"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Providing initial user estimates\n",
+ "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameters $A$ and $B$ is $0.5$.In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar values computed for other qubits."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1e-06, 2e-06, 3e-06, 4e-06, 4.9999999999999996e-06, 6e-06, 7e-06, 8e-06, 9e-06, 9.999999999999999e-06, 1.1e-05, 1.2e-05, 1.3e-05, 1.4e-05, 1.4999999999999999e-05, 1.6e-05, 1.7e-05, 1.8e-05, 1.8999999999999998e-05, 1.9999999999999998e-05, 2.1e-05, 2.2e-05, 2.3e-05, 2.4e-05, 2.4999999999999998e-05, 2.6e-05, 2.7e-05, 2.8e-05, 2.9e-05, 2.9999999999999997e-05, 3.1e-05, 3.2e-05, 3.2999999999999996e-05, 3.4e-05, 3.5e-05, 3.6e-05, 3.7e-05, 3.7999999999999995e-05, 3.9e-05, 3.9999999999999996e-05, 4.1e-05, 4.2e-05, 4.2999999999999995e-05, 4.4e-05, 4.4999999999999996e-05, 4.6e-05, 4.7e-05, 4.8e-05, 4.9e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(delays)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from qiskit_experiments.library.characterization import T2RamseyAnalysis\n",
+ "user_p0 = {\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5}\n",
+ "\n",
+ "exp_with_p0 = T2Hahn(qubit, delays)\n",
+ "exp_with_p0.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
+ "expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n",
+ "expdata_with_p0.block_for_results()\n",
+ "\n",
+ "# Display fit figure\n",
+ "display(expdata_with_p0.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1e-06, 2e-06, 3e-06, 4e-06, 4.9999999999999996e-06, 6e-06, 7e-06, 8e-06, 9e-06, 9.999999999999999e-06, 1.1e-05, 1.2e-05, 1.3e-05, 1.4e-05, 1.4999999999999999e-05, 1.6e-05, 1.7e-05, 1.8e-05, 1.8999999999999998e-05, 1.9999999999999998e-05, 2.1e-05, 2.2e-05, 2.3e-05, 2.4e-05, 2.4999999999999998e-05, 2.6e-05, 2.7e-05, 2.8e-05, 2.9e-05, 2.9999999999999997e-05, 3.1e-05, 3.2e-05, 3.2999999999999996e-05, 3.4e-05, 3.5e-05, 3.6e-05, 3.7e-05, 3.7999999999999995e-05, 3.9e-05, 3.9999999999999996e-05, 4.1e-05, 4.2e-05, 4.2999999999999995e-05, 4.4e-05, 4.4999999999999996e-05, 4.6e-05, 4.7e-05, 4.8e-05, 4.9e-05]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "# exp_with_p0 = T2Hahn(qubit, delays)\n",
+ "# print(exp_with_p0.circuits()[0])\n",
+ "print(delays)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.88480306e-01 4.97918456e-01 1.97965689e-05] ± [5.00066917e-03 3.02468036e-03 5.50772490e-07]\n",
+ "- χ²: 0.8659410051879719\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 1.9796568934717197e-05 ± 5.507724903223753e-07 s\n",
+ "- χ²: 0.8659410051879719\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print results\n",
+ "for result in expdata_with_p0.analysis_results():\n",
+ " print(result)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The units can be changed, but the output in the result is always given in seconds. The units in the backend must be adjusted accordingly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1e-09\n"
+ ]
+ }
+ ],
+ "source": [
+ "from qiskit.utils import apply_prefix\n",
+ "\n",
+ "unit = \"ns\"\n",
+ "delays2 = list(range(1000, 50000, 1000))\n",
+ "conversion_factor = apply_prefix(1, unit)\n",
+ "print(conversion_factor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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aNWvGTTfdSo8ePVm9ejV9+/Zi4MBB7LjjjukOTaTR0B4GEckoo0ePYsiQ41I6ZmFhIT169ASgXbt27Lprm62e5SAi0VQwiEiDGj16FDk5ts20aNFbAEycOImpUx8GoH//Yi644BcpXf8bbyxk8+bN/OAHP0jpuFFefHEuQ4ceT5cuHcjJMaZNm5qyZVatWsVZZ42kQ4fd2HnnXHr06MbcuXPqtG6RqlQwiEiDO+qoo1mxYtVWU+XTD1u2bEmrVq3qZb1ffPEFP//5GUyePKVexq9JaWkp3bvvx8SJk2jRokXKlvnqq68oLj4Md2fGjGdYtOhdbrnldnbbrW2d1i1Slc5hEJEGl5OTQ7t27aqdN3r0KNauXUubNm2YO3cOc+fO4e677wTiPzFz+PCT+ec/X2DcuKs4//wLAXj33Xfp27cXU6Y8wLBhp1BeXs5JJw3hkksup2/fQ+vrrcV17LGDOPbYQUDwHlO1zMSJv6ewsJAHHpi2pa1Lly51XrdIVRm/h8HMjjSzJ83sUzNzMxsV0b9z2K/qNLCBQhaRFJg4cRJ9+vRl5Mgzt+yFiHcY4aabbuXkk0/jhhuuBaC8vJwzzjiVoUNPZNiwU3B3Ro8eRXHxUQwfPiJy3Tfe+Ftat86vdmrfvpDWrfN56aUXU/p+a+vJJ2dw8MG9GT78ZDp2bMvBB/dk8uQ70JOIJdUawx6GfGAxMC2cEjUQWBTzWmc4iWSI5577B61bf//s98MOO4Knnvr7Vn1atmzJDjvsQIsWeXH3RlQqLCzkwgt/zV133cGKFSu4/fZb+frrr5k0KdgzMW/eyzz++GPsv/8BPPnkDAAefPAh9ttv/2rHO/vsczjhhGHVzisvLyUnJ58OHTok+nbr1fLlH3LPPZM5//yLuOSSy1m06C0uuuiXAJx3XmrP/5DtW8YXDO7+LPAsgJlNTWLRz919db0EJSJ1csQRR3Lnnd+fR5CK4+qdO3emVatWTJz4e+6/fwqzZs1lp512AuCwww6nrKwi4bFat25N69atq51XVvYNubk71TneVKmoqKBXryKuv/53APTseSAffLCMu+++UwWDpFTGH5Kog7+Y2X/M7GUzOzHdwYjI91q0yGPPPffcMqXqr/UDDujBPfdMZty48fTp07fW4zSmQxKFhYXsu2+3rdr22WdfPv54ZZoikmyV8XsYaqEUuBh4GdgEHA88ZmYj3f3htEYmIklp3nwHNm/enHB/d6dbt+6MGze+TuttTIck+vY9jKVL39+qbdmypXTqtHuaIpJslXUFg7uvBSbGNC0wszbApcA2BYOZjQHGABQUFFBSUpLwukpLS2vsX14OTbJ5H049KCsrZcmSknSHkRU2biyjomIzZWXfpDuUrWzevJGKik1x44qd37FjB1577RXef38x+fn57LLLLjSJ85/q3nun8OKLc/nhD3/Ixo3fsXFj7WPMy2tOXl5BtfMqKtrQpElTIP57qE5paSkffvhhOEYFy5cv47XXXmaXXXbZcjLnlCn3MGXKFBYsWJjwMmPHjmHAgP785jcTGDp0KG+//TZ33nkbEyZM2BJfIuOkS7zP6MaNZfouqEZFBXz2Wc19orZNtWWN6UxaMysFfuHuU5NcbiRwt7vXeKC0qKjIFyxYkPC4JSUlFBcXx52/dCnk58edLdVYsqSE7t2L0x1GVhg8uIg5c2Zn1PF2+P6yyRkzno6cv3TpUkaPHsnbby9i/fr1cS+rfPfdf9G3bxEjR/6cKVPu4vPPvyEvL69e4q/tOQxz5pQwYEC/bdpHjBjJffdNBeA3v7mG66+/lvJyT3gZgGeffYYJE65g6dL3+cEPOnHuub/gf/7nl5hZUuOkQ7x89u5dxDPPJP59vL0oLYW99qq5T9S2qSozW+juRZH9tpOC4Rbgp+7etaZ+KhjSTwVD6mRqwZBq5eXlHH54b/bdtxt3330fu+66EyUlL9O7d596WV+mnfTY2KlgSE46C4aMPyRhZvnAnuHLJkAnM+sJfOHuK83sd8Ah7v7jsP9IYCPwJlAB/AT4H+Cyho5dROrflVdezrp167j99rvIy8tjzz1/yB13TKKwsD2dOnVKd3giWaMxHGEvItj4vwm0AK4N/31dOL8Q2KPKMuOBBcDrwCnAz939lgaJVkQazPPPP8ddd93B1KkP07JlSwAuv/xKSkr+yejRI9McnUh2yfg9DO5eAlgN80dVef0H4A/1G5WIZIL+/Qfw7bdbn904fPiIhO7mKCLJaQx7GERERCTNVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEinj78MgIrXTtm0hhx7al+bNc9MdSlbZuLFMOU2hePls27YwDdFITVQwiGSpBx98Ss/mqAfKaWopn42HDkmIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiETK+ILBzI40syfN7FMzczMblcAy+5vZHDNbHy43wcysAcIVERHJShlfMAD5wGLgAmB9VGcz2xl4HlgDHBwudwnwq3qMUUREJKtl/NMq3f1Z4FkAM5uawCLDgTxgpLuvBxab2T7Ar8zsZnf3egtWREQkSzWGPQzJ6gu8GBYLlWYC7YHOaYlIRESkkcv4PQy10A74pErbmph5y2NnmNkYYAxAQUEBJSUlCa+otLS0xv7l5dAkG0uyelRWVsqSJSXpDiNrKJ+pp5ymlvKZnIoK+OyzmvtEbZtqKxsLhqS4+xRgCkBRUZEXFxcnvGxJSQk19V+6FPLz6xjgdmbJkhK6dy9OdxhZQ/lMPeU0tZTP5JSWwl571dwnattUW9n49+9qoKBKW0HMPBEREUlSNhYM84EjzCw3pq0/8BnwUVoiEhERaeQyvmAws3wz62lmPQni7RS+7hTO/52ZzYpZ5I/Ad8BUM9vPzIYClwO6QkJERKSWMr5gAIqAN8OpBXBt+O/rwvmFwB6Vnd19HcEehfbAAuBOYCJwc8OFLCIikl0y/qRHdy8B4t6l0d1HVdP2DnBk/UUlIiKyfWkMexhEREQkzVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEahQFg5mdZ2bLzazMzBaa2RE19C02M69m2qchYxYREckmGV8wmNnJwCTgt8CBwDzg72bWKWLR7kBhzLSsPuMUERHJZhlfMAC/Aqa6+73u/q67/xJYBZwbsdx/3H11zLS5/kMVERHJThldMJjZDkAv4Lkqs54DDo1YfIGZrTKzWWbWr14CFBER2U6Yu6c7hrjMrD3wKfAjd58b0z4BGO7ue1ezzN5AP+B1YAdgBHBOOMaL1fQfA4wBKCgo6DV9+vSE4ystLSU/Pz/u/PJyaJLRJVnmKSsrJTc3fk4lOcpn6imnqaV8JqeiAnJyau4TtW2qql+/fgvdvSiqX7OER2wk3P194P2Ypvlm1hm4BNimYHD3KcAUgKKiIi8uLk54XSUlJdTUf+lSSOJ3JsCSJSV0716c7jCyhvKZesppaimfySkthb32qrlP1LaptjL979+1wGagoEp7AbA6iXFeBX6YqqBERES2NxldMLj7BmAh0L/KrP4EV0skqifBiZIiIiJSC43hkMTNwENm9hrwMsH5CO2BuwHMbBqAu58Rvr4Q+AhYQnAOw+nAEOCEhg1bREQke2R8weDuj5nZrsB4gvspLAYGufuKsEvV+zHsAPwf0BFYT1A4DHb3ZxsoZBERkayT8QUDgLtPBibHmVdc5fXvgd83QFgiIiLbjYw+h0FEREQygwoGERERiaSCQURERCKpYBAREZFIKhhEREQkkgoGERERiaSCQURERCKpYBAREZFIKhhEREQkkgoGERERiaSCQURERCKpYBAREZFIST18ysz6AAOBPgSPmG4BrAXeB+YAM9z9y1QHKSIiIumV0B4GMxtpZu8A84CLgDxgGfAq8CXQG7gP+NTMpppZl3qKV0RERNIgcg+Dmb0N7AZMA84A3nJ3r6ZfS+A4YDjwLzMb5e6PpTheERERSYNEDkncD9zj7mU1dXL3dcAjwCNm1gNol4L4REREJANEFgzuPinZQd19EbCoVhGJiIhIxtFVEiIiIhIp4askzGwI8FOgG9A6bP4C+BfwN3efkergREREJDMkctLjLsBTwKHASmAJsDSc3RooBkaa2XzgOF1WCV9+CU88AYsXQ8eOMHgwtGq1dZ+vvoJnnoH//Afatt22T9T8bB2jsBDeeiv5MRrje22IMeLlMxVxZNp7zfTPaCriaIz5SudnNBVjZFq+PvkE9tsPTjwRdtmFhuXuNU7AAwSFwhE19Dkc+Ai4P2q82kzAecByoAxYWFMsYf8fhf3KgA+BcxJZT69evTwZs2fP3up1RYX7+PHuubnuO+7obuael+eek+N+wQXun3wSTBdcELTl5W3b5+OPa56f7WPcdNPspMZozO+1Icaoms9UxJGp7zVTP6PKecN/RrM95zvuGGxnxo8PtjtR26YowAJPZFsc2SG4MdOwBPqdDHyeyEqTmcJxNwJnA/sCtwOlQKc4/bsA34b99g2X2wicELWuuhYM48cHv1DYdmrRIvilX3BB8O94fQ45pOb52T7GTTfNTmqMxvxeG2KMqvlMRRyZ+l4z9TOqnDf8Z3R7yXleXrDdido2RUllwfAN0D+BfscA3ySy0mQmgptD3VulbRnwuzj9bwSWVWm7D5gfta66FAxffBFUfNX9UiunHXYIqsSa+kRN2T5G7JdHKqZMfq8NMUbVfKYijkx9rw01RrKfUeW85qk+PqPbU85zc92//DL+tikRqSwYngNKgJ1q6LNT2GdmIitNdAJ2ADYBJ1VpvxOYE2eZucCdVdpOCvcyNK9pfXUpGKZMCXYT1ecHVJMmTZo0aYqddtwx2P7E2zYlItGCIZGrJC4Mi4EVZvYMsJjgdtAAuwDdgcHAZqBfAuMlow3QFFhTpX0NcHScZdoBL1TTv1k43qrYGWY2BhgDUFBQQElJScLBlZaWbumfnw/XXvv9vIsvLk54HBERkUTddFPJVq/z8yF20xW7bUqpRKoKoBC4BfiAoDCoCKfNwL/Dee0TGSuZieABVw4cWaV9AvB+nGWWAhOqtB0ZjlNY0/rqew/DDjsEU12qyWwfoz4OSWTqe22IMarb3VvXODL1vTbUGLU5JKGcx5/q4zO6PeW8IfcwJHTjJndf5e4XufuewI5Ah3DKd/c9wnmf1b182cZagqKkoEp7AbA6zjKr4/TfFI5XL048ETZvju5nVvd1aYyGX4fGaPh1aIyGX4fGaPh11HWMzZvhpJPqHkcikr7To7uXhQXEKndfXx9BxaxrA8Hlkf2rzOpP8OTM6syP03+Bu29MbYTf22UXuPhiyMurfn6LFnDuuXDOOcG/4/U55JCa52uMxhlnNo3RWOLMpjEaS5zZNEZjiDMvL9juVL2nQ72J2gUBDE1kV0WVZQqBPskuF2esk4ENwGiCyyQnEVxWuXs4fxowLaZ/5WWVt4b9R4fL1/tllboPg+7DkGlj6D4M6f+MKue6D0O23IfBgr7xmdmnwH+Bu4E/ufsXNfQ9AhhB8Ijri9x9StIVTPXjngdcSlCILA7HnhvOKwFw9+KY/j8iOK+iO/AZcKO73x21nqKiIl+wYEHCcZWUlFBcXLxNe9U7PR53HLRsuXWfqnf1qtonan62jlFYWMKqVcVJj9EY32tDjBEvn6mII9Pea6Z/RpXzhv+MZmPOK+/0eNJJ8fcsxNs2xWNmC929KLJfAgVDHnAx8AuCqyLeJXgS5X+B8rCtK1AEtCS4rPFKd493yCBjpapgqLR0aXD2qiRuyZISuncvTncYWUP5TD3lNLWUz+SUlsJee9Xcp74KhkQeb/0dcJ2Z/S/wM2Ag0JvgCoZc4HPgPYJDBY+5+3sJRykiIiKNQsJPq3T3DWY2i+DJlGX1GJOIiIhkmMirJMysqZldY2ZfEtwA6Wsz+7OZtar36ERERCQjJLKH4RyCGyWVAK8TnK/wM+Br4Mx6i0xEREQyRiIFw9kED38aW9lgZmOBO8xsrAf3ShAREZEslsiNm7oCj1dpe4zgGQ+7pzwiERERyTiJFAz5BIcfYn0T/twpteGIiIhIJkr0KokOZtY15nXTmPavYju6+4epCExEREQyR6IFwxNx2mdU09a0mjYRERFpxBIpGHQlhIiIyHYukTs9/qEhAhEREZHMlfTjrUVERGT7o4JBREREIqlgEBERkUgqGERERCSSCgYRERGJpIJBREREIqlgEBERkUgqGERERCRSRhcMZpZjZreb2Voz+9bMnjSzjhHLXGNmXmVa3VAxi4iIZKOMLhiAW4ETgFOBI4CdgafNLOp5Fe8DhTHT/vUYo4iISNZL9OFTDc7MWgJnAWe6+/Nh2whgBXA0MLOGxTe5u/YqiIiIpEgm72HoBTQHnqtscPePgXeBQyOW7Wpmn5nZcjObXuXR3CIiIpKkTC4Y2gGbgbVV2teE8+J5FRgFDATODvvOM7Nd6yFGERGR7YK5e8Ou0Ox64MqIbv2A9sA0oLnHBGlm/wSWufvYBNeXD3wI/K+731zN/DHAGICCgoJe06dPT+h9AJSWlpKfnx93fnk5NMnkkiwDlZWVkpsbP6eSHOUz9ZTT1FI+k1NRATk5NfeJ2jZV1a9fv4XuXhTVLx3nMNwKPBzRZyXQB2gKtAH+GzOvAHgx0ZW5e6mZLQF+GGf+FGAKQFFRkRcXFyc6NCUlJdTUf+lSSOJ3JsCSJSV0716c7jCyhvKZesppaimfySkthb32qrlP1Lapthq8YHD3tWx7mGEbZrYQ2Aj0B/4YtnUE9gXmJbo+M8sF9gFm1yZeERERyeBzGNx9HXA/8HszO9rMDgQeAt4GXqjsZ2bvmdkvYl7fZGY/MrMuZtYbeALYEfhDw74DERGR7JGxl1WGLgQ2AY8BLYBZwBnuvjmmz94Ehy0qdQQe5ftDGa8Afdx9RUMELCIiko0yumBw93Lgl+EUr49VeX1KfcclIiKyvcnYQxIiIiKSOVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEyuiCwczGmNlsM/vKzNzMOie43Alm9i8zKw9//qyeQxUREclqGV0wAHnAc8A1iS5gZn2Bx4BHgJ7hz8fNrHc9xCciIrJdaJbuAGri7rcCmFlREotdCMx29xvC1zeYWb+w/dRUxiciIrK9yPQ9DLXRl2CvRKyZwKFpiEVERCQrZPQehlpqB6yp0rYmbN+GmY0BxgAUFBRQUlKS8IpKS0tr7F9eDk2ysSSrR2VlpSxZUpLuMLKG8pl6ymlqKZ/JqaiAzz6ruU/Utqm2GrxgMLPrgSsjuvVz95IGCAd3nwJMASgqKvLi4uKEly0pKaGm/kuXQn5+HQPczixZUkL37sXpDiNrKJ+pp5ymlvKZnNJS2GuvmvtEbZtqKx17GG4FHo7os7IO468GCqq0FYTtIiIiUgsNXjC4+1pgbT2uYj7QH/i/mLb+wLx6XKeIiEhWy+hzGMysHcG5B5U7YLqZWStgpbt/EfaZBbzm7uPCPpOAuWZ2OTAD+BnQDzi8AUMXERHJKpl+St45wJsE91IAeCZ8fXxMnz2AwsoX7j4POAUYBbwNnAGc7O6vNkC8IiIiWSmj9zC4+zVE3LTJ3TtX0/YE8ES9BCUiIrIdyvQ9DCIiIpIBVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEkkFg4iIiETK6ILBzMaY2Wwz+8rM3Mw6J7DMqLBv1Sm3AUIWERHJSs3SHUCEPOA54G/ALUks9x2wR2yDu5elMC4REZHtSkYXDO5+K4CZFSW/qK9OfUQiIiLbp4w+JFEHLcxshZl9YmZPm9mB6Q5IRESkMTN3T3cMkcI9DK8DXdz9o4i+fYG9gEXATsAFwCCgh7svq6b/GGAMQEFBQa/p06cnHFdpaSn5+flx55eXQ5NsLcnqSVlZKbm58XMqyVE+U085TS3lMzkVFZCTU3OfqG1TVf369Vvo7pF78hu8YDCz64ErI7r1c/eSmGUSLhiqWV9T4C1gtrufX1PfoqIiX7BgQcJjl5SUUFxcHHf+0qWQxO9MgCVLSujevTjdYWQN5TP1lNPUUj6TU1oKe+1Vc5+obVNVZpZQwZCOcxhuBR6O6LMyVStz981mtgD4YarGFBER2d40eMHg7muBtQ21PjMz4ACCQxQiIiJSCxl9lYSZtQPaEZyTANDNzFoBK939i7DPLOA1dx8Xvr4aeAVYBuwMnE9QMJzbsNGLiIhkj0w/Je8c4E3gkfD1M+Hr42P67AEUxrxuBUwB3iW4h0MH4Eh3f62+gxUREclWGb2Hwd2vAa6J6NO5yuuLgIvqLSgREZHtUKbvYRAREZEMoIJBREREIqlgEBERkUgqGERERCSSCgYRERGJpIJBREREIqlgEBERkUgqGERERCSSCgYRERGJpIJBREREIqlgEBERkUgqGERERCSSCgYRERGJpIJBREREIqlgEBERkUgqGERERCSSCgYRERGJpIJBREREIqlgEBERkUgqGERERCRSxhYMZtbazG43s/fMbL2ZfWxmd5nZrgkse4KZ/cvMysOfP2uImEVERLJVxhYMQHugA3ApsD9wOnAk8GhNC5lZX+Ax4BGgZ/jzcTPrXZ/BioiIZLNm6Q4gHndfDAyNafrAzC4Bnjaznd396ziLXgjMdvcbwtc3mFm/sP3U+opXREQkm2XyHobq7AyUA9/V0Kcv8FyVtpnAofUVlIiISLYzd093DAkxs1bA68Df3f38GvptAEa7+7SYtjOAe909p5r+Y4AxAAUFBb2mT5+ecEylpaXk5+fHnb9hAzSS9GaM8vJScnLi51SSo3ymnnKaWspncsxghx1q7hO1baqqX79+C929KKpfgx+SMLPrgSsjuvVz95KYZfKBp4BPCc5pSBl3nwJMASgqKvLi4uKEly0pKSGZ/hJNOU0t5TP1lNPUUj5Tr75ymo5zGG4FHo7os7LyH2Gx8Gz48jh3L4tYdjVQUKWtIGwXERGRWmjwgsHd1wJrE+lrZjsBfwcMGOjupQksNh/oD/xfTFt/YF6SoYqIiEgoY6+SCIuF5whOdBwC7GhmO4azv3D3DWG/WcBr7j4unDcJmGtmlwMzgJ8B/YDDGy56ERGR7JLJV0n0AvoA3YClwKqYKfaKhz2AwsoX7j4POAUYBbwNnAGc7O6vNkjUIiIiWShj9zCEJz1aAv06V9P2BPBE6qMSERHZPmXyHgYRERHJECoYREREJJIKBhEREYmkgkFEREQiqWAQERGRSCoYREREJFKjefhUQzCz/wIrklikDQnetVISppymlvKZesppaimfqZdsTnd3992iOqlgqAMzW5DIE74kccppaimfqaecppbymXr1lVMdkhAREZFIKhhEREQkkgqGupmS7gCykHKaWspn6imnqaV8pl695FTnMIiIiEgk7WEQERGRSCoYREREJJIKhloys05m9pSZfWtma83sNjPbId1xNRZm1sPMHjWzj81svZm9b2aXmlmTKv32N7M5YZ9PzWyCmUU+9nx7ZmZtwly5mbWpMk/5TJKZnW5mb5lZWfh/fVqV+cppgszsYDN7wcy+CqdZZnZIlT7KZxxmNsnMFoSfxY/i9InMn5mdYGb/MrPy8OfPEll/sxS8h+2OmTUFngE+B44AdgX+ABjwyzSG1pj0Av4LjABWAocA9xJ8Jn8LYGY7A88Dc4GDgX2AB4FvgYkNH3Kj8SDwFtA+tlH5TJ6ZnQ+MAy4BXgFaAHvFzFdOE2Rm+cA/CL47+xB8X14JzDSzTu7+jfIZqQnBtmZ/YEDVmYnkz8z6Ao8BVwN/AYYCj5vZYe7+ao1rd3dNSU7AsUAF8IOYttOBMmDndMfXWCfg98DCmNfnAl8DLWLaxgOfEp6wq2mbHF4AzAKOAhxoo3zWOpetwi/a/jX0UU4Tz2dR+JnsEtPWJWwrUj6TyuXFwEfVtEfmLywWnq+y3AvAo1Hr1SGJ2ukLvOvuH8e0zQRyCP5yltrZGfgy5nVf4EV3Xx/TNpPgL+fODRhXo2BmBwKXAWcQFLRVKZ/JGQA0BQrC3bafmtlfzaxrTB/lNHHvE+xVPMvMcswsBzibYA/jkrCP8lk3ieSvL/BcleVmAodGDa6CoXbaAWuqtK0FNofzJElmdhAwCrgrprm6PK+JmSchM9sRmA780t0/jdNN+UxOV4LvyPHAr4CfAc2B2WaWF/ZRThPk7t8AxcAw4LtwOplgD07lBk75rJtE8hevT2R+VTBI2pnZ3gTHNW919z+nO55G6jbgJeUvpZoQFAjnu/s/3P01YDjQFvhJWiNrhMysBfAAwbkgfYDDgDeBv4UFr2Q4FQy1sxooqNLWhmD35eqGD6fxMrN9gBJgurtfXmV2dXkuiJkn3/sxMMrMNpnZJoLzGABWm9kNlf9G+UzGqvDnvyob3H0d8BnQKWxSThN3GrAHcKa7v+7ur4RtnQj23oDyWVeJ5C9en8j8qmConfnAvmbWMaatP1AOLExPSI2PmXUjKBYed/eLqukyHzjCzHJj2voTfGF/VO8BNi4DgB5Az3AaHbYXE+x9AOUzWS+HP/eubAjP9C8EVoRNymni8ghOcIw9v6YibKvcFimfdZNI/uaHbVTpMy9y9HSf7dkYJ4I9Ce8A/wQOBI4mOAv19nTH1lgmoDvBcbPpBMfOtkwxfVoSVL3Tgf0ILv/5Gvh1uuPP9ImgUKh6lYTymXweZwCLCXafdwMeD79485TTpHO5D8GVZHcB+4bfAQ8B64COymdCOdyT4A+CmwmKgJ7htEOi+SM4uXETcHn4OxkHbAR6R64/3QlorBPBbrSnCU7c+Zzgr7icdMfVWCbgmnCDts1Upd/+BNcUlxHsIr4aXV6VSH63KRiUz1rlcSeC+4N8QXAFz1PAHspprfPZH3gJ+CrM52zgUOUz4fyVxPne7JxM/oATgfeADcC7wNBE1q+HT4mIiEgkncMgIiIikVQwiIiISCQVDCIiIhJJBYOIiIhEUsEgIiIikVQwiIiISCQVDCLbCTMbZWYeM31rZh+FT2AcZmZWy3GLw/GKUxtxjevc6r1Umedmdn0dxz+9Sq6a1S1ikcZPBYPI9uckgkfcDgKuIril+aPA8+EDghqToQTvJdX+EY57fz2MLdIoqWoW2f685e4fxLx+yMweJ7jt8e+BX6YnrFp5090/SvWg7r4WWGtmA1M9tkhjpT0MIoIHj8X+G3C2meVVtptZnpndaGbLzWxD+PNKM6vxu8PMBpjZs2a2ysy+M7PFZvZrM2sa0+cpM3uzmmW7mFmFmZ2TivcWvoenwlh6hG0Hm9nzZva5ma03sw/NbHIq1ieSrbSHQUQqPQsMAYqAueFx+5kED136DcED1/oQHMZoDfy6hrG6Ejxi+3aCe9oXETw/ZDeCh95A8BCiZ8zsEHd/LWbZMcC3wCN1fUNm1prgmS9tCJ5ZsDx84uRM4DVgFPAN0JngoTwiEocKBhGptDL8WRj+PBU4HPiRu88N22aF50ZebWY3uvt/qhvI3e+u/Hd4MuWLwA7AxWZ2hbtXEJwn8CEwlmDjjZk1B84EHnH3b+ryZsysE0FhUAoc5u7/DWftA+wCXOrub8csMrUu6xPJdjokISKVKq+SqLzqYCCwAphnZs0qJ+A5oDnB3obqBzIrNLN7zGwFwRPxNgLXA62AtgBh0XAPcIqZtQwXHQIUhO110Q2YB3wM9IspFgCWETwt8Z7waogf1HFdItsFFQwiUqlyw7kq/NkW2J1gYx87VR4+2LW6QcLzG54EjiMoEo4CDgZuCLvkxnS/H2gKjAhfnwO85u7bnNuQpCOBDsD97l4aO8Pd1wH9gM+AycDK8ByLE+q4TpGspkMSIlJpMMH5BgvD158Dy4Fhcfp/FKd9D4JzFka4+8OVjWb2k6od3f1zM/sTMNbMZhJsyEfXKvqt3QO0JLgCZFN4Umfset8CTgj3mBQB44A/mVkPd1+cgvWLZB0VDCJC+Nf18cAkd/8ubP4HcAJQ6u7vJTFc5VUWG2PGbw4Mj9N/MjAfuA9YB0xPYl3xuLv/wsw2AdPN7DR3f7yaTpuAV8zsKoL3vy+ggkGkGioYRLY/Pc2sDcFJiJ0IDh2cBDxP8Jd2pUcITkCcZWYTgUXhMnsQbFyHxBQXsd4lOPfhBjPbTFA4XBQvGHd/Jby88kjg9jhj1oq7XxjG8Ecza+Luj5nZcQRXYswg2IOyI3A+wdUS81O1bpFso4JBZPtT+Zd2GfAf4A3gFOAJd99ym2V332hmxxBcBjkG6EJwueO/gWcITmbchrtvMLMhwB3ANOAL4AGCqzDurSGmA6n7yY7VxfPrcE/DI+H5FW8A6wkuDy0kKBReB/q7+yepXr9ItrCY7wcRkbQws5eBCnc/IsH+o4AHgT2BFeGhhVTGYwQnY04gKCyap3odIo2N9jCISFqYWQ5wEHA0wU2TflqLYSpvcV2rB2fVYDjwUIrHFGnUtIdBRNLCzDoTnEPwFTDZ3a9MYtldCQ6RAODuC1IcW2uCu1XWy/gijZEKBhEREYmkGzeJiIhIJBUMIiIiEkkFg4iIiERSwSAiIiKRVDCIiIhIJBUMIiIiEun/AbZNvZ0dLMb4AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "p0 = {\n",
+ " \"A\": [0.5],\n",
+ " \"T2star\": [20000],\n",
+ " \"B\": [0.5],\n",
+ "}\n",
+ "backend_in_ns = T2HahnBackend(\n",
+ " t2hahn=[20],\n",
+ " frequency=[100100],\n",
+ " initialization_error=[0.0],\n",
+ " readout0to1=[0.02],\n",
+ " readout1to0=[0.02],\n",
+ " conversion_factor=conversion_factor,\n",
+ ")\n",
+ "exp_in_ns = T2Hahn(qubit, delays2, unit=unit)\n",
+ "user_p0_ns = {\n",
+ " \"A\": 0.5,\n",
+ " \"T2\": 20000.0,\n",
+ " \"B\": 0.5\n",
+ "}\n",
+ "exp_in_ns.set_analysis_options(p0=user_p0_ns)\n",
+ "\n",
+ "# Run experiment\n",
+ "expdata_in_ns = exp_in_ns.run(backend=backend_in_ns, shots=2000).block_for_results()\n",
+ "\n",
+ "# Display Figure\n",
+ "display(expdata_in_ns.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Print Results\n",
+ "for result in expdata_in_ns.analysis_results():\n",
+ " print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import qiskit.tools.jupyter\n",
+ "%qiskit_copyright"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 3d7a8d5e0f..befec587fa 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -74,7 +74,7 @@ def _default_experiment_options(cls) -> Options:
options.delays = None
options.unit = "s"
- options.conversion_factor = None
+ options.conversion_factor = 1
options.osc_freq = 0.0
options.num_echoes = 1
return options
@@ -163,7 +163,7 @@ def circuits(self) -> List[QuantumCircuit]:
raise ValueError("Conversion factor is not set.")
circuits = []
- for delay_gate in prefactor * np.asarray(self.experiment_options.delays, dtype=float):
+ for delay_gate in np.asarray(self.experiment_options.delays, dtype=float):
total_delay = delay_gate * (self.experiment_options.num_echoes + 1)
# delay_gate = delay
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index c2f8ac129b..3c694daf9e 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -48,7 +48,7 @@ def __init__(
backend_name="T2Hahn_simulator",
backend_version="0",
n_qubits=int(1e6),
- basis_gates=["barrier", "ry", "rx", "delay", "measure"],
+ basis_gates=["barrier", "rx", "delay", "measure"],
gates=[],
local=True,
simulator=True,
From d5aea8772cfa292e5b30dd17ec650b2e95c87857 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 6 Dec 2021 19:16:46 +0200
Subject: [PATCH 59/93] Added tutorial and fix bug for echoes
---
docs/tutorials/t2hahn_characterization.ipynb | 311 ++++++++++++------
.../analysis/t2hahn_analysis.py | 1 +
.../library/characterization/t2hahn.py | 8 +-
qiskit_experiments/test/t2hahn_backend.py | 28 +-
test/test_t2hahn.py | 7 +-
5 files changed, 249 insertions(+), 106 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 1e8d1a044f..34bddbca29 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -15,7 +15,7 @@
"\n",
"In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for the transverse magnetization to fall to approximately 37% ($\\frac{1}{e}$) of its initial value.\n",
"\n",
- "Since the qubit exposed to other noises (like $T_1$), we are using a Rx pulse for decoupling and to solve our inaccuracy for the qubit frequncy estimation."
+ "Since the qubit exposed to other noises (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequncy estimation."
]
},
{
@@ -41,12 +41,12 @@
" 2. delay\n",
" 3. measurement\n",
"\n",
- "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi. By varying the extension of the delays, we get a series of decaying measurments. We can draw the graph of the resulting function, and can analytically extract the desired values."
+ "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi$. By varying the extension of the delays, we get a series of decaying measurments. We can draw the graph of the resulting function, and can analytically extract the desired values."
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -61,7 +61,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 3,
"metadata": {
"scrolled": true
},
@@ -93,9 +93,17 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1e-06\n"
+ ]
+ }
+ ],
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
"# FakeJob is a wrapper for the backend, to give it the form of a job\n",
@@ -111,7 +119,8 @@
" readout0to1=[0.02],\n",
" readout1to0=[0.02],\n",
" conversion_factor=conversion_factor,\n",
- ")"
+ ")\n",
+ "print(conversion_factor)"
]
},
{
@@ -119,21 +128,21 @@
"metadata": {},
"source": [
"The resulting graph will have the form:\n",
- "$f(t) = a \\cdot e^{-t/T_2}+ b$\n",
+ "$f(t) = a \\cdot e^{-\\frac{t}{T_2}}+ b$\n",
"where *t* is the delay and $T_2$ is the decay factor.\n",
"`conversion_factor` is a scaling factor that depends on the measurement units used. It is 1E-6 here, because the unit is microseconds."
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 5,
"metadata": {
- "scrolled": false
+ "scrolled": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -144,6 +153,9 @@
],
"source": [
"dt_factor = apply_prefix(1, unit)\n",
+ "\n",
+ "# exp1.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
+ "\n",
"expdata1 = exp1.run(backend=backend, shots=2000)\n",
"expdata1.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
@@ -153,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -162,16 +174,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.80948315e-01 4.91279801e-01 2.17599437e-05] ± [4.99342220e-03 3.32804486e-03 6.35068642e-07]\n",
- "- χ²: 1.0442511540259622\n",
+ "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527131e-03 3.03978270e-03 5.77293057e-07]\n",
+ "- χ²: 0.7488240853426228\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 2.1759943722224376e-05 ± 6.350686415844018e-07 s\n",
- "- χ²: 1.0442511540259622\n",
+ "- value: 1.9828300679956625e-05 ± 5.772930568055365e-07 s\n",
+ "- χ²: 0.7488240853426228\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -193,7 +205,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -202,7 +214,7 @@
"{}"
]
},
- "execution_count": 28,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -221,29 +233,12 @@
},
{
"cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1e-06, 2e-06, 3e-06, 4e-06, 4.9999999999999996e-06, 6e-06, 7e-06, 8e-06, 9e-06, 9.999999999999999e-06, 1.1e-05, 1.2e-05, 1.3e-05, 1.4e-05, 1.4999999999999999e-05, 1.6e-05, 1.7e-05, 1.8e-05, 1.8999999999999998e-05, 1.9999999999999998e-05, 2.1e-05, 2.2e-05, 2.3e-05, 2.4e-05, 2.4999999999999998e-05, 2.6e-05, 2.7e-05, 2.8e-05, 2.9e-05, 2.9999999999999997e-05, 3.1e-05, 3.2e-05, 3.2999999999999996e-05, 3.4e-05, 3.5e-05, 3.6e-05, 3.7e-05, 3.7999999999999995e-05, 3.9e-05, 3.9999999999999996e-05, 4.1e-05, 4.2e-05, 4.2999999999999995e-05, 4.4e-05, 4.4999999999999996e-05, 4.6e-05, 4.7e-05, 4.8e-05, 4.9e-05]\n"
- ]
- }
- ],
- "source": [
- "print(delays)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -267,27 +262,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1e-06, 2e-06, 3e-06, 4e-06, 4.9999999999999996e-06, 6e-06, 7e-06, 8e-06, 9e-06, 9.999999999999999e-06, 1.1e-05, 1.2e-05, 1.3e-05, 1.4e-05, 1.4999999999999999e-05, 1.6e-05, 1.7e-05, 1.8e-05, 1.8999999999999998e-05, 1.9999999999999998e-05, 2.1e-05, 2.2e-05, 2.3e-05, 2.4e-05, 2.4999999999999998e-05, 2.6e-05, 2.7e-05, 2.8e-05, 2.9e-05, 2.9999999999999997e-05, 3.1e-05, 3.2e-05, 3.2999999999999996e-05, 3.4e-05, 3.5e-05, 3.6e-05, 3.7e-05, 3.7999999999999995e-05, 3.9e-05, 3.9999999999999996e-05, 4.1e-05, 4.2e-05, 4.2999999999999995e-05, 4.4e-05, 4.4999999999999996e-05, 4.6e-05, 4.7e-05, 4.8e-05, 4.9e-05]\n"
- ]
- }
- ],
- "source": [
- "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
- "# exp_with_p0 = T2Hahn(qubit, delays)\n",
- "# print(exp_with_p0.circuits()[0])\n",
- "print(delays)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -296,16 +271,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.88480306e-01 4.97918456e-01 1.97965689e-05] ± [5.00066917e-03 3.02468036e-03 5.50772490e-07]\n",
- "- χ²: 0.8659410051879719\n",
+ "- value: [4.78978431e-01 5.02409209e-01 2.01192655e-05] ± [5.09032092e-03 3.07792331e-03 5.78387141e-07]\n",
+ "- χ²: 0.5509343846546172\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 1.9796568934717197e-05 ± 5.507724903223753e-07 s\n",
- "- χ²: 0.8659410051879719\n",
+ "- value: 2.011926549231594e-05 ± 5.783871411742618e-07 s\n",
+ "- χ²: 0.5509343846546172\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -322,41 +297,76 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The units can be changed, but the output in the result is always given in seconds. The units in the backend must be adjusted accordingly."
+ "### Number of echoes\n",
+ "The user can provide the number of echoes that the circuit will preform. This will translate to more delay gates and more echo gate. As the number of echoes is greater, the total time of the circuit will grow. This let us estimate $T_{2}$ better as we have more samples and the echoes are canceling the $T_{1}$ noise effect on the qubit.\n",
+ "Note that the delay time providedis the for each delay in the circuit and not the total time."
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1e-09\n"
+ " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
+ "q_0: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
+ " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
+ "c: 1/══════════════════════════════════════════════════════════»\n",
+ " »\n",
+ "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
+ "«q_0: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
+ "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
+ "«c: 1/══════════════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
+ "«q_0: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
+ "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
+ "«c: 1/════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌─────────────────┐┌──────────┐┌─┐\n",
+ "«q_0: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └─────────────────┘└──────────┘└╥┘\n",
+ "«c: 1/════════════════════════════════╩═\n",
+ "« 0 \n"
]
}
],
"source": [
- "from qiskit.utils import apply_prefix\n",
+ "import numpy as np\n",
+ "# set the computation units to microseconds\n",
+ "unit2 = \"us\" # microseconds\n",
+ "qubit2 = 0\n",
"\n",
- "unit = \"ns\"\n",
- "delays2 = list(range(1000, 50000, 1000))\n",
- "conversion_factor = apply_prefix(1, unit)\n",
- "print(conversion_factor)"
+ "# set the desired delays\n",
+ "conversion_factor = 1e-6\n",
+ "# delays2 = list(range(1, 25000, 100) )\n",
+ "\n",
+ "delays2 = np.append(\n",
+ " (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
+ " (np.linspace(10.5, 45.0, num=70)).astype(float),\n",
+ " )\n",
+ "delays2 = [float(_) * conversion_factor for _ in delays2]\n",
+ "num_echoes = 4\n",
+ "\n",
+ "\n",
+ "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "exp2 = T2Hahn(qubit2, delays2, num_echoes=num_echoes)\n",
+ "print(exp2.circuits()[0])\n"
]
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -366,12 +376,15 @@
}
],
"source": [
- "p0 = {\n",
- " \"A\": [0.5],\n",
- " \"T2star\": [20000],\n",
- " \"B\": [0.5],\n",
- "}\n",
- "backend_in_ns = T2HahnBackend(\n",
+ "\n",
+ "from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
+ "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
+ "from qiskit_experiments.test.utils import FakeJob\n",
+ "\n",
+ "\n",
+ "estimated_t2hahn2 = 20\n",
+ "# The behavior of the backend is determined by the following parameters\n",
+ "backend2 = T2HahnBackend(\n",
" t2hahn=[20],\n",
" frequency=[100100],\n",
" initialization_error=[0.0],\n",
@@ -379,37 +392,143 @@
" readout1to0=[0.02],\n",
" conversion_factor=conversion_factor,\n",
")\n",
- "exp_in_ns = T2Hahn(qubit, delays2, unit=unit)\n",
- "user_p0_ns = {\n",
- " \"A\": 0.5,\n",
- " \"T2\": 20000.0,\n",
- " \"B\": 0.5\n",
- "}\n",
- "exp_in_ns.set_analysis_options(p0=user_p0_ns)\n",
"\n",
- "# Run experiment\n",
- "expdata_in_ns = exp_in_ns.run(backend=backend_in_ns, shots=2000).block_for_results()\n",
"\n",
- "# Display Figure\n",
- "display(expdata_in_ns.figure(0))"
+ "dt_factor2 = apply_prefix(1, unit2)\n",
+ "\n",
+ "# exp2.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
+ "\n",
+ "expdata2 = exp2.run(backend=backend2, shots=2000)\n",
+ "expdata2.block_for_results() # Wait for job/analysis to finish.\n",
+ "\n",
+ "# Display the figure\n",
+ "display(expdata2.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### $T_{2}$ v.s $T_{2}^{\\ast}$\n",
+ "This experiment purpose is to give a better estimation for the dephasing noise. In Ramsey experiment we we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
+ "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. By using echo pulse ($Rx(\\pi)$) we can reduce the effect of the inhomogeneous broadening and get better estimation."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ┌───┐┌─────────────────┐┌─────────┐ ░ ┌───┐ ░ ┌─┐\n",
+ "q_0: ┤ H ├┤ Delay(1e-06[s]) ├┤ Rz(π/5) ├─░─┤ H ├─░─┤M├\n",
+ " └───┘└─────────────────┘└─────────┘ ░ └───┘ ░ └╥┘\n",
+ "c: 1/═══════════════════════════════════════════════╩═\n",
+ " 0 \n"
+ ]
+ }
+ ],
"source": [
- "# Print Results\n",
- "for result in expdata_in_ns.analysis_results():\n",
- " print(result)"
+ "import qiskit\n",
+ "from qiskit_experiments.library import T2Ramsey\n",
+ "from qiskit_experiments.test.t2ramsey_backend import T2RamseyBackend\n",
+ "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
+ "from qiskit_experiments.test.utils import FakeJob\n",
+ "\n",
+ "# set the computation units to microseconds\n",
+ "unit_ramsey = \"us\" # microseconds\n",
+ "qubit_ramsey = 0\n",
+ "# set the desired delays\n",
+ "delays_ramsey = list(range(1, 50, 1))\n",
+ "\n",
+ "conversion_factor_ramsey = 1e-6\n",
+ "# defining backend for the experiment\n",
+ "backend_ramsey = T2RamseyBackend(\n",
+ " p0={\n",
+ " \"A\": [0.5],\n",
+ " \"T2star\": [20.0],\n",
+ " \"f\": [100100],\n",
+ " \"phi\": [0.0],\n",
+ " \"B\": [0.5],\n",
+ " },\n",
+ " initial_prob_plus=[0.0],\n",
+ " readout0to1=[0.02],\n",
+ " readout1to0=[0.02],\n",
+ " conversion_factor=conversion_factor_ramsey,\n",
+ ")\n",
+ "\n",
+ "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "exp1_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, unit=unit_ramsey, osc_freq=1e5, backend=backend_ramsey)\n",
+ "\n",
+ "# Run the experiment\n",
+ "expdata1_ramsey = exp1_ramsey.run(backend=backend_ramsey, shots=2000)\n",
+ "expdata1_ramsey.block_for_results() # Wait for job/analysis to finish.\n",
+ "\n",
+ "# Printing a circuit for example\n",
+ "print(exp1_ramsey.circuits()[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see that while both backends have $T_2 = 20 [\\mu s]$, the estimation of the Hahn Echo experiment is better."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "\n",
+ "display(expdata1_ramsey.figure(0), expdata2.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import qiskit.tools.jupyter\n",
"%qiskit_copyright"
diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index faeb0e7b97..bab825c61b 100644
--- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -35,6 +35,7 @@ def _default_options(cls) -> Options:
options.data_processor = DataProcessor(
input_key="counts", data_actions=[Probability(outcome="0")]
)
+ options.p0 = {"amp": 0.5, "tau": 0.000001, "base": 0.5} # The analysis will not work without initial guess
options.xlabel = "Delay"
options.ylabel = "P(0)"
options.xval_unit = "s"
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index befec587fa..fe92296b4a 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -83,6 +83,7 @@ def __init__(
self,
qubit: int,
delays: Union[List[float], np.array],
+ num_echoes: int = 1,
backend: Optional[Backend] = None,
unit: str = "s",
):
@@ -103,7 +104,7 @@ def __init__(
super().__init__([qubit], backend=backend)
# Set experiment options
- self.set_experiment_options(delays=delays, unit=unit)
+ self.set_experiment_options(delays=delays, unit=unit, num_echoes=num_echoes)
self._verify_parameters()
def _verify_parameters(self):
@@ -164,10 +165,9 @@ def circuits(self) -> List[QuantumCircuit]:
circuits = []
for delay_gate in np.asarray(self.experiment_options.delays, dtype=float):
- total_delay = delay_gate * (self.experiment_options.num_echoes + 1)
- # delay_gate = delay
+ total_delay = delay_gate * (self.experiment_options.num_echoes * 2)
- delay_gate = np.round(delay_gate, decimals=10)
+ delay_gate = np.round(delay_gate, decimals=12)
circ = QuantumCircuit(1, 1)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 3c694daf9e..4f5b5c2bdf 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -149,10 +149,27 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"ZX plain": True,
"Theta": new_theta,
}
+ elif isclose(angle, -np.pi / 2):
+ new_theta = np.abs(angle - qubit_state["Theta"])
+ new_theta = new_theta % (2 * np.pi)
+ new_qubit_state = {
+ "XY plain": False,
+ "ZX plain": True,
+ "Theta": new_theta,
+ }
else:
print("Error - This angle isn't supported. We only support multipication of pi/2")
+ print("The angle is:" + str(angle))
else:
- if isclose(angle, np.pi / 2):
+ if isclose(angle, np.pi):
+ new_theta = qubit_state["Theta"] + np.pi
+ new_theta = new_theta % (2 * np.pi)
+ new_qubit_state = {
+ "XY plain": False,
+ "ZX plain": True,
+ "Theta": new_theta,
+ }
+ elif isclose(angle, np.pi / 2):
new_theta = (
qubit_state["Theta"] + 3 * np.pi / 2
) # its theta -pi/2 but we added 2*pi
@@ -162,16 +179,17 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"ZX plain": False,
"Theta": new_theta,
}
- elif isclose(angle, np.pi):
- new_theta = qubit_state["Theta"] + np.pi
+ elif isclose(angle, -np.pi / 2):
+ new_theta = np.pi / 2 - qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plain": True,
+ "ZX plain": False,
"Theta": new_theta,
}
else:
print("Error - This angle isn't supported. We only support multiplication of pi/2")
+ print("The angle is:" + str(angle))
return new_qubit_state
def _measurement_gate(self, qubit_state: dict) -> int:
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 6556f7e303..0a4b807b31 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -19,6 +19,7 @@
from qiskit.test import QiskitTestCase
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+import unittest
class TestT2Hahn(QiskitTestCase):
@@ -46,7 +47,7 @@ def test_t2hahn_run_end2end(self):
(np.linspace(1.0, 15.0, num=15)).astype(float),
(np.linspace(16.0, 45.0, num=59)).astype(float),
)
- exp = T2Hahn(qubit, delays, unit=unit)
+ exp = T2Hahn(qubit=qubit, delays=delays, unit=unit)
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
@@ -129,3 +130,7 @@ def test_t2hahn_concat_2_experiments(self):
self.assertLessEqual(res_t2_1.value.stderr, res_t2_0.value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+
+
+if __name__ == "__main__":
+ unittest.main()
From 48bc87c545b655535f6624a47ac9e5d73c7b2af5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 8 Dec 2021 16:59:17 +0200
Subject: [PATCH 60/93] Added Parallel experiment to the test and backend
---
docs/tutorials/t2hahn_characterization.ipynb | 266 +++---------------
.../library/characterization/t2hahn.py | 3 +-
qiskit_experiments/test/t2hahn_backend.py | 88 ++++--
test/test_t2hahn.py | 48 ++++
4 files changed, 158 insertions(+), 247 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 34bddbca29..deb8b33c70 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -15,7 +15,7 @@
"\n",
"In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for the transverse magnetization to fall to approximately 37% ($\\frac{1}{e}$) of its initial value.\n",
"\n",
- "Since the qubit exposed to other noises (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequncy estimation."
+ "Since the qubit exposed to other noises (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequency estimation."
]
},
{
@@ -41,7 +41,7 @@
" 2. delay\n",
" 3. measurement\n",
"\n",
- "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi$. By varying the extension of the delays, we get a series of decaying measurments. We can draw the graph of the resulting function, and can analytically extract the desired values."
+ "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
]
},
{
@@ -76,10 +76,18 @@
"c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
" 0 \n"
]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\framework\\base_experiment.py:82: UserWarning: Defining a default BaseAnalysis class for an experiment using the __analysis_class__ attribute is deprecated as of 0.2.0. Use the `analysis` kwarg of BaseExperiment.__init__ to specify a default analysis class.\n",
+ " warnings.warn(\n"
+ ]
}
],
"source": [
- "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "# Create a T2Hahn experiment. Print the first circuit as an example\n",
"exp1 = T2Hahn(qubit, delays)\n",
"print(exp1.circuits()[0])"
]
@@ -97,10 +105,14 @@
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1e-06\n"
+ "ename": "SyntaxError",
+ "evalue": "invalid syntax (t2hahn_backend.py, line 83)",
+ "output_type": "error",
+ "traceback": [
+ "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
+ " File \u001b[0;32m\"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\"\u001b[0m, line \u001b[0;32m3418\u001b[0m, in \u001b[0;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n",
+ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-4-b23a53a6ddf0>\"\u001b[1;36m, line \u001b[1;32m1\u001b[1;36m, in \u001b[1;35m<module>\u001b[1;36m\u001b[0m\n\u001b[1;33m from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\u001b[0m\n",
+ "\u001b[1;36m File \u001b[1;32m\"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\test\\t2hahn_backend.py\"\u001b[1;36m, line \u001b[1;32m83\u001b[0m\n\u001b[1;33m qubits_sates[qubit] {\"XY plain\": False, \"ZX plain\": True, \"Theta\": np.pi}\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -135,22 +147,11 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "image/png": 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w+TgHAlO99y9673/23s8GpgBjYlTfKsXHw7ff2uyA4cq9/HJs6iQiIhKpmAYAzrkkoDvwXqWX3gOCLQuVDORX2pcHHO2cS6yifMxs3Rr87j9AywWLRMcvv/xCz5496dKlC926deNlRdYiNRLrJMCWQDywttL+tUCwiazfBS51zr0GLMACiMuAxNLjVVhT1Dk3DBgGNvf43LlzI67c9u3bq1W+Wzdr4h8//ji2bEnmiiu+4oADtlQoExcH++4L1Thsg1Ldc9rY5Ofnh1yOtrLi4uJqlW9I8vPzufvuu+nWrRtr167lpJNO4sQTT6RJkyY1Om5jPqe1IRrnMz8/X383yqmtv6P1YRTAeGAfLFfAYcHCTOAGYJf7b+/9FKyLgB49evjqzJ5U3dmWNm2yWQADcwA88cQRu5RJSYE1axrvioGaCTC0lJSUas2aVl9mrRsyZAjZ2dm8+eabUTtmRkYGBx100M6fW7VqRWFhYY3PR305p/VFNM5nSkqK/m6UU1t/R2OdA5ANFAOtK+1vDVTZUO69z/PeXwKkAR2BDsByYBuwvrYqGonMTFskKNgiYmlpWi5YGqYhQ4bgnNtl+/rrrwGYNGkSzz33HAA9e/bcuchPtCxcuJDi4mL23XffqB43EpMnT6ZTp06kpKTQvXv3kEsod+zYscrz1Dcwk1gl99xzD865Xc5XcXExt912287P7dSpE7feeitFRUXVKiNSXkxbALz3Bc65hUBvoHwHXm/g1TDvLQRWATjnBgBveu/rfCHpu++Gbdvg8cdtCCDYCAHvLTgILCcs0tCceuqpPPvssxX2tWzZEoCmTZvW2udu3LiRQYMGMXXq1Fr7jGBeeuklrrnmGiZPnswJJ5zA5MmT6dOnD4sWLaJDh10HMn3xxRcUFxfvfL5mzRq6d+9e5bLMn376KVOmTKFbt267vHbffffx+OOPM3PmTA499FC+/fZbBg8eTHJyMrfddlvEZUQqiGTFoGhuwPlAAdaPfwg2JHA7sF/p67OAWeXKH4yNBDgIOBqYDWwAOob7rNpYDbAqW7Z4f8cdtkLgPvt4f9dd3v/4424dqsHRaoChVfd3tPxKa3Vp8ODBvm/fvmFfr2r1uWArFp533nm+efPm/qGHHtq5b9GiRT41NdW/+OKL3nvv8/Pz/YknnljlioK7qzrn9Oijj/aXXXZZhX0HHnigv/HGGyN6/1133eWbNm3qc3NzK+zfvHmz33///f2HH37oTz75ZH/llVdWeL1v375+0KBBFfYNGjSowr9BJGViIRq/o1oNsKLaWg0w5sMAvfcvAdcCtwJfAycAZ3rvV5QW6VC6BcRjQwe/Ad4HUoDjvffLY1Pj8Jo0gXPOsRUBs7KgRw8bHRBuhIBIQzdp0iSOO+44Lr74YtasWcOaNWuCNts//PDD/PnPf+bOO+8EYMeOHVxwwQX069ePAQMG4L1nyJAhnHLKKQwcODDsZ0+YMIH09PSQW6jm+8oKCgpYuHAhp512WoX9p512GvPnzw/7fu89zzzzDBdddBGpqakVXhs2bBj9+vWjV2BykUpOOOEE5syZww8//ADAokWL+PDDDznzzDOrVUakvDpJAvTeTwYmB3mtZ6Xni7EJg/ZY8fHQurUFAbNmwfPPwy232IyAyi2Shuqdd94hPT195/MTTzyRt99+u0KZpk2bkpSURFpaGvvss0/I47Vp04ZRo0bx2GOPsWLFCh5++GG2bt3K448/DsAnn3zCSy+9RLdu3Xj99dcBePbZZzn00EOrPN7w4cOrbGovr127dhH3kWdnZ1NcXEzr1hVTmFq3bs2//vWvsO9///33WbZsGUOHDq2wf+rUqfz44487cyaqMmbMGLZt20aXLl2Ij4+nqKiIW265hREjRlSrjEh59WEUQL3QtCn0728BwGuvwZgxsGGDAgBpuE466SSmTJmy83nlu9rd0bFjR5o1a8b999/PlClT+Oijj3ZmlJ9wwgmUVKNZrXnz5jRv3jxsuVgNAZw6dSpHHXUUhx122M59S5Ys4eabb2bevHkkJgaf1uSll15i1qxZvPDCC3Tt2pWvv/6aa665hk6dOnHppZdGXEakPAUAUZKSAoccYs3/CxbAP/9pKwbm59trIg1NWloaBx54YNSPe9hhhzF58mTuuOMOjjvuuN0+zoQJE5gwYULIMm+//TaHH354RMdr2bIl8fHxrF1bcRqTtWvXhm3dWLduHX//+993tmYE/Oc//yE7O5uuXbvu3FdcXMxHH33Ek08+SU5ODsnJyVx//fWMHj2aAQMGAHDooYeyYsUK7rnnnp0X90jKiJSnACBKnLNhgQMGWADw3HPwpz/ZbIEKAKQxS0pKqpAJH473nq5du3LrrbfW6HOj3QWQlJRE9+7def/99+nfv//O/e+//z5/+tOfQr53xowZJCcnc8EFF1TYf84559CjR48K+y6++GIOOuggbr75ZpKSkgDIzc0lPj6+Qrn4+PgKLSKRlBEpTwFAFKWnQ+/eNu7/v/+1pYIPPNBWDqz0/1Kk0ejYsSOff/45y5cvJz09nebNmxMXZBGNxx9/nI8++ojOnTvvcjGrrtroAhg5ciQDBw7k6KOP5ne/+x1PPvkkq1evZvjw4TvLPPbYYzz22GM7k/G89zz99NMMGDCgQs4EQLNmzWhWaaKQJk2a0Lx5c37729/u3HfWWWdx77330qlTJ7p27cpXX33FxIkTGTRoULXKiJRXJ8sBN1RJSXbxD9wMBHJ6tm+vsyqJ1LnRo0eTlJREly5daNWqFStXrqyy3KJFi7j++uu58sorWbp0Kbm5uTGuaXjnn38+Dz/8MHfddReHH3448+bN46233mK//fbbWSY7O5slS5bsfD537lyWLl26S/JfdTz66KP069ePESNGcMghhzBq1CiGDh3K3XffXa0yIuU5H5i9pgHq0aOHX7BgQcTlozHdYk4OfPQRnHmmTQj0xRf22KmTdRM0NpoKOLQePXpQnd/Rhjpt7Y4dOzjmmGPo0qULTz/9NBkZGXzyyScce+yxtf7ZDfWc1pVonM/q/r9o6Kr7d9Q5t9B73yNcObUARFlqKhx0EBx/POTlwd//DoWFtiqgiFTtxhtvZMuWLTzxxBOkpaVx0EEHMWnSpKCtBSJScwoAoiwuzpIBAzlCzz0HiYmwcWPd1ktkT/Xee+/x2GOP8dxzz+2cQviWW27hww8/ZPDgwXVcO5GGS0mAtSAjA37/e2jZEhYvhm++sSGCO3YEXzhIpLE67bTTKCwsrLBv4MCBEc32JyK7Ty0AtSApySYGKh2Oy/TpkJAAW7bUbb1EREQCFADUkhYtoF8/G/731luwebNt1RgOLSIiUmsUANSS1FRo1w769LGLvoYEiojInkQBQC2Ji7M5AQITfz3/vO3bsAEa8MhLERGpJxQA1KK99oIjj7QEwOxsePttDQkUEZE9gwKAWpSUBE2aQGAmzunTbRTAhg11Wy8REREFALWseXM44wzrDvjqK/j+e5sgKD+/rmsmIiKNmeYBqGVpaTYvwPnnw1NPWSvAfffZxEBt29Z17aSutWnTZpfV4ELJz88nRctLRpXOaXRF43y2adMmSrWRUBQA1DLnrBXgvPNgyhR44w247TYoKoKCAusmkMbrjTfeqFZ5ra0QfTqn0aXzWX+oCyAGMjJsSOBpp9lFf+ZMmx9g69a6rpmIiDRWCgBiICHBZgYMTGs+c6Y9btyoiYFERKRuKACIkWbN4IgjoFs3u/D/7W+2X60AIiJSFxQAxEhKim2XXmrPp061IYEbN0JJSd3WTUREGh8FADEUFwcTJ1r//9Kl8M471gWQk1PXNRMRkcZGAUAMeG+Z/507wy+/lPX7X3EFPPYYrFun6YFFRCS2FADEwNixduefn1+xud97Gxr4179qemAREYktBQC1bNMmePDB4Bf4HTtg2jT46Se1AoiISOwoAKhlr7xiff6hxMXB3/+u6YFFRCR2FADUsqys8M37eXnWUpCdHZs6iYiIKACoZfvsY+sBhNOsmY0GUCuAiIjEggKAWtavX2Sz/W3eDImJNi+AiIhIbVMAUMsyM2H06OCtAIHFgJ57DrZsgWeegdtvt4mCNm2KXT1FRKRx0WqAMTBunD0++KAlBObmWkBQXAxDhsC8efDdd3DMMTZMsKQEmjSBv/zFgodx42xVQRERkWhRABADzsH48TBypI0KyMqy3IBzz7Um/+XLLQAoKip7T2B2wIkT7XH8+JhXW0REGjAFADGUmQlDh1bct2kTfPhh8Pfk5lrLwahRligoIiISDcoBqGP/+lf4eQLi4+Hll2NTHxERaRwUANSx9evDD/3LzbVuAxERkWhRAFDH9tkHUlNDl0lLs3IiIiLRogCgjvXrV3GBoKoUF0P//rGpj4iINA4KAOpYuHkCUlNt9IASAEVEJJoUAOwBxo2zi3xycsXx/omJcPHFcPXVdVc3ERFpmBQA7AEC8wT89BPccQccf7ztP/JIuPlmmyGwoKBOqygiIg2MAoA9SNu2MHAgPPEEZGTAZ5/BggWQkAAbNtR17UREpCFRALAHcQ5atrSugEsvtX0PP2x5AFu3wo4ddVo9ERFpQBQA7GHS08v6/ps0gblz4csvrRUgO7uuayciIg1FnQQAzrkRzrllzrl859xC59yJYcr/2Tn3tXMu1zmX5Zx7zjnXIEfGOwetWlkrwMUX274HH7RWgO3bw08aJCIiEomYBwDOufOBScAE4AhgPvC2c65DkPK/A54FZgJdgXOALsDzsahvXQi0Alx2meUC/Pvf8Omntk+tACIiEg110QIwEpjhvZ/qvV/svb8aWANcEaT8ccAq7/1D3vtl3vtPgUeBY2JU35gL5AKkpMCwYbbv/vutVSAnB/Ly6rZ+IiJS/8U0AHDOJQHdgfcqvfQecHyQt30CtHHOneVMS2AA8Fbt1bTupadbv//FF9skQJ99Bh9/DElJtn6A93VdQxERqc+cj+GVxDnXFvgVONl7/1G5/WOBC733nYO871xgBpCKLWH8PnC2936Xe2Hn3DBgGEDr1q27z549O+L6bd++nfT09IjL17biYigqgpdf3pdnnjmAzp238sgjX1JSYoFAXD1I4dzTzml9p/MZfTqn0aXzGX3VPae9evVa6L3vEa7cHh8AOOe6YBf8h4F3gTbAA8DX3vtBoT6vR48efsGCBRHXb+7cufTs2TPi8rWtpASWLbMg4IQTrP9/+nTo1cuCg06dKs4cuCfa085pfafzGX06p9Gl8xl91T2nzrmIAoBY30NmA8VA60r7WwPBFry9Cfjce/+A9/5b7/27wAhgoHOufe1Vte7FxVkugHNl0wE/8ADEx1tQsG1b3dZPRETqr5gGAN77AmAh0LvSS72x0QBVScOChvICz+tBI3jNZGTYBf+CC2xJ4EWLbIrg3/8eHn1UowJERGT31MUFdCIwxDl3mXPuEOfcJKAt8CSAc26Wc25WufJvAGc7565wzu1fOizwEeBL7/3KmNc+xgKtAMXF0KWL7Vu/Hlatgrvvhn33hdtuU1KgiIhUT0KsP9B7/5JzrgVwK9af/x1wpvd+RWmRDpXKz3DOZQBXAX8FtgAfAmNiV+u6lZEBt9wC8yu1kQSGA06caI/jx8e2XiIiUn/VSRO6936y976j9z7Ze9+9fEKg976n975npfKPeu+7eu/TvPdtvPcXeu9XxbzidWTLFnjmmeCzAObm2myBmzfHtFoiIlKPNfg+9IbglVcsDyCUuDh4+eXY1EdEROo/BQD1QFaW3eWHkpdn5URERCKhAKAe2GcfSEsLXSYlBTIzY1MfERGp/xQA1AP9+tkogFBKSmyyoJKS2NRJRETqNwUA9UBmJoweHbwVIC4Ohg+3JYM1OZCIiERCAUA9MW4cjBxpTf2BNQBSUuwxcPffpAmsW2ezBIqIiISiAKCecM7G+a9eDU8+aS0Ct90GV11lr995p00G5Bxs3Fi3dRURkT1fzCcCkprJzIShQ2HHDli+3IYHvvoqfPedDQM8/3zYtAmaNoXk5LqurYiI7KnUAlBPJSfbRR5slkCA++6DnBxITIS1azU9sIiIBKcAoB5r0cL6/88+G444wvr/H3vMcgNyc2H79rquoYiI7KkUANRjiYnQvLlNAnTnnbZvyhT45RdLCFy7NvzwQRERaZwUANRzgcl/Dj8czjnHcgPGj7fcAO+VECgiIlVTAFDPxcfD3ntbk//NN9tcAP/8J3z0kc0bsGGDBQUiIiLlKQBoADIyrDtg773hL3+xfbfdBoWFkJSkhEAREdmVAoAGIC7O1gvIy4PLL4dOneDHH2HatLKEQM0QKCIi5SkAaCDS0iA93e70x4+3fRMn2gqBgYRAzRAoIiIBCgAakFatoKAAHn3UkgNzcsoSAp2D9evruoYiIrKnUADQgCQnW3fA2rX2c0ICvP46zJ9vLQRbtlh3gIiIiAKABsJ7S/w77DBYudKa/p2z1y6/3EYCpKbafi0ZLCIiCgAaiLFjrc8/P7/sAl9YaI8bN8KFF9pIgaIizQ0gIiIKABqETZvgwQdDN+//5z/wzTeWEJidrbkBREQaOwUADcArr1iiXziBOQKSk2HNGnUFiIg0ZgoAGoCsrMiS+3780ZYOTk62FoDNm2u9aiIisodSANAA7LOPZfmHkpRkj3feaTkA6ek2LFBdASIijZMCgAagX7/IVv075hi7+I8bZyMEkpKs9UDTBIuIND4KABqAzEwYPTp4K0BqKgwbBg88YM3/L78Mc+faz/n56goQEWmMFAA0EOPGwciRNvd/XOm/apMm9vyaa2DECNh/fysDcP31tj5Akyawbp26AkREGhsFAA2Eczbt7+rV8OSTFhA89JBl+99zD7RsaYmCw4fbZEGrV8Pdd1uwEOgK0KgAEZHGI6E6hZ1zxwJnAMcCbYFUIBtYAvwbeN17vynalZTIZWbC0KG77m/eHLZutemAs7Nt37PPQq9ecPrpsH27dQU0bx7T6oqISB2JqAXAOTfYOfdfYD5wHZAGLAU+AzYBxwBPA78652Y45zrVUn1lN8XFwdSpcNRR1ioQcMkl1hKQlmajAvLzbX/PnraJiEjDFLYFwDn3LdAKmAUMAr72fte8cedcU+APwIXAIufcEO/9S1Gur+ymsWNtlcCq+vqfesqmCb72WusaaNrUgoSCAgsa+vWzlgUREWk4ImkBeAbo5L0f473/qqqLP4D3fov3/nnv/ZlYF8HmKNZTaiDcVMHFxTB5sr3+4IPQrp1NGrR8OVx3HbRtawsNabigiEjDEbYFwHs/qboH9d5/A3yzWzWSqItkquCiIhsp8PnnFVsJcnLsceJEexw/vnbqKCIisaVRAI1AJFMFew8ffQR5eVW/Hmgd0JwBIiINQ8QBgHPuHOfcdOfcZ865paXbZ6X7zqnFOkoNRTJVMIRv4o+Pt0mERESk/gsbADjnMp1z84DXgF7YsL9PS7dsoCfwmnPuE+ecUsX2QJFMFexc+OPk5lprgoiI1H+RtAD8FegAnOy97+i97+u9H1i69fXedwJOAtoBD9ZmZWX3RDJVcK9eZTMIBpOWZq0JIiJS/0USAPwRGO29/zhYAe/9PGAMcE6U6iVRFmqq4CFD4OGHISFMSmhxMfTvX9s1FRGRWIgkAEjGJvsJZzOQVKPaSK0pP1XwgQdCx44VpwpOTIQrrihbNriy1FRrRWjWLJa1FhGR2hLJVMD/AW5xzn3qvd9WVQHnXAZwEzZToOzBMjNhyZKK+7y3qYCvusqeP/po2boAqan28+DBMGpUbOsqIiK1J5IA4FpgLrDCOfdP4DvKWgQyga5AX6AYSxKUesY5aN0ali2zboKBA+HMM22VwG7dYPp0yMiAtWutyyAlpa5rLCIiNRW2C8B7vwg4DJgJHAdMAJ4s3SYAv8OmCT7ce/997VVValNCgiX45eZCmzbw3HOQnAyffQYff2x5A8nJ1oUQbkSBiIjs+SKaB8B7v8Z7f533/kCgCZbx3w5I994fUPra6tqsqNS+jAzrIsjNha5dbfpfgBtugF9+sfyAkhJrGdC0wCIi9Vu1ZwL03ueXBgRrvPdB5o2T+qplS7vbLyy00QGnnWZLCF9+uU0RnJYGK1fCQQdBp062WNAmLQAtIlLvRDIR0LnVPahzro1z7tgQr49wzi1zzuU75xY6504MUXaGc85XseVUt14SXny8dQEEpgSeOBHat4dvvoE774T774eTT7Z8AS0WJCJSf0XSAvCoc+5r59xw51zzUAWdcyc656YAPwLdgpQ5H5iE5Q8cgY0ceNs51yHIYa8B2lTafgb+L4K6y25ITYW997aRAZmZMGWKNf/PnGmrBu7YUTZKICcH8vMtUBg7tm7rLSIikYskADgImwZ4HLDWOfetc+5Z59xE59w9zrknnXPvOec2YqMFDgJ6e++nBDneSGCG936q936x9/5qYA1wRVWFS5cZzgpswAHA/sDUan1TqZbMTGvuz8uDww6zPACwroGqaLEgEZH6JZJRALne+3FAe+AiYCHQHbgEuA44C4jH7uq7eu97ee+rnA/AOZdU+t73Kr30HnB8hHUeCnwf7DMkOpyzUQElJbZUcEZG+CWFtViQiEj94Xw1Om6dcy2B7d77/N36MOfaAr9i6wp8VG7/WOBC733nMO9virUW3OS9nxSkzDBgGEDr1q27z549O+L6bd++nfT09IjLNwYlJVBQANnZsGJFPI88ciTr1jXhsMPWcdFFi3ZZRKhtW8shCNA5jS6dz+jTOY0unc/oq+457dWr10LvfY+wBb33ITfs7v4ObPKfYqAAeBVoFu69VRyrLeCBkyrtHwssieD9VwL5QPNIPq979+6+OubMmVOt8o3FunXejx/vfVqa95bqV/WWlub9lCkV36tzGl06n9GncxpdOp/RV91zCizwEVwjI8kBGF56gf4KW+3v78DZwEMRBiPlZZcGEa0r7W8NRLLQ7FDgVe/9xt34bNlNLVrA2WeHnwCouBj++Mey5z17wv/+V6tVExGR3RRJADAUmOq9P8V7P8Z73x+7E7+otE8/Yt77AiyHoHell3oTZh0B59zR2IyESv6Lsbg4+M1v4NJLbYRAVVJTYehQGxVQVGRzA6xZYyMGNFeAiMieJ5IAYH+gcmrXS1jXwH678ZkTgSHOucucc4c45yZhXQNPAjjnZjnnZlXxvmHAUu/93N34TKmhxESbA2DwYJsSuHzfv3M2adCNN1orwKhRlgvw44+WP6C5AkRE9jyRLAaUDmyttC+wKmBGdT/Qe/+Sc64FcCs2pv874Ezv/YrSIrvMB1C62uAAbCii1JEmTeDuu+Hii2HAALu7LyyE9ettYiDv4fHH7Y4/v1yaaE7plE0TJ9rj+PGxr7uIiFQUSQAA0M45t3+55/Hl9m8uX9B7/3O4g3nvJwOTg7zWs4p927BAROpYZia0awfvv2/N/j/+aP3+77wDd9xhiwjt2FH1ewNzBYwaBc2axbLWIiJSWaRrAbwCLC23/VC6//VK+5dGuX6yhwnMD+CcXegPPBCefNLmAHjmmfBN/JorQERkzxBJC8DFtV4LqVfi460VYMUKW0b4pJPgrrvgppuszz+U3FzIimS8h4iI1KqwAYD3fmYsKiL1S3KyJfatWmWzBA4aBP/8J8ybF/p9aWnWgiAiInWr2ssBiwSkp5ctGgS2UFBcmN+o4mLo37/26yYiIqEpAJAaycyEvfayTP8WLeDyyysOESwoKPsVS02FkSOVACgisidQACA14hy0bm3LBeflwS232IRBAc8/34XUVOsyuOQSmywo3IyCIiJS+xQASI3FxVk+ANi8AHfeCf/4h+3//vuWHH44fPkl3HyzzRK4erUtMiQiInVHAYBERWKijQwoKLA7/O7d4ZVXIDGxhP/8B5591sqlpVlLwdq1mhVQRKQuKQCQqElJsZaAnBy7wz/mGBgzZjHOwb332iRBYMmD27YpCBARqUsKACSq0tMtJ2D7dru4n3TSeiZMsNduvNG6BgLltmyBdesUBIiI1AUFABJ1mZk2IiAwPHDQIBgzxi70f/kLzJ1r+zMyYPNmyM5WECAiEmsKAKRWtGxpwwMDyX5XXw3DhlmS4GWXwRdf2P70dNiwwTYFASIisRPpYkAi1RIYHhgXZ0l/qakwdqw1+7/0EgwcCPvtZwHAK69YAADWclB+HgEREakdagGQWhMXZ6MD4uMtCHAO7r8f/vAHSwJctAh+/hleeMGGB2ZnqztARCRWFABIrWvXzi7++fkWDHTsaMFBSYklAY4da8MGn3xSQYCISKwoAJBal5gI7dvbBf/ee23Z4PITAeXn29LCU6bAU09Zd4CCABGR2qUAQGIiKcn6+6dOte6AquTlWStASYkFARoiKCJSexQASMy88YZ1AYTinC0rvNdeNkQwK0vTBouI1AYFABIzWVnB7/4D8vPhv/+1n4uL4fjjbbTAU0/Bpk21X0cRkcZCAYDEzD772FoA4bzyii0bfOSRsHIlrFoF111n0wzfdpu6BUREokEBgMRMv37hlwIOzBvw0kuWGBho/s/Ls9aBiRNt1ICIiNSMAgCJmcxMGD06eCtAaipccokFAcHk5sKDD1p+QCQ2bYLOnaFTJ0tAVDeCiIhRACAxNW6cNe+npJRd6NPSIDnZpgg+6CD7ORTnbPKgULy37oK2beHHH2H5cnUjiIiUpwBAYso5GD8eVq+GAw+0SYEefthmBLzySlsiOD8/9DHy82HJkrLFhqoydqx1F+Tnl3Uj5OSoG0FEJEBrAUidyMy0i3h5eXnQrJm1DoQaLZCaanfyq1ZZYmGzZhVf37TJugmCBRKBboRRo3Z9r4hIY6EWANljpKZaN0C4cf9FRfDHP9rEQllZsH59xSb9V14JP99AfDy8/HLN6ywiUl8pAJA9Sps21lefmhq8TGCVwbg4yMiAjRthzZqyEQZZWXaXH0purpUTEWmsFADIHmfCBLj2WksGLJ8omJhoj7/8Aueea3kE/frBkCHWv//LL1BYGNl8A2lpVk5EpLFSACB7HOcsCFi5Ejp0sNUE77gDvvkGPvgADjjAlhI+88yyiYJef936/pcvh759w883UFwM/fvH4MuIiOyhFADIHmvvvWHpUvjkEzjnHGja1AKC11+31QXXr7dWgFWrLEA47jh49FHYsgWuuip4K0Bams1HoARAEWnMNApA9mgJCXaxz8qCbdusz//pp2254PICff5PP20tCMOHW7Lgk09CQYElFjZpYnf+I0fafAQiIo2ZWgBkjxcfb8P+mjWzfv4nnww+xC8vzxYOArjiCvjsM9h/f5tv4KGHLFlw/HgLEkREGjO1AEi94Jxl/7/4YuipggNl33wTLrzQgoe33rI8gkgWIhIRaSzUAiD1yrZtkc0U+Pnn9nNqqo0mWLnShgtqCmAREaMAQOqVSJcUfvVVSwgsKbE8gowMSxpctcpyA0REGjsFAFKvRLKkcHy83enfe6/NEbBpk3ULZGRYQuDy5RUnCurZ0zYRkcZEAYDUK5EsKXzVVTBzpiUNfvAB9OljcwgEXk9Ksi6B7GzYsMESA1es0HLBItK4KACQeifcksLXXw+nngrvvguHH24jB845B6ZNs5aBhARbR2DsWEsO1HLBItIYKQCQeifYksI//WR3/zt2WLn27eG116wboKDALuwXX2zJgA8+CDNmWFktFywijZGGAUq9VdWSwgUFFhjk5NjEP8nJcPfdcPzx1jLw/vvw+99bU39hYdXH1XLBItIYqAVAGpSkJJsuOD0dtm4tu7vv29cu/kcdBevWBb/4B2i5YBFp6BQASIMTF2fLCrdpYy0BgS6Bdu3glVfg2GPDHyM311oSREQaKgUA0mA1bWr5Ad5bIACWAHjuudY1EEpKipXdvt2ea6igiDQ0CgCkQUtOhv32K+sSKC627oBwSkrgj3+0iYMWL7bWAA0VFJGGpE4CAOfcCOfcMudcvnNuoXPuxDDlk5xz40rfs8M5t9I595dY1Vfqt/h46w5o184WC0pOttUCU1OrLp+YCMOGQfPmtvDQEUfYCAMNFRSRhiTmowCcc+cDk4ARwLzSx7edc1289yuDvG020B4YBiwFWgNB/nyLVC0jwy7+WVkWAIBd4AsL7Y4/Pt5aCAoLYf58uOkmyxkI5BBAWVfCxIn2OH58bL+DiEi01EULwEhghvd+qvd+sff+amANcEVVhZ1zpwG/B8703r/vvV/uvf/Mez83dlWWhiIpCfbdF/be24KAzz6zLoL27eGee2DyZFtvYOFCePZZazGoSmCo4ObN0aub8gxEJJZiGgA455KA7sB7lV56Dzg+yNvOAb4ARjrnVjnnljrnHnHOpddeTaUhc86a9zt2hL32gnfegU8/teWDzz4bPvwQjj46suM891x0ugI2bdKUxCISW87HsCPTOdcW+BU42Xv/Ubn9Y4ELvfedq3jPO0BP4ANgHNAMeBT41nvfr4ryw7CuAlq3bt199uzZEddv+/btpKcrroim+nBOi4psi4uzizrA2rUwf35TXn31YNatawJAjx5r6Nv3ZzIyyiYR2HtvazFISCh7b3WtXm3dEoH/inFx9vM++1i+QXn14XzWNzqn0aXzGX3VPae9evVa6L3vEbag9z5mG9AW8MBJlfaPBZYEec97QB7QtNy+00qP0zrU53Xv3t1Xx5w5c6pVXsKrL+c0N9f7n37yfskS71et8v7++71PS/PeLsVVb2lpVu6nn7xfvNj7deu8Lyqq3ufeemvwz0lLs9fLqy/nsz7ROY0unc/oq+45BRb4CK7Jsc4ByAaKsSS+8loDWUHeswb41Xu/pdy+xaWPHaJbPWmsUlOtS6BlSxv737t3+GWHCwvhD3+wOQMyMiwf4OefYcuWshkIQ9m0yfIIyi9NXF5t5BmIiATENADw3hcAC4HelV7qDcwP8rZPgLaV+vwPLn1cEd0aSmMWF1eWG5CZCZdcEnyoIFgAcPPN8Ouv1vzfpImVz8qyIYM5OaHzA155xUYehKIpiUWkttTFKICJwBDn3GXOuUOcc5OwroEnAZxzs5xzs8qVfwHYAEx3znV1zv0OG0b4ivd+XawrLw1fcrKtJzBhgq0emJxccdnhpCRLEkxOhtdfh5NOsufnnGPlMjLswv3LL7YFG0mQlRX87j8gN9fKiYhEW8wDAO/9S8C1wK3A18AJ2BC/wN18B8o17XvvtwOnAk2x0QD/B/wbuCRmlZZGxzlbCXDSJPjqKwsI2rWDO+6Ar7+Gv/0NPvrIRg3k51srwNdf26iAoiKbTGivvawbYcUKe738fAJgSX5paaHrkZZm5UREoq1OZgL03k/23nf03id777v7ciMCvPc9vfc9K5Vf4r0/zXuf5r1v572/0nu/LeYVl0YnIQEOOQQWLYKPP7bpgTMy7LX27W3egGeftVaBwkIYMwZ69bKhhd5bK8Fee9n6AyecYEP9Cgrs/f36hc8zKC6G/v1r9zuKSOOktQBEIhBIEtxnH2vSz821RL/774fLLitbXtg5SwS89FLrEpg/35L41q+3pvxp06xFISvLcgZGjw7eCpCWZq83axab7ygijUvMpwIWqa+csxUGmzSBjRutO2DGjIpN++WT/hYssLv3wPwA3ltewfjxFjRcdRVceaXd5T/0kLUMlJTY8YuLYeRIGDcult9QRBoTtQCIVFNCgvXxT58ePMEPKl74A4FBbq4FDNOm2ToEOTkweDB8+SXsv7+1Mjz0kHUVjB+/+5MLiYiEoxYAkd0QyRC+UEMA8/IsALj8cmtVyMuDN9+0ZYubNw89/FBEJBrUAiCyGyIZwhdOYaGNMvDeLvgZGdY6sHKlbeHmERARqQkFACK7IZIhfOGUlMBTT9nIgnffteeBWQVLSmDVKli2DLZujWxmQRGR6lAAILIbIhnCF05iogURX35psw6ecgq8+KLNK5CUZIFAQoK1Nvz8M/zud7BkSXTqLyKiAEBkN2Rmhh7Cl5pqF+9Q4uJsMqE774Q2bWDpUjvmMcdYIuCGDXaM9HQLClavti6CBx6wn9U9ICI1oQBAZDeNG2dD9VJSyqYKbtLEno8aBTfeGDpAGD7cLvyXXWbzBUyaBF27Qna2LQJ09NF2/Ouvhx49LC+gsBBuv91GDPzlL7bwUE1bIkSkcdIoAJHd5JwN1Rs5Eo491sbx33yzjf1v1qzsDv3BB3cd43/JJRYAFBXZXX5SknUr/OlPFgxMmQL/+he89FLFzywudjuHHj7zjB3ruutsJMFee1nwISISCbUAiNRQZqb1zS9bBkOHls3cFwgQVq+GAw+sOMb/kUdsbYGiIkvyC0wm5Jz19c+cCf/8565DDe+669idP+fl2XwChYWwbZutObB8uR1PrQIiEo5aAERqWSBAqCwjw/r38/Ksv3/bNmsNSEmxQOD7720tgfLDDbdtS65wDO/hH/+AQYPseWFh2eqBe+1lLQOB40VLz572OHdu9I4pIrGnFgCROuSc5Qnsu6+1EDRpAtu32xwAa9fuOtPgFVd8VeF5QQHcdRfcfTf8+CMMGGAzCzZpYsdYudJGEGzYsOtqhLtj0yZrwVixAqZOteciUj8pABDZQyQn2/wCBxwALVtay0HlPv0DDthS4blzdqGfPBlOPtkWGvrhB3j6abvgZ2RYfsHGjdY9sHy5JQ4GFi+KlPdw223Qtq0FGsuXW+5B27a2XyMSROofBQAie5iEBLv4Dx8e/sKamAjPPQddutjzHTts9cE77oBDDy1LNGzSxIIB56xl4eefrXVg61Z7PZyxY2HiRBuOGJiUKCfHnk+caK+LSP2iAEBkD9WiRfi5Bq64Ar74whIQKyspgTfegEMOgRtugE8+seGK6ellsw0GJhkKFQxs2mQjGYJNfZyba69v3rzbX1VE6oCSAEX2YIHlgANDCcECguJi6+u/8EI48cTQ/fs7dsDzz9vWujWceSb84Q9w1FEWDIAdO5A8mJJiyYNpadbCEMnCR/Hx8PLLNgpCROoHtQCI7MEqDyVMSoKHH7aL9aRJZXf1oaSk2DTD++1nzf/Tp9t8Az16wC23wMcf2+ekp9tWUlLWTbB8ubUuhFv4KDe3LIAQkfpBLQAi9UBgKOHcuWXD8MCGDubnh37vjh1w5JEwaxZ8+60tO/zmm9bsP2OGbc2aQe/e0KcPPPGE3dG/+qp1CaSlWRBReURCeWlplsAoIvWHWgBE6rFIViVMSYG997a7/HHjbPGh+fPhrbfgqqts1MHmzdaEf8klsGABfP01XH01/PQTnHtu+NUIi4ttBkQRqT8UAIjUY5GsSlhSYq0Gv/5qzfSrVsELL1iXwE032YJEc+bAccdZkOC9tSq89pp1HfTsaSMKkpKqPn5qKlx6qeUR5ORoFkKR+kIBgEg9Fm5VwrQ0e/3vf4eTTrJm/1WrbEGhI4+E+++3C/7rr9tdf1XDDtevt1aBQBJiQEqKzV0wbBiMGWPdEatWlc0TsHGjdRuEaz0IpmfPit0dIhJdygEQqecqjxQov+jQyJH2WmAMf0CgP3/KFLtwP/986JEE8fE2W+Hy5WX78vNt9sLNm+Gdd+D44y0ggbJug2eftVaFlBQbehgIGsIlLgZmHCwosBkH+/UrO7aIRIcCAJF6LtSqhN7bbH3BEgXz8iw5MDEx9GckJ8OIEXDCCdZl8O9/w7x5ZbMLzpxp9fjtb6F7d9sXH2/rFPTta/XIzi5rYUhOLgsIkpJs8iOw18eOrRjMXHedLX08erQFO5Gua7CnrFmwp9RDpDIFACINRFWLDk2dGn4Mv3OhM/zBXl+3zvIGBg60rajIug3mzbNt4UL4739tCxgzxvIMTj/d1ivYe2/bX1hoXQSB7oGEBBuC+Ne/wmOPVQxYcnLsceJEexw/PnRdQS0IIpFQDoBIA5aVFX4Mf2Fh+BaAlBRbXXD7dusq8N4u2j16wLXX2mRBl122a6Kg99YV8dZbcMQR1oJw3XXwf/9n+QhpaXbhT0y0/IFJk2o242B11yzo2RP+97/Q370mtHiS7MnUAiDSgAWGCQbuoquSlhZ+cSDv4fLLLRDYts0uxoGLaVKSHf+ZZ3ZNFKxs2TLb/u//7Plee8Hhh1twsGVL+NyAuDjrshg61D63cutG+TULAoK1IAQuzjt2RL+VIJpdGSK1RS0AIg1YpMMEr7km/EiCvfe2C3a7djYrYYcONrVwfDz87W/hL96pqTa3wE03WZJifLytP/DRR3bnP2NGZF0RP/0Ev/xijz//bBfxLVtstsRI1izYtKliK0FBQfBWgt0didBQF0/SyIyGRQGASAMW6TDB+++3JMKUlLILeZMm9nzkyLKRBgGBzP6mTW10QElJ+BkJ8/Js4aKJE+3n4mI7RkKCJQ526hT++yQk2IU0N9e6DpKS7HPXrYNp08LfVcfHW/5CJBfn3W2+b6iLJ6k7o+FRACDSwI0bF/7iXnnNgY4d4aGH7A/++PHhL6xt2oSfkTAhwRIFd+wou/Dm51sy4aJFcNppwScbCigstK6GI46wroOLLoIHHrBhiEuXhm9ByMmBd98NfXF+4AG4/vrI8wgqq87iSbG0u3fv1c2rkPpDOQAiDVyoYYLNmlUsW9VIgkj062f926FUtdRwQF6edQFccokNKazqQp6YCF27WpCweLFNUPTvf9sWqXDJjmAtE488UjGfoTojESJJvIz14kk1GRVRnbyKnj3hz3+u/W4CDa2MDrUAiDQSgYv7smWWRFf54l/TY4fqakhKCn/xjYuzu8ohQypOFpSWVjYPwZtvWr7B4sXWAnDwwRbYnHmmDVEMp7AwfMJjUVHwZMZImu8jWZ+h8uJJtdW3XtO79+p0Z1ROqqytLoJIuiKUqxAZBQAiEhWhuhqOOy50CwDYHab31vWwdKl1Q7Rvb0mD8+ZZAJCbaxeYTZss8S831y6kDzxgCxyNGGHBQnXceefx1SrvHDz+uF2A1q61ugRyCAoLbanlcImX5RdPqs2+9eomI1a+cEbSnREXZ10xkSRV1kSkwYxyFSKnAEBEoiJUHsGFF0Z2V9ymjV3A9923LNv/llugWzcLBlq0sBEDPXrYH/jy6xrcc49NPDR8+K4tCElJcMEFcN99u17Qtm0Lk3hQSV6e1ct7u5hmZ9tCSytX2qiE7GxbHCk1Nfj3HD3aEihrs289GsmIkXZnvPde7Y94CBfM3HabchWqSzkAIhJVVeURRJIjEGxJYefsAp6UZBf5qVMrrlsQyBd45pmyIY0XXgjnnWd35FdeCWedBc2bW7nVq20NhMD7br75P0yYcBxgQYP34S8WTz1low7atbNgpX17+7ldO/t5wACry7RpVoeSEgsISkpg8GAYNMjq+fTTkfetQ/X6vKuTjDh0aNV5ApHMIwHBu1UCQcaoUTXrcgoEM8FGmuTmwr33WuBX01kkGxMFACJS6wI5AhMnVn1HmZZm3QehLhLhLgJ5eXbBHTfO8gG++84uZnl5FjBs327lrrjC9gcuzs2b7yAtzQKQIUMsGTHUwkjOWTCxYUPZWgjBtGhhEyfFxVkrxTHH2HDHefPgySdDXzgfeMAuwgkJ1uJQWAiPPmoLLbVsaceMiwt+kY/07n3NGrtDrmrSoiuvrPnyzuWDjN0VSTBTXBy+taOmgUhDowBARGIi3KqFlecaqCzSO9rXXrOLTXKyHT+Q7e695SEUFVlXwMiRNvQwMdHyDM44wyY6iosLPilRaqotf3zDDfb6qlXW9P/rr7atWlX2uHatBQkBn3xiW6RKSuDiiy2QKSqy+o8ZY0MUL7nEWhACwzMTEiz73jlbgCkx0S50qamhg4C0NPj8c5gzp+o75yeesO6WL7+s+jiJieGTKqMx4iGSYCacaAQiDY0CABGJieoMR6xKTYfXOWcXrMBohIwM67OfOxdOOskCkeJiCw7S0mxRosrN90OGWAtCbq4FCh072kXZOXj11YqfV1RkExStXm1bVpbdba9ZY/MhrF4d+rsUFsJXX1XcFwhKnnrK6t2zp7UGJCfb6A7vrf5nn22tDeHu3gsL4V//Ct7ikZtrkzddfbWdj8qB2ymn2DDMcFNNlx/xsDsi7YoIJdZDL2HPH66oAEBEYmp35xqIdF2D3bnYBJrTExNt1MKDD1ryYSBQufFG+H//z4KG4mK7cBYU2AV+7Vp7Pm2arXrYtGnZMZs3h1atrPm//FTJzz8Pd9yx+3e1JSU2FHLx4l1f++tfbYuPt8CloKDqnIbEROjcOfy/RVycfadPPrERDkVF1vpw9tn22m9/G/r9wXI7qiOSHJJwqvu7UdOLd31YkVKjAESkXohkXYNoXGwCys+bcPnlthZCaqpNQdysmd0RH310WRfAvfdaS8K0aXahycy0i05cnF0EcnIsD2HbNru41LRvPSkJDjggeLdIcbF9XrCExsJCW7o53AJOeXnw4ov2ff/4R0tgLCqy2Rc//hj69q049LL856WmWjfGunWWmb9smY3e+PVXuziuW2fdJJs22XnZvh1OPNG2/HxrmSgosMBr1KjgI0lSU60bJJRo/m6EUp9mTlQLgIjUC9FIJIyWULPjPfqo3V1Xzjj33u7ci4vt8dprrWxV3yUhIfy8CQUFdnEJFUgkJcELL8DGjdaiUVgIRx1lizjl5toUzP/7X9mwumAWLbItEjfccPLOn1NT4bPPrN89Pd3+jZo0sS0treKWkmLn6Kef7DuNHWt5GXvvbd9j4ED7HlWNrAj060+dWnXuRlqadWMkJdnrzln+B8AHH5TlUjhn2+bNsZk5sa4pABCReqOmiYTREMmQtKoyzp2zu/XAHfs991igUNV3iaRvPZDLECoAiI+3boILLoBPPy3r6nDOHrdsse6JUKMeEhLsvBYU2F361q0VHwPbli12N+992cIRGzfatjseeMC2wHdNS7MLfps2ZfkT7dvbtny5BRAHHmiBSuCcJCZaUHHccTZE8/nnrVxRkQUaRUWWAHrqqdZVk5hoiY/Tp5cFGddcY8HD0KH2bxofX3Yezz3XzuXf/27Pt26N7Hfjyivtd8M56N3bHqszpXW0KAAQkXqjpomE0VDd8fXBhPou3luTcSjeh+9GyM+3O95mzcpGQBQVlSU8JiTYiIJQox4uvtjmVAjcHZcPIAL7yvv223/TocPJO5v0t2+3QGbbNpskaeJEq8ORR9rFvKjIkh1//jl4S0RhoQUYW7ZU3L90qW3BBEYofPCBbVUJ5EwEEzgvjz9u/6b77WeBQlwcfPut/Tucfjrsv78FJuFaboqLrUvg5JPt33zJEjtWXeQJ1EkA4JwbAVwPtAG+B6713n8cpGxPYE4VLx3ivf+htuooInuu3U0kjIZoL/aTmWkXQqgYMITr7ujZM7IM/I4dbT6Cqnhv3RCZmfZZgZaItDR7vPJKu0Mu33VRVGSPgbKVL9rOeRIS7JiBu1zvbQbH8s33n31mjwMH2jwHobohkpIsGS8hwS7IgS03tyzIKf+8/L4dO8r2LV4c/rOC8d4SPteu3fW1r77adcRGMIWFMHu2beUF5l4YPbpshc7aFvMAwDl3PjAJGAHMK3182znXxXu/MsRbuwLlG5PW114tRUSqVhujEarKNA/X3XHdddasHUq4xLdAt8Tdd9uFZ3daVQIzJwa2X3+1u+RAzoP39l1mzqx6BseZM8MnxsXH23DF886zuRpCXRzLt0qUf9y61b7f7lz8A5KTbUXKwCRTlQVGeoT6jPj4somkyperizyBumgBGAnM8N5PLX1+tXPuDOAK4KYQ71vnvc+u9dqJiIRQk2mNqyOS7o5oJkXubqtK5W4A5yqOCti0yZZXDtYvHm4iIbD3FhfbLIrlp2quHHwEgo7AVn7fa6+F77oJZ8cO+Oab4F0vkQQX8fEWjAQrG8tZC2M6DNA5lwR0B96r9NJ7QLgluRY459Y45z5wzvWqlQqKiIQRbunjwGI/0frjHegi2G+/XZdxDrUCY6ySIsOJJGcinLQ0a+1ISrLgIiXFttTUspEF6ek2XLBpUztnLVrYJEmtWtmoh0CXQE0kJYX/LgkJVi7Y9zj55MhzSGpbrOcBaAnEA5V7UdYCwRrM1mCtA38CzgWWAB84506srUqKiIQS6wvv3LlVdxOEWoFx/PjY9COHE41pfKPRohLouqmJkpLwLRZFRTbqINjvxgknhA9EYjVrofMxnJXAOdcW+BU42Xv/Ubn9Y4ELvfedIzzOW0CR9/6PVbw2DBgG0Lp16+6zK2dahLB9+3bS09MjLi/h6ZxGl85n9NXknBYXww8/2IWhTRu786zp3W59V/l8ZmeHT7wLBCpVXY7i4uwOPtyoiHCKi635PtwlLy6u6rrGxVkLw7Ztob9LXJytEJmZWfXvRiTnI3CMli3teXV/R3v16rXQe98jbEHvfcw2IAkoAvpX2v848O9qHOd2YHG4ct27d/fVMWfOnGqVl/B0TqNL5zP6dE6jq/L53LjR+5SUqnrry7bkZO9Hj7ZycXG2r0kTe37rrd6XlESnbrfe6n1aWtV1SEvz/pZbrEywemzYEP67pKR4v2lT8DpEcj4qH6O6v6PAAh/BtTSmXQDe+wJgIdC70ku9gfnVONThWNeAiIjswSLJmbj+epv0p7a7MsJ13YwfH7pLpXnzmud/xDqHJJS6GAUwEXjWOfc58AkwHGgLPAngnJsF4L0fVPr8WmA5Nl9AEnARcA6WEyAiInu4SGdwrO35HSKdSCpUPaIxG+WeMKMl1EEA4L1/yTnXArgVmwjoO+BM7/2K0iIdKr0lCXgAaA/kYYFAX+/9WzGqsoiI1MCeMINjeTUJNKLxXfaU81EnMwF67ycDk4O81rPS8/uB+2NQLRERqUV1OYNjtEXju9T1+dBywCIiIo2QAgAREZFGSAGAiIhII6QAQEREpBFSACAiItIIKQAQERFphBQAiIiINEIKAERERBohBQAiIiKNkAIAERGRRkgBgIiISCOkAEBERKQRct77uq5DrXHOrQdWhC1YpiWQXUvVaax0TqNL5zP6dE6jS+cz+qp7Tvfz3rcKV6hBBwDV5Zxb4L3vUdf1aEh0TqNL5zP6dE6jS+cz+mrrnKoLQEREpBFSACAiItIIKQCoaEpdV6AB0jmNLp3P6NM5jS6dz+irlXOqHAAREZFGSC0AIiIijZACABERkUZIAUA5zrkOzrk3nHM5zrls59wjzrmkuq5XfeCcO8w596Jz7hfnXJ5zbolz7gbnXFylcoc65/5dWuZX59xY55yrq3rXB865lqXnyjvnWlZ6TeezmpxzFznnvnbO5Zf+P59V6XWd0wg5545yzv3LObe5dPvAOXd0pTI6nyE45yY55xaU/j4uD1Im7Dl0zv3JObfIObej9PH/hfvshCh9h3rPORcP/BPYAJwItABmAg64ug6rVl90B9YDA4GVwNHAVOx3bAKAc24v4H3gI+Ao4DfAdCAH+Gvsq1xvTAe+BtqW36nzWX3Oub8ANwHXA58CqcDB5V7XOY2Qcy4deAf7u3ks9rfyFuBd51wH7/02nc+IxGHXmkOB0yq/GMk5dM4dB7wE3A68BpwLvOyc+533/rOgn+y912aJkH2AEmDfcvsuAvKBveq6fvVxA+4HFpZ7fgWwFUgtt+9W4FdKE1K17XIOrwE+AE4BPNBS53O3z2Wz0j+avUOU0TmN/Hz2KP2d7FRuX6fSfT10Pqt9PkcDy6vYH/Ycll7836/0vn8BL4b6THUBlDkOWOy9/6XcvneBZOzuVqpvL2BTuefHAR977/PK7XsXu7PtGMN61QvOuSOAMcAgLDitTOezek4D4oHWpU2kvzrn/uac279cGZ3TyC3BWv0udc4lO+eSgaFYC+D3pWV0PmsuknN4HPBepfe9Cxwf6sAKAMrsA6yttC8bKC59TarBOXckMAR4otzuqs7x2nKvSSnnXBNgNnC19/7XIMV0Pqtnf+xv3q3ASOD/AYnAHOdcWmkZndMIee+3AT2B84Dc0u18rIUlcLHS+ay5SM5hsDIhz7ECAIk651xnrF/wYe/9q3Vdn3rqEWCezl9UxWEX/L9479/x3n8OXAjsDZxVpzWrh5xzqcA0LJfiWOB3wFfA30sDWNnDKQAokwW0rrSvJdZkmBX76tRPzrnfAHOB2d77Gyu9XNU5bl3uNSnze2CIc67IOVeE5QEAZDnn7g78jM5ndawpfVwU2OG93wKsBjqU7tI5jdyfgQOAi733X3jvPy3d1wFrXQGdz2iI5BwGKxPyHCsAKPMf4BDnXPty+3oDO4CFdVOl+sU51wW7+L/svb+uiiL/AU50zqWU29cb+wO8vNYrWL+cBhwGHF66XVa6vyfWOgA6n9X1Selj58CO0kz2NpQtG65zGrk0LOGvfH5KSem+wLVF57PmIjmH/yndR6Uy80Meua4zH/eUDbvT/y/wIXAEcCqWZfloXdetPmxAV6zPaTbW77RzK1emKRaRzgZ+iw1V2QqMquv67+kbduGvPApA57P65/F14DusuboL8HLpH9E0ndNqn8vfYKOkngAOKf0b8CywBWiv8xnxeTwQC/Inll7UDy/dkiI9h1iyXxFwY+m/y01AIXBMyM+u6y+/J21Y09WbWDLLBuxOK7mu61UfNuCO0gvULlulcodi41nzsSbZ29FwoEjO7y4BgM7nbp3HDGx+io3YCJU3gAN0Tnf7fPYG5gGbS8/nHOB4nc9qncO5Qf52dqzOOQT6AT8ABcBi4Nxwn63FgERERBoh5QCIiIg0QgoAREREGiEFACIiIo2QAgAREZFGSAGAiIhII6QAQEREpBFSACBSTznnhjjnfLktxzm3vHSFu/Occ243j9uz9Hg9o1vjkJ9Z4bvU0mfcWu4zVtXGZ4jUJwoAROq//thyoGcCt2HTV78IvF+6YEt9ci72XWrD9NJjv1VLxxepVxLqugIiUmNfe+9/LPf8Wefcy9g0t/cDV9dNtXbLV9775bVxYG/LKv/qnFtfG8cXqW/UAiDSAHlbRvjvwNBya93jnEtzzt3nnFvmnCsofbzFORfyb4Fz7jTn3FvOuTXOuVzn3HfOuVHOufhyZd5wzn1VxXs7OedKnHPDq/s9nHMdS5vsh1Tav0s3hXPudOfcfOfcFufcdufcEufc2Op+pkhjoQBApOF6C0gGegA45xKAd7GVBScBfYCnsW6DB8Ica39sSeJLgL7ATGz9h7vLlXkCONw5d3Sl9w4DcoDnd/+rhOac2x/4B7AMOB/4I7a4italFwlCXQAiDdfK0sc2pY8XACcAJ3vvPyrd90FpruDtzrn7vPfrqjqQ9/7JwM+lyYUfA0nAaOfczd77EuAd4GfgcuDz0rKJwMXA8977bdH8cpUcWVqfK7z3W0v3fViLnydS76kFQKThCowCCGTVn4Gtez/fOZcQ2ID3gETg2KAHcq6Nc+4p59wKbLWxQuAuoBmwN0BpEPAUMMA517T0recArUv316avS+s02znXzzm3dy1/nki9pwBApOHat/RxTenj3sB+2IWy/PZ56estqjpIaX7AP4A/YBf9U4CjKGv+TylX/BkgHhhY+nw48Ln3fpfcgGgqTYI8Hfub9iyQ5Zz71Dl3cm1+rkh9pi4AkYarL7Z++MLS5xuwPvLzgpRfHmT/AVgewUDv/XOBnc65syoX9N5vcM79H3C5c+5doBeWc1BTlf9WpVfx2XOAOc65ZOB3wDjgn865jt777CjUQaRBUQAg0gA55/6EJcJN8t7nlu5+B/gTsN17/0M1DhcYRVBY7viJwIVByk8G/oMlGG4BZlfjs4L5baXnQbsrvPc7gA+dc+nYSIhOgAIAkUoUAIjUf4c751piSXAdsKb6/sD7wE3lyj2PJeR94Jz7K/BN6XsOwIKFc8oFC+UtxnIH7nbOFWOBwHXBKuO9/7R0OOBJwKNBjlldlznnfgG+wlojrirdf7pzbiVwWunnvQX8ArTEvvtq4LsofL5Ig6MAQKT+e7n0MR9YB3wJDABe8d7vnFbXe1/onDsduBEbmtcJG573E/BPLLlvF977AufcOcBjwCxgIzANG2UwNUSdjiB6yX8PA/2ACcCPWHLhBOAK4F9YMNMHuAfLddgIzAMu9N7nRakOIg2KK/f3QUQkKpxznwAl3vsTIyw/BJuq90Bghfe+qHR/Ryxv4WLv/Ywa1slhCYrPAL/33revyfFE6ju1AIhIVJQm3x0JnAocD5y9G4cJTGm8WwsZhXELML70519r4fgi9YoCABGJljbAfGAzMMF7/49qvPcNbGhhbXoGS4SEIN0dIo2JugBEREQaIU0EJCIi0ggpABAREWmEFACIiIg0QgoAREREGiEFACIiIo2QAgAREZFG6P8DkpioCxRXJKQAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"dt_factor = apply_prefix(1, unit)\n",
"\n",
@@ -165,92 +166,29 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DbAnalysisResultV1\n",
- "- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527131e-03 3.03978270e-03 5.77293057e-07]\n",
- "- χ²: 0.7488240853426228\n",
- "- quality: good\n",
- "- extra: <4 items>\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n",
- "DbAnalysisResultV1\n",
- "- name: T2\n",
- "- value: 1.9828300679956625e-05 ± 5.772930568055365e-07 s\n",
- "- χ²: 0.7488240853426228\n",
- "- quality: good\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Print results\n",
"for result in expdata1.analysis_results():\n",
" print(result)"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Additional fitter result data is stored in the `result.extra` field"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{}"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "expdata1.analysis_results(\"T2\").extra"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameters $A$ and $B$ is $0.5$.In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar values computed for other qubits."
+ "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameters $A$ and $B$ is $0.5$.In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "from qiskit_experiments.library.characterization import T2RamseyAnalysis\n",
- "user_p0 = {\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5}\n",
- "\n",
"exp_with_p0 = T2Hahn(qubit, delays)\n",
"exp_with_p0.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
"expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n",
@@ -262,31 +200,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DbAnalysisResultV1\n",
- "- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.78978431e-01 5.02409209e-01 2.01192655e-05] ± [5.09032092e-03 3.07792331e-03 5.78387141e-07]\n",
- "- χ²: 0.5509343846546172\n",
- "- quality: good\n",
- "- extra: <4 items>\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n",
- "DbAnalysisResultV1\n",
- "- name: T2\n",
- "- value: 2.011926549231594e-05 ± 5.783871411742618e-07 s\n",
- "- χ²: 0.5509343846546172\n",
- "- quality: good\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Print results\n",
"for result in expdata_with_p0.analysis_results():\n",
@@ -298,42 +214,15 @@
"metadata": {},
"source": [
"### Number of echoes\n",
- "The user can provide the number of echoes that the circuit will preform. This will translate to more delay gates and more echo gate. As the number of echoes is greater, the total time of the circuit will grow. This let us estimate $T_{2}$ better as we have more samples and the echoes are canceling the $T_{1}$ noise effect on the qubit.\n",
- "Note that the delay time providedis the for each delay in the circuit and not the total time."
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increase, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, Hahn Echo experiment let us estimate $T_{2}$ better.\n",
+ "Note, that the delay time provided is the for each delay in the circuit and not the total time."
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
- "q_0: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
- " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
- "c: 1/══════════════════════════════════════════════════════════»\n",
- " »\n",
- "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
- "«q_0: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
- "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
- "«c: 1/══════════════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
- "«q_0: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
- "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
- "«c: 1/════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌─────────────────┐┌──────────┐┌─┐\n",
- "«q_0: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └─────────────────┘└──────────┘└╥┘\n",
- "«c: 1/════════════════════════════════╩═\n",
- "« 0 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"# set the computation units to microseconds\n",
@@ -342,7 +231,6 @@
"\n",
"# set the desired delays\n",
"conversion_factor = 1e-6\n",
- "# delays2 = list(range(1, 25000, 100) )\n",
"\n",
"delays2 = np.append(\n",
" (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
@@ -352,29 +240,18 @@
"num_echoes = 4\n",
"\n",
"\n",
- "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "# Create a T2Hahn experiment. Print the first circuit as an example\n",
"exp2 = T2Hahn(qubit2, delays2, num_echoes=num_echoes)\n",
"print(exp2.circuits()[0])\n"
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {
- "scrolled": true
+ "scrolled": false
},
- "outputs": [
- {
- "data": {
- "image/png": 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cT69ltKCgYcIFBdbQMM46dnSPZWWuK6IxxhjTWFhQEGedOrnHDRssKDDGGNO4WFAQZ/6g4IUX4JxzUpsXY0xtw4cPjzrRkjHZzIKCOPPfPigvh7VrYdYsN5OiMcYYk+4sKIgjVXj1Vff3nj2wbh2MHg0dOsD997vtxhhjTLpKSVAgIjeJyCoR2SMiS0Uk4rybInKViHwiIhUislFEnhORdkFpLhWRZSKy1/f434ktRV3jx8Nzz9Vet2uXCxCmTHHbjTHGmHSV9KBARIYB04BJQB/gPeBVEekUJv0pwFzgWaAncDHQA3g+IE1/YL5v3fG+xwUiEnky8Djatg0mT4bdu0Nvr6hw233jjBhjkuCbb75h0KBB9OjRg969e7NgwYJUZ8mYtJaKmoIxwGxVnaWqX6jqrUAZMCpM+v7AOlX9paquUtX3gSeBwAv+HUCpqj7sO+bDwCLf+qR48UUIM0jcAbm5YN9JxiRPkyZNmDp1KsuWLeO1117jjjvuYNeuXanOljFpK6nDHItIHtAXmBy06TVgQJjd3gUmichFwF+A1sAVwCsBafrjAoVAC4FbwuRjBDAC3FjrixYt8pT/8vLysGmLiuBnP3N/P/NMLz7/vA3XXvs5xx23uU46jy+XMpHKmUkaSzn37NnDzp0767VvVVVVvfdNtpEjR/Ldd9/V69d8uHL6hxfeuXMnzZs3p1WrVqxdu5aO/hbBjUxjOp/15bWMe/bsaRT/v+Gk7fdPqBGNErUAHXBDZp4etH48sDzCfpcA3wP7ffu/BjQL2L4PuC5on+uAvdHyFK8RDWfOVG3e3I1mGG5p3tylS3epHmkrWRpLOTNlRMPrr78+5PC5H3/8saqqbt++Xbdt26aqbujdm2++2fOxvZRzyZIl2rNnz/pkvUHefPNNveiii7RDhw51hncOZ9KkSdqvXz9t0aKFtmnTRi+88EL97LPPwpZz0qRJCtR6z0INL11SUhKvYiWMjWiYHDTWEQ1FpAeuFmAirpbhXKAd8OtU5ivY0KHRByuqqnIzKRqTrc466yzKyspqLf5JdA4++OBaEyDF09atW7nuuuuYOXNmQo4fSXl5Ob169WLatGk0a9bM0z6LFi3ipptu4r333uMf//gHTZo04ayzzqo18ZLf+++/z8yZM+ndu3edbUcffXSt9zp4dkNjgiU7KNgCVAElQetLgI11kwNwD/Chqj6mqp+q6kLgJuBaEfHXAW6M8ZhxV1wMY8dCYWHo7YWFbrvNnGiyWX5+/oFZ/PyLf0rn4cOHc+GFFzJ8+HDefPNNpk+fjoggIqxevTrk8YYNG0br1q2ZPn36gXVffPEFhYWFzJs3D4C9e/dy8cUXc/fddzNgQLi7lIlz/vnnM2nSJIYOHUpOjrev3IULF3LDDTfQq1cvjj32WObOncvmzZv54IMPaqXbsWMHV199Nb/73e8oLi6uc5wmTZrUeq/btm0blzKZzJXUoEBV9wFLgeBJ0ofgeiGEUogLJAL5n/vzvzjGYybEhAkwZgzk5dWsKyyEggK3fsKEZObGmMZp2rRp9O/fnxtuuOHAL9zDDjssZNqpU6dy1VVX8Ytf/AJwAcCVV17J0KFDueKKK1BVhg8fzhlnnMG1114b9bUnTZp0oB1CuOXtt9+Oa3m92LlzJ9XV1XVqUkaMGMHQoUMZPHhwyP1WrlxJhw4dOPzww7niiitYuXJlEnJrGrOkNjT0mQLMFZEPcY0IR+LaGjwNICJzAFT1Ol/6l4FZIjIK13iwPTAV+KeqrvWlmQa8JSJ3Ay8B/w0MBk5NQnkOEIGJE2HYMDj2WMjJgXHjYNQoqyEwBuBvf/sbRUVFB56fdtppvOof8cvn4IMPJi8vj8LCQtq1axd8iFrat2/PnXfeyVNPPcWaNWuYOnUq33///YGag3fffZf58+fTu3dvXnrpJQDmzp3LscceG/J4I0eO5PLLL4/4moceemi0Ysbd7bffzvHHH8+JJ554YN2sWbP4+uuveS54cBSfk046idmzZ9O9e3c2bdrEQw89xIABA/j8889p3bp1srJuGpmkBwWqOl9EWgPjcBf4fwPnq+oaX5JOQelni0gLXE+Cx4EdwD+AnwakeU9ErgAeAiYA/wGGqWrturYk6d7d1Rbs2wfnnWcBgTF+p59+eq37+l7vsUfSpUsXWrZsyaOPPsrMmTN56623aNGiBQCnnnoq1dXVno/VqlUrWrVq1eA8xdOYMWN45513eOedd8j19Xtevnw59957L++88w5NmzYNud95551X6/nJJ59M165defbZZxkzZkzC820ap1TUFKCqM4AZYbYNCrHuSep2OQxO8yLwYjzy11BNmkD79rBmjZst8dhjo49hYEw2KCws5Mgjj4z7cXv16sWMGTN48MEH6d+/f72PM2nSJCZNmhQxzauvvsppp0UchDVuRo8ezbx58ygtLaVr164HuuotXryYLVu20LNnzwNpq6qqeOutt3j66afZtWsX+fn5tY5VVFREz549WbFiRVLybhqnlAQF2aBjx5qgoKrKggJjYpGXl0dVDHOPqyo9e/Zk3LhxDXrddLp9cPvttzN//nxKS0vp3r17rW0XX3wx/fr1q7XuhhtuoFu3btx7773kBTZs8tmzZw9ffvll2PYHxoAFBQnjbxflDwqMMd516dKFDz/8kNWrV1NUVESrVq3CttyfPn067777LkcfffSB6vX6SsTtg/Lycr7++msAqqurWbt2LZ988gmtWrWik2+u9aeeeoqnnnqKL7/8EoCbb76ZuXPn8tJLL1FcXMzGja4jlarSokULWrZsWafRoX9wJn8Xz7Fjx3LRRRfRqVMnNm3axMSJE9m1axfXX399XMtnMkvaj1PQWHXu7B7LyiwoMCZWY8eOJS8vjx49etC2bVvWrl0bMt2yZcu46667+PGPf8yKFSuoqKhIck6jW7JkCX369KFPnz7s3r2bBx54gD59+jA+YIa0LVu2sHz58gPPZ8yYwc6dOznzzDNp3779geWJJ57w/Lrr1q3jyiuv5Oijj+aSSy4hPz+f999/n87+LydjQrCaggTx/99t2AD796c2L8akg9mzZ3veftRRR7F48eKI6ffu3ctVV13FxRdfzMSJE/ntb3/Lp59+ysknnxyH3MbPoEGD/KOshvXggw/y4IMPHngeLn2k4X+Dh8z1j9NgTCyspiBBunRxj2VlsHdvSrNiTEa6++672bFjB7/61a8oLCykW7duTJs2LWytgjEmOgsKEiSwpsCCAmPi67XXXuOpp57iueee4+CDDwbgvvvu4x//+IfdMzemAez2QYL4awo2boQ0vM1pTKN29tlnsz/ovty1117radRCY0x4VlOQIIWF0Lata2T47bfW2NAYY0z6s6AggfzdmTdsgMrK1ObFGGOMicaCggQKHKvAggJjjDHpzoKCBPKNS2JBgTHGmEbBgoIECmxsuGdPSrNijDHGRGVBQQL5uyWuX29BgTHGmPRnQUEC+SeDW7vWRjU0xhiT/mycggTq1g1E4Jtv3ABGNluiqY/27dvXmRHPqz179lBQUBDnHKUfK2fm8FrG9u3bJyE32ceCggRq3hzatXNDHa9f72oOLCgwsXr55Zfrve+iRYsYNGhQ/DKTpqycmSMbypjO7PZBAonUNDZcs8YGMDLGGJPeLChIsK5d3aO1KzDGGJPuLChIsMDGhjYxkjHGmHRmQUGC+ac0t6DAGGNMurOgIMEKC93j6tUWFBhjjElvFhQk0LZtsGOH+3vtWti82RobGmOMSV8pCQpE5CYRWSUie0RkqYicFiHtbBHREMuugDSDwqTpnpwS1aYK998PHTq4GgKA6moYNMitV01FrowxxpjIkh4UiMgwYBowCegDvAe8KiKdwuxyO9A+aFkJ/CFE2p5B6VbENfMejR8PU6a4oY2rq2vW79sH06a57cYYY0y6SUVNwRhgtqrOUtUvVPVWoAwYFSqxqu5Q1Y3+BTgC6ArMCpF8U2BaVU16Zf22bTB5MlRUhN5eUeG2b9+e1GwZY4wxUYkmsS5bRPKACuBKVV0QsH460EtVB3o4xmygn6r2Clg3CCgF1gD5wDLgIVUtDXOMEcAIgJKSkr7z/F0EoigvL6eoqChimi1b3LDGgTUEpaWH8de/HsGpp67j4ou/JicHDjsM2rTx9LJJ56WcmSAbypkNZQQrZybJhjJC6ss5ePDgpapad/x0VU3aAnQAFDg9aP14YLmH/Q/GBRW3B60/GhgJ9AX6AzOAauC0aMfs27evelVaWho1zYQJqiKqruVA6EXEpUtXXsqZCbKhnNlQRlUrZybJhjKqpr6cwBINcU1sbHMfXIO75TE3cKWqLgeWB6xaLCJdgLuAt5OWO9xcB4WFsGtX+DSFhS6dMcYYk06S3aZgC1AFlAStLwE2etj/x8AfVXWrh7QfAN1iy17DDR0avdthVRVcdlly8mOMMcZ4ldSgQFX3AUuBIUGbhuB6IYQlIicCxxG6gWEox+MaMCZVcTGMHVszaFGw/HwYPRpatkxqtowxxpioUnH7YAowV0Q+BN7FtQXoADwNICJzAFT1uqD9RgArVHVR8AFF5A5gNfA5kIe7zXAxcGkC8h/VhAnucfJk1w2xuhpyctzjWWfBuHGpyJUxxhgTWdKDAlWdLyKtgXG4sQT+DZyvqmt8SeqMVyAiLYArgAlhDpsHPAZ0BHbjgoMLVPWVOGffExGYOBHGjIGTT3aBQdu28NFHcMIJNqqhMcaY9JSShoaqOgPXQyDUtkEh1u0EwvbdUNVHgUfjlb94KS6G5b7mj/ff74KCb75xcyC0aJHavBljjDHBbO6DJDnqKPf4v/9rEyMZY4xJTxYUJIk/KNi71w1/bIwxxqQbCwqSpG1b97hnD8yd60Y+NMYYY9KJBQUJ5p8xsWfPmnWPPOKGObYZE40xxqSTxjaiYaMTOGOin79NwZQp7nHixOTnyxhjjAlmNQUJZDMmGmOMaUwsKEigF1+E3NzIaXJyYMGCyGmMMcaYZLCgIIE2bgxfS+BXUQHPP29tC4wxxqSeBQUJ5J8xMZrFi13bA2OMMSaVLChIIC8zJoIbBtnaFhhjjEk1CwoSyD9jYl5e9LS5uda2wBhjTGpZUJBgEyZA//7R01VUuDYIxhhjTKpYUJBgInD11dHbFhQWujYIxhhjTKpYUJAEQ4dCdXXkNFVVcNllycmPMcYYE4oFBUngb1sQrragsNBtb9kyqdkyxhhjarFhjpNkwgT3+NhjNcMcFxS4xzFjarYbY4wxqWI1BUki4uY4WL0amjZ16667DlascOtFUpo9Y4wxxoKCZDvkEDjnHPd3ly7QrFlKs2OMMcYcYEFBkuXkQPfu7u+vv649e6IxxhiTShYUpEDPnu5xxQrYvTu1eTHGGGP8LChIgeOOc49ffeW6KnoZCtkYY4xJtJQEBSJyk4isEpE9IrJURE6LkHa2iGiIZVdQuoG+Y+0RkZUiMjLxJamfbt1cW4Jvv4UrroDKylTnyBhjjElBUCAiw4BpwCSgD/Ae8KqIdAqzy+1A+6BlJfCHgGMeDrziO1Yf4OfAkyJyaYKK0SB5eXDEEe7v3bth//7U5scYY4yB1NQUjAFmq+osVf1CVW8FyoBRoRKr6g5V3ehfgCOArsCsgGQjgQ2qeqvvmLOAZ4GxiS1K/TRt6noeAJSVwaxZsG1bSrNkjDHGJDcoEJE8oC/wWtCm14ABHg/zY+BzVX0vYF3/EMdcCPQTkab1yWuiqML48fDqq+75jh0wbhx06AD33++2G2OMMakgmsSrkIh0ANYDA1X1rYD144GrVfXoKPsfjKtVuEdVpwWs/wp4TlUnBKw7HXgT6KCqZUHHGQGMACgpKek7b948T/kvLy+nqKjIU9pwNmxwbQmWLWvFb37Tm65dt3PTTZ8ArrtiSYkLEFIpHuVsDLKhnNlQRrByZpJsKCOkvpyDBw9eqqr96mxQ1aQtQAdAgdOD1o8HlnvY/2ZgD9AqaP1XwPigdaf7Xqt9pGP27dtXvSotLfWcNpStW1ULClRdfUDopaBAddu2Br1MgzW0nI1FNpQzG8qoauXMJNlQRtXUlxNYoiGuicluU7AFqAJKgtaXABs97P9j4I+qujVo/cYwx6z0vWZaePFFyM2NnCY3FxYsSE5+jDHGmEBJDQpUdR+wFBgStGkIrudAWCJyInActRsY+i0Oc8wlqpo2bfs3boSKishpKipcOmOMMSbZUtH7YAowXERuFJFjRGQa7rbC0wAiMkdE5oTYbwSwQlUXhdj2NHCoiEz1HfNGYDgwOSElqKd27cJPn+zXrJlLZ4wxxiRb0oMCVZ0P3AGMAz4BTgXOV9U1viSdfMsBItICuAL4TZhjrgLOx7Uj+AS4D7hNVf8Y9wI0wNCh0UcvrKhwIx1aLwRjjDHJ1iQVL6qqM4AZYbYNCrFuJxCxmaaqvgmcEI/8JUpxMYwdC1OmRL6NMGMGFBS4KZWNMcaYZLG5D5JswgQYFXKYphoVFTB5MmzfnpQsGWOMMYAFBUknAkcfHb1tgfVCMMYYk2wWFKTAxo3Rp0y2XgjGGGOSzYKCFPDSC6Gw0HohGGOMSS4LClLASy+Eqiq47LLk5McYY4wBCwpSwt8LIVxtQWGh296yZVKzZYwxJsulpEuicb0QwPUy2LPH/Z2f7xoijhlTs90YY4xJFqspSBERNw7BqlU1NQYXXggff+zWi6Q2f8YYY7KPBQUpdsghNeMWFBVBXl5q82OMMSZ7WVCQYjk5cPzx7u9PP4XKyuiNEI0xxphEsKAgDZx4ontctgz274d9+1KbH2OMMdnJgoI00KEDdO4Me/fCsGFw1lmpzpExxphsZEFBGmjaFHr1cn/v2gXV1anNjzHGmOxkQUEayMuDY491f3/3HXzzDcyaBdu2pTZfxhhjsosFBWniq6/c486dsH49jB7tbivcfz+opjZvxhhjsoMNXpQGxo+HV16pvW7XLvc4ZYp7nDgxuXkyxhiTfaymIMW2bas9qmGwigq3ffv2pGbLGGNMFoopKBCRk0XkQRH5m4h8KiIrRGSxiMwWkRtEpDhRGc1UL74IubmR0+TmwoIFycmPMcaY7OUpKBCR60XkM+A9YDRQCKwAPgC2AScBvwHW+wKEwxOU34yzcaOrDYikosKlM8YYYxIpapsCEfkUaAvMAa4DPlGt2/RNRA4GLgSuBpaJyHBVnR/n/Gacdu3c3Af+NgShFBa6dMYYY0wieakp+C1wuKr+VFU/DhUQAKjqDlV9XlXPB04Gtscxnxlr6NDowxpXVcFllyUnP8YYY7JX1KBAVaepaphmcGH3+ZeqLqx/trJHcTGMHVszU2KwwkK3vWXLpGbLGGNMFkpJ7wMRuUlEVonIHhFZKiKnRUmfJyITfPvsFZG1InJbwPbhIqIhloLEl6bhJkyAMWOgICC3TZpAfr5bP2FC6vJmjDEme3gep0BELgZ+CPQAWvlWbwWWAf+nqi95PM4wYBpwE/CO7/FVEemhqmvD7DYP6AiMwDVwLAGaBaWpAI4IXBFrDUeqiLhxCMaMge7dYdMmOOwwmD8fevd2240xxphE89LQsBh4GRgArAU+B3zj79EKGARcLyKLgQtVNdrgvGOA2ao6y/f8VhE5FxgF3BPi9c8GzgSOUNUtvtWrQxxXVbVRt9EvLoZOnVxQsGGDqznYu9fVGBhjjDGJ5uX2weNAJ2CgqnZR1QtU9VrfcoGqHg6cDhwKTI50IBHJA/oCrwVteg0XdIRyMfARMEZE1vnGRnhCRIqC0jUTkTW+NH8RkT4eypZ23ngDjj7aBQPLlkXulWCMMcbEk4TpTFCTQGQLcJOq/iFKumHADFVtHSFNB2A9LsB4K2D9eOBqVT06xD5/w9VG/B2YALQEngQ+VdWhvjT9gaOAfwEtgNuB84HjVHVFiGOOwN2KoKSkpO+8efMiFe2A8vJyioqCY5H4UoWpU4/kz3/uyPXXr+LKK9ckvaYgGeVMB9lQzmwoI1g5M0k2lBFSX87BgwcvVdV+dTaoasQF2AkM8ZDuHGBnlDQdAAVOD1o/HlgeZp/XgN3AwQHrzvYdpyTMPrnAZ8AT0fLdt29f9aq0tNRz2vqqrFSdOlUVVE87TXX5ctW9exP+srUko5zpIBvKmQ1lVLVyZpJsKKNq6ssJLNEQ10Qvtw8WA/eJSItwCXzb7sGNeBjJFqAK11AwUAkQrj1AGbBeVXcErPvC99gp1A6qWgUsAbpFyU/ayc2F/v3d30uWwP79sG9favNkjDEmO3jpfXAHsAhYIyJ/Bf6NG9oYoBjoCVyAu9gPjnQgVd0nIkuBIUDgaP5DgD+G2e1d4DIRKVLVct+6o3yPa0LtICIC9MbdTmh0unZ1y8qV8OWX0KYNZEFtmjHGmBSLGhSo6jIROQ74CXARcBXg7ySnuJ4Ac4DHVHWDh9ecAswVkQ9xF/yRuNsKTwOIyBzf617nS/974H7gGRF5ENemYBrwoqpu8u3zAPA+rrviQcBtuKBglIf8pJ3CQujb1wUF//wn9GmUTSaNMcY0Np4GL1LVMlUdrapHAs1xPQ0OBYpU9QjfNi8BAermQ7gDGAd8ApwKnK+q/l/9nQi4LeCrHTgLOBjXC+EPwJvAjwIO2xKYibut8Jovb6er6ode8pRu8vLgBz9wf3/wgbuFsH9/avNkjDEm83kevMhP3YBAZQ15UVWdAcwIs21QiHXLcY0Lwx1vNG72xozQpAkM8HXQ/OgjqK52XRSbNk1tvowxxmS2qDUFInJJrAcVkfYicnL9smTAjVXQsSN8/z385z+wc2eqc2SMMSbTebl98KSIfCIiI0WkVaSEInKaiMwEvsbd0zf15G9XAK5dQXm5G8PAGGOMSRQvtw+6AWNxAwc9KSJf4Fr1bwb24nogdAX64e77v4Ub1yBa90QTwXnnwVrfTBAffABXXAEDB0JODixalNKsGWOMyVBeeh9UABNE5BHgv4FzgZNwPQYKgO+AL3E9Auar6peJy272EIGDDnJ/v/++e6yqckGBMcYYkwieGxr6xhj4O25GxEYx+2BjtW0blJVBRQW0aAFbt8Knn7pJkgBmzYKhQ90ESsYYY0y8eGlomCsiD4rINuBb4HsR+aOItEx47rKMKtx/P3ToAF9/DevWucAA4PLL3e2E1ath9GiX5v77rZ2BMcaY+PFSUzASNzfBItw4AV1xtxG+B25IWM6y0PjxMGUK7Amoh6mqco9uNgT3t3/mxClT3OPEicnLozHGmMzl5Q71j4FZqnqGqv5UVS8Dbgau8U2FbOJg2zaYPLmmZsCLigq3z/btCcuWMcaYLOIlKOhK7XkKAObjZiLsHPccZakXX3STIcUqNxcWBJ8dY4wxph68BAVFuFsFgfxD6YSdOdHEZuPG2GoJ/Coq3L7GGGNMQ3ntfXCoiHQNeJ4bsH57YEJVXRmPjGWbdu3cgEX+9gJeFRa6fY0xxpiG8hoUvBhm/Ush1tWjEtwMHQq33Rb7flVVcNll8c+PMcaY7OMlKLAeBklQXAxjx8Ljj8Pu3d72KSyEMWOgZcuEZs0YY0yW8DKi4bPJyIiBCRPcPAevvBI9bdOmLiCYMCHx+TLGGJMdbNDcNCICJ5/sHqMZOdKNT+AlrTHGGOOFBQVpxt/gMJK8PDjkEKiuTk6ejDHGZAcLCtLM0KE1oxiGU1kJ55zjve2BMcYY44UFBWnG3+AwUm2BKtx0E3wfPHqEMcYY0wAWFKShCRNcI8KCgpqpkps3h/x8FzSoQnm5W+wWgjHGmHixoCANibhGhBs2wJFHQpcu8MtfupkTL7nEpVm7FubNq5lO2RhjjGkoCwrSWHExtG8PnTvDjTfCr34Fc+a4bRUVMGmSCxpsCmVjjDHxYEFBmlu0yC3jx8PUqbB/f822PXtg7143hXKXLjBoUEqyaIwxJkOkJCgQkZtEZJWI7BGRpSJyWpT0eSIywbfPXhFZKyK3BaW5VESW+bYvE5H/TmwpkifatMoVFbBuneuVYIwxxtRX0oMCERkGTAMmAX2A94BXRaRThN3mAecCI4CjgcuATwOO2R83nfPzwPG+xwUiclICipB0XqZVVoUvv4RZs1wQYYwxxsQqFTUFY4DZqjpLVb9Q1VuBMmBUqMQicjZwJnC+qr6uqqtV9QNVXRSQ7A6gVFUf9h3zYWCRb32j52VaZVX47jsYPRo6dLB2BsYYY2InmsQrh4jkARXAlaq6IGD9dKCXqg4Msc8M4CjgQ+A6YDfwKnCvqpb70qwFnlTVxwL2uwu4RVU7hzjmCFytAyUlJX3nzZvnKf/l5eUUFRV5LG38bNkC33xTu/vhO+8cyksvdeOYY7bwP//z7zr75ORASYkLEGKVqnImWzaUMxvKCFbOTJINZYTUl3Pw4MFLVbVfnQ2qmrQF6AAocHrQ+vHA8jD7/A3YA/wVOAk4B/gKeDEgzT7guqD9rgP2RstT37591avS0lLPaeNp61bVggJV99vf+1JQoLptW+yvl6pyJls2lDMbyqhq5cwk2VBG1dSXE1iiIa6JjaH3QQ4ukLhK3W2DhcAtwKUiUpLarCWHl1EOQ8nNhQULoqczxhhjIPltCrYAVUDwxbwE2BhmnzJgvaruCFj3he/R3zhxY4zHbHT8oxzm53vfp6LCtUcwxhhjvEhqUKCq+4ClwJCgTUNwvRBCeRfoICKBN1+O8j2u8T0ujvGYjY5/lMNbbvE+XXJhoZt10RhjjPEiFbcPpgDDReRGETlGRKbh2ho8DSAic0RkTkD63wPfAc+ISE8ROQXXpfFFVd3kSzMNOENE7haR7iJyDzAYmJqkMiXFtm0wfbr3XgW7d8Nll9U8HzTIBjgyxhgTXpNkv6CqzheR1sA4oD3wb1x3Q/+v/k5B6ctF5CzgSeAjYBvwEnB3QJr3ROQK4CFgAvAfYJiqfpDg4iSVl/EK/HJyoGNHaNkyoVkyxhiTQZIeFACo6gxgRphtg0KsWw6cHeWYLwIvxiN/6crLeAUATZq4rohduiQ8S8YYYzJIY+h9YHzatYveA6FpU7jnHjft8tq1NSMcbtsGZWWwZo2NemiMMSY0CwoakaFDoaoqcpqqKnj0UTfN8urVcMcd0LYtHHJIzTob9dAYY0woFhQ0Il7GK1B1Myf6Rz+sqHCBQmVlzbpdu9wMi1OmuNkXjTHGGLCgoNHxj1dQUOAaE4ILEpr4WofE8su/osLNvrh9e9yzaYwxphGyoKCR8Y9XsGEDHHmka0z4+OPwk5/UBAmxsFEPjTHG+FlQ0EgVF0P79tC5M4wcCfv3154wySsb9dAYY4yfBQUZokuX2IZA9rNRD40xxvilZJwCEx+LFrlHVVi50jUwjFXwqIfGGGOyl9UUZIDx4+FXv4p9Pxv10BhjTCALChq5bdtcD4JoIx3m5tY0RGze3N1qaNnSNVy0wYyMMcaABQWNnpf5EAoK4Be/cL0VOnd2kyKJuK6Ia9bYYEbGGGMca1PQyHmZD2HvXti82fVWWLUKXn21dk+FXbvc45Qp7vHMMxOTV2OMMenNagoaOS/zIeTnuzTz58OmTeG7LvoHM4o2lLIxxpjMZEFBI+dlPoR9++Cii+DZZ6PfasjNtfYFxhiTrSwoaOS8zIdQXQ07dsD69dFvNVRUuIGQjDHGZB9rU5ABJkxwj5Mnu1qB6mrXw6CyEo46Cj77DB5+2NUoRGtIWFjopl82xhiTfaymIAOEmg/hl7+EZcvg6aehRQt44w14553ox6qqcrUPxhhjso8FBRkkcD6EH//YPbZtC6NGue3RbgsUFrpbEdHaHRhjjMlMFhRkmEWLaoY/zs2FNm3cxV4k+r6DB9fcijDGGJN9LCjIcAcd5HoTeBmU6KSTvAUPxhhjMpMFBRkuNxe6doVmzSKna97cZks0xphsZ0FBFrjmmvADFvlVVbnZEgcNgq++Skq2jDHGpJmUBAUicpOIrBKRPSKyVEROi5B2kIhoiKV7QJrhYdIUJKdE6a1VKze/QbjaAn8Dw3jMljhokFuMMcY0PkkPCkRkGDANmAT0Ad4DXhWRTlF27Qm0D1hWBG2vCNreXlX3xDHrjdagQfDee64XQl5ezfrcXDcE8o9+BA8+6NoelJW5uRJs5kRjjMk+qagpGAPMVtVZqvqFqt4KlAGjouy3SVU3BizBg/tq0PaNCcl9IyUCjz0G778Phx7qnldVufENbrkFfvITN1Pi11+7AZBs5kRjjMk+okn8xheRPNwv+itVdUHA+ulAL1UdGGKfQUApsAbIB5YBD6lqaUCa4cBvgXVALvAJcL+qfhwmHyOAEQAlJSV9582b5yn/5eXlFBUVeUqbLqqq4MsvXZuC9u3h4IPdhf/dd9vz/PNHU1hYyUMPfQTsPdDuoGPHctatc+XMyYGSEhcgxPpaxcXpPeZBYzyfscqGMoKVM5NkQxkh9eUcPHjwUlXtV2eDqiZtAToACpwetH48sDzMPkcDI4G+QH9gBlANnBaQpj9wPXA8cBrwIi746BYtT3379lWvSktLPadNtepq1XHjVAsKVHNyVEG1eXP3/I47VJcvVz3nHLdexD36l8mTS2s9LyhQ3batfq81bpzbno4a0/msr2woo6qVM5NkQxlVU19OYImGuCam/dwHqrocWB6warGIdAHuAt72pVkMLPYnEJH3cLUFtwK3JSuv6WT8eJgyBfYEtKrYtcs9zpzpRjf8+c9dW4OdOyMfKzcXFixwoyTG+lpTprjHiRPrVw5jjDHJk+w2BVuAKqAkaH0JEEsbgA+AbuE2qmtvsCRSmky2bZubHCncjIgVFfDb37rA4Jxzoh9v1y6YNCn0tlNOcdsivdbkybB9u6esG2OMSaGkBgWqug9YCgwJ2jQE1wvBq+NxjRNDEhEBekdKk8lefDH6vfzcXHj3Xdi8OfrxcnJq91oI7Ha4ZUv0/f01DcYYY9JbKm4fTAHmisiHwLu49gIdgKcBRGQOgKpe53t+B7Aa+BzIA64BLgYu9R9QRB4A3sd1UzwId8ugN9F7NGSkjRvD/3L3q6iAl16CDz+su2379vxaz1XdrYFZs2Do0Npp/VM1R3utjdYXxBhj0l7SgwJVnS8irYFxuPEE/g2cr6prfEmCxyvIAx4DOgK7ccHBBar6SkCalsBMoB2wA/gY15gxxCUv87Vr5wYk8t/XD6VZM1i8OPTMib/73bF11q1d67op3nqrGxK5RQuYOtW9hkjkbouFhTaEsjHGNAYpaWioqjNwvQhCbRsU9PxR4NEoxxsNjI5X/hq7oUPhtijNK/ftc7cEQgUFGzbU7ibjv+D7g4y9e2HrVhckeOEfQtlkJv+tJP/snMaYxsvmPshAxcVu2OLCwtDbCwtdA8Hdu0Nvb958X9zyEs8hlI0xxiSWBQUZasIEGDMGCgpcQ0Fw1f4FBW79VVeFDxpGjvxXg1+/sLDmtSZMaPDhTJryD429Zo0NjW1MJrCgIEOJuLEBNmyAI4+ELl3gl790X+ATJ7rq/KrggaJ92reP0BjB42tfemnNa4k06HCNTjZMCqXqhsD2D429enX2Do2dyPPt9djZ8JkzyWFBQYYrLnZDDnfu7AYf8lfjR7vF0BCq0K2b3TLIZIEDVvl7n+za5Z5PmeK2G2MaHwsKssCiRaEbgYW7xdDQX/bNm2dvb4NsqE73MjhWtgxYlcjz7fXY2fCZM8ljQUEWC77F0LlzfKogY+ltkCnVntlUne51cKxMHrAqkec70rE3bKg5djZ95kzyWFBgDtxiUIXS0oZ9meTlwc03h751EGsAkI4BQ7g8ZWJ1eriyeh0cK5MHrErk+Y507G+/rTl2Jn7mTOpZUGAAN17BunXRv+yjUYXp07Prl0q2Vaf7B8eKJJMHrErk+Y527Opqt3316uz6zJnksaDAADB8uBvlsKH27w/9SyXUfc9I90LT8T5pqDwNGgQnn5xd1elDh4bvueKXyQNWJfL2iddjjx8fex7SsebNpB8LCgzgrUo4Fv5fKtu21b3veccd0LYtHHJI3Xuh48al333SSPduV61yIzxmWnV6pKDMy+BYmTxglZf/lUgzizb02BUVsH595n3mTHpIyTDHJv14mS/BLyfHVWPm5kb+xZiTA9dc43o+7NlTsz7Ul5n/dX/xC9cAMnD4Zf+2KVPc48SJ0fMYD/5fVaedVnPvNjhP33zjem80beqGjg6nsVSnq7pfoZMn10x2NXq0GzZ77FjXY0WkZkCqwHTNm7vPQzoOWBXPoZi9/K8Ezywaz2MXFsKhh3pL5//M+YO8fftqJjYrLq67T7KHrE71ENmpfv10ZDUFBvBWJSwCnTrB00/DXXd5mx1x4cLYaiAqK0PPx+A/Xn3uk3qpNg2XZv/+yPduVd1w0ZECAmg81eleG69FGxzLS7fWeFVnDxoEX33V8ON45eV/BVxtWCKOXVXlgi4v6YYOTb+aN5PeLCgwgLcq4cMOg8MPd4MgdeuWmIGPoknWvXn/L6vly6MHP9Eksjo9nveJ69OALtTgWOl279pr+xSv+Y72v9KsmXsf1q+PvT1MtGPn5LjtXbp4u4UzZUr69lBIx3ZDkH6f32SzoMAc4B/MSCT0fAmrV9dUs3n9teQlTSxivU8a6xdPcPuB776LXgsQTuB7l27V6aHUtwFduMGxIonXBcF/nL176x4nkf34Qw38VVjo3p/9+13gVN/XizRvSUlJzWcp2vwmo0fHFuQl6yId7bwkSyqCkkYRcKhqVi99+/ZVr0pLSz2nbczeeKNUjzpKtUsX1ZkzVbdtC51u3DjVwkJV92+enKV5c5enaKqrXf4KClRzcmr2LShw66ura87n1q16oLwXXKDarFnD8iii2rp15PcuXgYOdEs4sXxmJ0xweY9WtgkTwh8j8L2cOdM9D+TlvHgRfJzJk0vrHCfS57Ow0G0PFO29jFbeCy6I7fViObb/sxTqfIZKp+r+bt48+v/Tr38dn3PiVbTz8vzzdcsYT5E+g3feqdqtW/jPbyxCfZ4C16X6egIs0RDXxJRflFO9WFBQV2lpqacvyFD/XIleCgq8XWi9XBBKS0sTlv8uXWJ/32O9KEW7AKvWfGa9HNvrRSRUUOb1Yh/rhTqc4ONMnlxa6zh33ule2+tnyct7Gc7AgaoDBsT2ev79op2T4DThvoNCHctrkDdwYHyDmUi2bo3+Pj3+eGnE//H6BG+Bov2Y8b9nDQ2MGmtQYLcPTL0FNjY7/PDEz4bo9d6813vj69bVvd8aD82bw733xu94wVQTUy3ekPEHvDRQjNegP16OM21aTZV6OLm58Ic/NPy9XLTIjfORiLELQt2aCVUFHSqdl0GmmjWD995L3iBIXm5RiSSu3VC0zw7UnPOGtL3wMi5LvG+txosFBSakWO4TFxe7f/REBQWFheHvzYf6gvTyxZOTA5s2xXdsBj8vPQ0acm8x1uFtvd47re/4A14u0pMmwQ9+0PALp9fBokS8jSXwk5/EpyFerEM/J+N+tpcgb98+1502kng27vXyPlVXJ258BS/fDcFiCYxCBezhxmX517/SsweIBQUmLq65JrYPd9Om3vpx5+RA375u/ILLL3f/mBUVrutiOF6/oOtDxP26Cpf3+vY0iGVGPK+/tlVdLU4sv4KjNV4L1WDS6xet/9xFEtyQNFTwtG9f9OPs3x/9YicC338fn1/JXod+LilJXhdBL0HeKae4LrWR1GcQpHBBr5f3KScn/JgeDQ2m6jtIm9fAKFTAXlHhgrPKytqBp2rqe4CEYkGBiQsv/+yBIl3UA1VXw9tvw+23Q+/ebrnkEvcL74kn4D//ccv06e6Xv6q3vDRtWr8vYBHo08e9fiwXzkCBX2wzZ7ovbq8XiVh6CIwf7ybQieVXcH3GH/D666+iIvqFOtIgT/73bedOb8eJxsv593ox8HrrZcWKhtVMVFXFdlGMFuRddVXs81g0pJbLy/tUXV23pi1et8xi/Z7yixYYDRrkAqxotyZCHTc48Ex5D4VQDQ2yabGGhnXVp5xeGhDVd8nNVT3++MjHF3EN3Pr1U334YdWmTSMfs0kT1SlTSmPKR3Cjq3CtvsMJ1RgvUj5DNfLy2njsnnvc6wQ2wIu14ZvXBl1eGih6XUI1/uvc2bXsj9QgNLicBQWqY8c2vHdMqN4W4d6XaI0oY238GOqz8/jjpfXqIRDus+rl/zYnR/WUU7x/LqI12oz0PuXkqD7xRGnM722nTt4+q/X9norW62ngQFfmWP4P/J/Z4GM3tCGlV6RTQ0MRuUlEVonIHhFZKiKnRUg7SEQ0xNI9KN2lIrJMRPb6Hv878SUxftGqKkNp4nGQ7bw8uPRSWLoUXnkFzjyz7q9lVfcLY8kSuO++8KMigvuVOXgwfP31wZ5eP1wtQKiBeyIJVbUYKZ+hfkV4raZet67h9++9tivxOmZFNP5bLwcfXPtX4Zo18Ne/em8Q6j/Oo4/W/ZUcrYYhmAg895y3tNF+lR91VP3Pif+z4/+cQ+QahuBfm+E+q15uMXTsWPO/Gqn6XtXbr/lw71N+fk2+Ao/t5ZbZunV1ax9D/eKuz/cURG4n5H9PNm3yNkx8sGS3NYkm6UGBiAwDpgGTgD7Ae8CrItIpyq49gfYBy4qAY/YH5gPPA8f7HheIyEnxzr8Jz//Pnp/vLb3XWwh79riLZ6dO7ov1nXeiX4R69HBfOqHs3w+vvw4zZvQJu39OjruANG/uGsjddBO0aAG//jU8/zz8+c/wj3+4C8/06e4LcPNm96VQVVXzBejnpdVzKPv21b5IeK2mPuyw5E2YU98vWj//hbNNG3jrrdDBUyzH8QdvwbdCDjusfvnzOlxxtFsv335bv4mUEj01d6RgZtQo9/+8ejVceGHkC359h8ju3NldvEVcGfbtq33sBQuiB1Oq8OWX9b+lEilYDNdOKDgIqu/7n+y2JtGkYkKkMcBsVZ3le36riJwLjALuibDfJlXdEmbbHUCpqj7se/6wiAz2rb+y4Vk2Xvj/2ceMcf/s27bF58Ps/7Vy4YXuC7ZJEzeCXaR8dOsGs2e7L8yrrnLpTz/d/ZN9/z385S/QqtUOvvvu4JD/zNXVbtm/v34j9uXn1yx5ee71I+U5nOpq+O1vYdky98XYpIlrePnhh6FrGZo2deX8z3/c3x980D7ssZs2dV8+v/+9+/JevtwFFP/v/0H//i4IEqlpRxD4d05O7ecnnwz/9V/wxz+6YM/reW/RAm680V0U7r8ftmxxgVaso0iuXXsQo0a5GqCiItcOJVCzZu6z4B9x0Av/L9ddu+Ddd926nTvde1tZCT/9qXu9Fi3q7ltY6JYePeDzz9268nJ3zMCJtYKJuGO/917Nuv/7v5q/16w5KOy+P/85/PCHdfN4xhkuj/5gI/DYfuedB6ee6s5FZSVce6379f3kk+79UnW/XgP5fxU/9pi7kP3pT+HPW0WFO6+nnlr7/SoshB074I03av4/1qw5qNaxjzkm+i9wVTf66G23wS23uHZHod6DaOX94x9ryltQ4P7/Lr8czj237vs2axa88EL9/q/95fS/N4sWufcv8Fj+Mj/8sHutjz92tT2JJprEEERE8oAK4EpVXRCwfjrQS1UHhthnEFAKrAHygWXAQ6paGpBmLfCkqj4WsO4u4BZV7RwpT/369dMlS5Z4yv+iRYsYlPZjVDZcPMo5cKC74Gza5D7oDfmYFRS4YODii90X05o10Y/XtKm7ANxxR80F4s9/dv/wlZXuS+OGGxZxxhmD2LHD/QLft899AfTu7b4Mfv5z9zh0qPtC37nTtdTetcv9I1dUuC/4igq3fvfu+l/8jTEmkp/9LL49FURkqar2q7M+yUFBB2A9MFBV3wpYPx64WlWPDrHP0cBg4CMgD7gWGOk7xtu+NPuAG1V1TsB+1wGzVLVOZbaIjABGAJSUlPSdN2+ep/yXl5dTVFTksbSNVzzK6Z+17ogj4N//9n6rIFhOjqtaKylx1YOVlaGr56Pt36FDzbqqKnestm3LyckporjY/dIBd3sCah/f/3fwY7i/XQ1DDvv2uWX//hw2b85h40Zh//4cqqqEqqocKivd39XVcmBdVZWgKgfWV1cLRUVC06buJ/mWLW77QQcJVVWwdatLW1Agvh4V4m8ix65dkJNTSXl501rr/fnMyxOqq/1TH8uBMqjWdDFo2tT9ug3cLzhd8PtSUeF/rZolNCEvr6ZGJtR7Gkpg/vzy86vIjVDHrFozA2e04/vvn8fymW3SJFp7BZfn/fsj11T4a178Qa2/5sD/Czwvr4p9+0KXM9pU5k2b1s6jv8Yi+DabavRuivUVnIfAsvlFKmO8Xz+U4Pcl1PsUKt+xysurYv9+V04v32fnn/8NQ4dujnmchXAGDx4cMiio0/IwkQvQAVDg9KD144HlMRznFeDPAc/3AdcFpbkO2BvtWNb7oK54l7NLF2+tcf09CAJbVt93X8OHUva36PYyXn68VFerVlW5pbJSdfPm+vfOKCx0+/70p64V+KmnqpaXq+7c6f4+9VT3986dqt9/X7Ps2KH63HOltd47/7HGjlVdtcpba/hVq1yr7a1b3XC+Awa4v7/7TnXLltDL1197O/bNNzd8romCAtW//a1UN23SiMtXX6nm50f/DN50U+x5KihQXbEi/Gv37++Wb79VHT269ue5WTPXwyY3t/Z5ys93aZcvr8l3uN4k+fnRyxaYx6++Uu3aVfWww1Qff9w99+f18cfr12sjWo+fnBz3moHvy09/Wrc3TXAZRdx719DPiZfzFHiuIr1PofIdajnoILffihU1x+rYUfWss2r3JPH6/sbzu4o06X2wBagCSoLWlwCxNHn6AOgW8HxjHI5pEuTee13DpUiaN3eNkYIbaIk0fCjiwH77yZpG1j/TZE6Oe/02baI3xgs3IqT/NsUTT8AXX7h7n7//vfvF+fbbbikqckuLFjXLQQfBoYfWbvg2dap7Xx97zDW29NIa/vXXXWPC4uKaX1rFxdCqFbRuHXo54ojordpvvtm1l2jIr1J/I7D8fNcgMNLSrRvcdVfkPI0ZA7/7Xex5ys2F0tLwr52X55ZDDnGftcBzcsYZLv9VVbUHvNm71zVsnTs3er7PPDN6b57cXNc49okn3C2y1avhm2/c5753b7e+TRv3PxFr+b02Mm3fvvb7csQR3nrTDB8Od95Zu3FgfUQ7T23burYD774b+X3q2tX7IEz+NlbdurnGrjk57jVUY/tO278/OVNeJzUoUNV9wFJgSNCmIbheCF4dD5QFPF8ch2OaBPHaYn748NrdplTr12I/WEUFrFqV2BbcXkRq5X3nndHnj9i9G7Zujd4y2Wt3tEQPzRuPLnrh1Hda6kTlKVJPjlDvm/+cdOgAf/979M/lmDGRpzU/6SRv5/L3v48eGNdngJ/qajfAmNeujX5evht274Y5c2r3WGjd2tuIqMG89riJ9gPiq6+i5zsvDz74oPa6//3fhg+vnujvqlSMUzAFGC4iN4rIMSIyDXdb4WkAEZkjIoFtA+4QkYtFpJuI9BSRnwMXA08FHHMacIaI3C0i3UXkHlw7hKlJKpOJIJYx9QNb+tdnnPJwx49Hv/2GitRlbfJk94Xpdf6IeNRwJHpo3nh00fPz92Nv1coFN9FGWQwnnnkKFGokRtXI71tpqfeJlF580eXvuONC57t9+/hNfjRkSGzjTkQaFyK4a2NwQBnrOAn+YOroo+tXYxBpxEw/L11Ap093vRxinSckXt9pifyuSnpQoKrzcV0FxwGfAKcC56uqv8NLJ9/ilwc8BnwKvO1Lf4Gq/ingmO8BVwDDfemuA4apalCcZlKlPmPqex2nPNpFIdn99qMJ98s91vkjwPuvhlDdKpM1NG+48noJSkTcr8Jf/tKdm2OPdRdEL4NFJSpPoYQa3MbL7apYa2tyc0PnO56TH73+urdxJ6KNC9GliyvnLbe4i+h//hM6oIz23dClS+3XXbTIVe/XZ2yM3bujT1bm5cK9b5/rKpqo77RoEvpdFaqhQTYt1tCwrkSWM5ahgb0Mn9u8uRsCN9p88KGOFdygKdpQpolW3+GCA/Md/P6+8UZpxNdM5NC8gUIN3VqfIXbDiddntj7D4IYajtrLcQoKVKdO9fYZ95/fSOWMdi4HDvQ2RPaECaGH5G7e3DVmbNXKDT0d6f/Xf76j5SnUsOFPPFF64NiRhkyOddjwnBw3HHI0XoYSB5enwHzH+p0WaQjyWP7n64s0aWhoslwsQwN7/SU7d270iN3rsaL9ikik+g4XXFHhqpFDVVVHm541kUPzBgpVUxFr1XEyeBmd0V8zFelXodeJq1Tj97mM5+RH4W6zhKutCW7HsmiRu38eSzse/3dDfr4bVOjxxyPfsgqVx6eecucv1HvQsWPdWodQvNZg7dzpboVAfL/TAgcFCyeh31WhIoVsWqymoK50Kmd9fmmEi9iDjxUYqYf6tZcKkcob6VdDuNqSyZNLPZUt3HvndQKm4EmDvAr3izTWrlfBn9mGTCoTKU933qnarVv0X4WxvG+xfMa9/G82ZPKjULU+XibKCrXOa01f8C/e0tLSmN6TUK8f/B6sWhV5kqbg989rbVF9Pqv+soWqKfBP7tSpU2zlrw/C1BSk/KKc6sWCgrrSqZyxXjQiXQySOU5BfYUqb7QlPz/8l5j/i6e+Vfz1/WKPVawzTgaLZ1AQLU9ejh3L+xbLZ9zr/2Z9Z3Ksz8UmXBV/fQPKN94ojdstq9NPr1/QGWtwHst752XGy3gFy5FYUBBmsaCgrnQsp9eLhpcv7FD3LtNNYHmjtZk4//zwF6Bw07PGko94fEF70ZALeSKCgoYcpz7vm5fPeEODgnhebKId69e/rl9AuWBBadwC0foGQfUJzmP9P3jjjdKo57uhwXIk4YICa1NgGgWvbRG8TF4UeO+yoa3YEyWwvC+/HPlesdc+6vVprRxLd9KGqs/EU6HEc/rZ+uapPu9brFNxRxIu39G6ZMbSvTMefflD3Rvfvz8+n+eGzC4Z/D55GRch1m6C4XqSBIrnZ8IrCwpMoxGvi4b/WP55DtKVv7zRvsi99FEP7p8daq75cOrTnTQVVNNn+lmo3/sWz894JA292CSyL3/TprF/nkPx2tgz0oXc/z61aBF9ds36BN7JOt+xsKDAmASL5QIcSbgv8kT3rIjnr8tESuYw1l6k+/vWkAuS1wtut26xB0bFxfH5PMc6BkQkeXnxCVTqI9mBgwUFxjQi9e3aF/iLrL7V66moyvSqqir1w1iHk87vW315veB++23sgVFubnxuWXkdsTPahXzRInj//fTv0hwvFhQYk0DxvL8dSbiqapGaX2TxqF5Px+pOcO9rqoexjiRd37f6ivWCG2tgFI9bVvGsQUtm25pUs6DAmARI9v3tcFXVxx1X84ss3arX4ylejdOMN/W54MYSGMXj1ku8L+SNpW1NQ1lQYEwCpOoCHPyLzP/ruSEtsRuDeDVOM94k65dzQ2+9xPNCnu5tROLFggJj4izVF+BQv8ji0RI7ncWrcZrxLlm/nBty6yURF/JMbCMSyIICY+IsHS/A8WyJnY7i1TjNeNeYfjnH+0KeaW1EAiVxqhFjskM6XoD9DcN27QqfprFXr/t/mU6e7Ka2ra52v1yrqjLrnm+68V9wwV1w01WmXsTjzWoKjImzeHWFiqfGMEtkQzWmX67GpCsLCoyJs3S8AGdTl6pMv+ebjjK5Oj3bWFBgTJyl6wU4W7pUgV2kjKkvCwqMSYB0vABb9boxJhpraGhMAvgvwGPGwMknu4Zv997rbhmkujq7sTQMM8YknwUFxiRQul6ArWrdGBOKBQXGJJhdgI0xjYW1KTDGGGMMkKKgQERuEpFVIrJHRJaKyGke9ztVRCpF5N9B64eLiIZYChJTAmOMMSbzJD0oEJFhwDRgEtAHeA94VUQ6RdmvGJgD/D1MkgqgfeCiqnvilW9jjDEm06WipmAMMFtVZ6nqF6p6K1AGjIqy32+BZ4HFYbarqm4MXOKYZ2OMMSbjJTUoEJE8oC/wWtCm14ABEfa7CSgBHopw+GYiskZE1onIX0SkT4MzbIwxxmQRUdXkvZhIB2A9MFBV3wpYPx64WlWPDrHPscAbwMmqukpEHgSGqmqvgDT9gaOAfwEtgNuB84HjVHVFiGOOAEYAlJSU9J03b56n/JeXl1NUVOSxtI2XlTNzZEMZwcqZSbKhjJD6cg4ePHipqvYLXp/WXRJFJB+YD4xV1VXh0qnqYgJuK4jIe8AnwK3AbSHSzwRmAvTr108HDRrkKT+LFi3Ca9rGzMqZObKhjGDlzCTZUEZI33ImOyjYAlThbgUEKgFCtQFoDxwDPCMiz/jW5QAiIpXA+aoafCsCVa0SkSVAt7jl3BhjjMlwSW1ToKr7gKXAkKBNQ3C9EIKtB44Fjg9Ynga+9v0dah9ERIDeuAaMxhhjjPEgFbcPpgBzReRD4F1gJNABd7FHROYAqOp1qrofCB6TYBOwV1X/HbDuAeB9YAVwEO6WQW+i92gwxhhjjE/SgwJVnS8irYFxuNsD/8bdBljjSxJxvIIwWuLaCLQDdgAfA6er6ocNz7ExxhiTHVLS0FBVZwAzwmwbFGXfB4EHg9aNBkbHJ3fGGGNMdkpql8R0JCKbgTVREzptcI0lM52VM3NkQxnByplJsqGMkPpydlbVtsErsz4oiIWILAnVrzPTWDkzRzaUEaycmSQbygjpW06bJdEYY4wxgAUFxhhjjPGxoCA2M1OdgSSxcmaObCgjWDkzSTaUEdK0nNamwBhjjDGA1RQYY4wxxseCAmOMMcYAFhQYY4wxxseCghiISCcReVlEdonIFhF5QkTyUp2vhhARDbGMDEpzrIi8KSK7RWS9iIz3TTqVlkRkmogsEZE9IrI6TJqoZRKRS0VkmYjs9T3+d1IK4FG0copIlzDn99ygdANFZKnvOCuDz38qichxIvKCiHzjO1fLReQnIpITlK7Rnk8vZcyQc9lWRBaKyAbfOfhGRKaLyMFB6RrzuYxaxrQ/l6pqi4cFyAU+AxYBJ+BmdtwAPJnqvDWwXArciJs3wr80C9h+EG5a6z8AvYChwE7gzlTnPUKZngRuxbXuXR1ie9QyAf2BSuA+3PTd9/men5Tq8sVQzi6+83tO0PnNC0hzOLDLd6xjgB8D+4FLU10+X/5+BDwBDAK6Alf4ztW9mXI+PZYxE85la9wEeH2BzsCZwJfAHzLoXHopY1qfy5R/UBrLApwHVAOHBay7BtgDHJTq/DWgXAoMjbB9FPA9tQOFcbhprSXV+Y9StrGEvlhGLRMwH3g9aL83gBdSXa4Yyun/8ukXYd9fACuC1v0GWJzqckXI86PA0kw9n2HKmKnn8jagLMPPZXAZ0/pc2u0D7/oDX6jqNwHrFgL5uKiwMZsm7nbIRyIyMqhqtj/wtqruDli3EDfddZdkZjKOvJSpP/Ba0H4LgQEJz138/UlENonIuyIyNGhbuHL2E5GmyclezA4CtgU8z8TzGVxGv4w5lyLSAbgEeDNgdUadyzBl9EvLc2lBgXftgG+D1m0BqnzbGqvxwDDgLGAe8Dhwb8D2UOX+NmBbY+SlTOHSNKYyl+NqES4Hzgf+DswXkWsC0oQrZxPchC1pRUROAIYDvwpYnVHnM0wZM+Zc+tpPVOB+/e8EbgjYnBHnMkoZ0/pcpmTqZJM+VHViwNNPRCQXd4/uoRRlycSJqm7BBXl+S0SkDfAT4LnU5Kr+RORo4K/AVFX9Y6rzkwjhyphh53I08DPgKODnwFTg/6UyQwkQtozpfi6tpsC7jUBJ0Lo2uAaIG5OfnYT5ADhIRPxlDVXuwG2NkZcyhUvTWMvs9wHQLeB5uHJWkkbT14pId1wj33mqenfQ5ow4n1HKGEqjPJequlFVv1TVP+MulCNE5DDf5ow4l1HKGEranEsLCrxbDBwjIh0D1g0B9gJLU5OlhDge13hyu+/5YuA0ESkISOPvebE6mRmLIy9lWuxbR1Ca9xKeu8Q6HigLeB6unEtUdX+yMhWJiPTAXSwXqOroEEka/fn0UMZQjqeRncsQ/NegfN9joz+XIQSXMZTjSZdzmeqWmY1loaZL4j+APrh78OtpxF0SgYtwXV16AUfguibuAKYFpDkYF7XO86W7BNc6OJ27JB6J+yebgvsyOd635HktE67RUiVwN9AduAfXJSjl3Z5iKOf1wFW4Lk1H4+5j7gNGBxzD3/Vpqi/djb406dKNrSfuXuo8anffahfLZzSdz6fHMmbCubzQV45euEaDFwDLCGhRnwHn0ksZ0/pcpvyD0pgWoBPwF6AC+A7Xtzg/1flqQHnOBT7GNYTZhQt6bgeaBKU7FngLV4NQBjxAGndHxP3i0hBLl1jKhOsj/aXvn/EL4JJUly2Wcvq+fJb5zu33wBLgmhDHGQj8E1frtQoYmeqyBeTtwTBl1Fg/o+l6Pr2UMUPO5Vm4X8Dbgd3AV7iud8UZdC6jljHdz6XNkmiMMcYYwNoUGGOMMcbHggJjjDHGABYUGGOMMcbHggJjjDHGABYUGGOMMcbHggJjjDHGABYUGJOxRGS4iGjAsktEVovI/4rI5SIi9TzuIN/xBsU3xxFfs1ZZEvQa4wJeY10iXsOYdGdBgTGZ7zLcVKznA/fjBkN5AXhdRJqlMmP1cAmuLInwjO/YryTo+MakPZsl0ZjM94mqfh3wfK6ILAAWAI8Ct6YmW/XysaquTsSBVXU9sF5ENifi+MY0BlZTYEwWUjct7/8BPxaRQv96ESkUkV+IyCoR2ed7vE9EIn5XiMjZIvKKiJSJSIWI/FtE7vRNxe1P87KIfBxi38NFpFpERsZaDhHp4qvuHx60vs4tDhE5R0TeE5EdIlIuIstFZHysr2lMJrOgwJjs9Qpu5rZ+ACLSBFiIm3xlGnAe8BvcLYfHohyrK/B34Ee4SWCexY3p/3BAml8Bx4vIiUH7jsCNA/98/YsSmYh0Bf6MG0N+GPBfuImkmifqNY1pjOz2gTHZa63vsb3v8UrgVGCgqr7lW/d3X3vEB0TkF6q6KdSBVPVp/9++BoxvA3nAWBG5V1Wrgb8BK3Hzy3/oS9sUuAF4XlV3xrNwQU7w5WeUqn7vW/ePBL6eMY2S1RQYk738vQ/8rfnPBdYA74lIE/8CvAY0BU4OeyCR9iLyaxFZg5u5bj/wENASOATAFxj8GrhCRA727XoxUOJbn0if+PI0T0SGisghCX49YxolCwqMyV6H+R7LfI+HAJ1xF8/A5UPf9tahDuJrb/Bn3FzyDwFnAD+g5tZBQUDy3wK5wLW+5yOBD1W1TluDePI1tDwH9503F9goIu+LyMBEvq4xjY3dPjAme12Am7N+qe/5d7h77peHSb86zPojcO0SrlXV5/wrReSi4ISq+p2I/AH4fyKyEBiMa8PQUMHfZUUhXrsUKBWRfOAUYALwVxHpoqpb4pAHYxo9CwqMyUIicimusd00Va3wrf4bcClQrqpfxnA4f++F/QHHbwpcHSb9DGAxrhHjDmBeDK8VTq+g52FvdajqXuAfIlKE64FxOGBBgTFYUGBMNjheRNrgGtp1wlXzXwa8DtwTkO55XKO/v4vI48C/fPscgQsgLg4IIAJ9gWuL8LCIVOGCg9HhMqOq7/u6Jp4OPBnmmLG6UUS+AT7G1Vrc4lt/joisBc72vd4rwDdAG1zZNwD/jsPrG5MRLCgwJvMt8D3uATYB/wSuAF5U1QNDBqvqfhE5B7gb103wcFxXwf8Af8U1IKxDVfeJyMXAU8AcYCvwO1zvhlkR8tSH+DUwnAoMBSYBX+MaME4CRgFv4AKc84Cf49pObAXeAa5W1d1xyoMxjZ4EfCcYY0xSiMi7QLWqnuYx/XDcMMRHAmtUtdK3vguuHcQNqjq7gXkSXCPI3wJnqmrHhhzPmMbIagqMMUnha+B3AnAWMAD4YT0O4x+uuV6TOUVxHzDR9/f6BBzfmLRnQYExJlnaA+8B24FJqvrnGPZ9GdfNMZF+i2tsCWFulRiT6ez2gTHGGGMAG7zIGGOMMT4WFBhjjDEGsKDAGGOMMT4WFBhjjDEGsKDAGGOMMT7/HyOWgyQhni+CAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"\n",
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
@@ -410,27 +287,15 @@
"metadata": {},
"source": [
"### $T_{2}$ v.s $T_{2}^{\\ast}$\n",
- "This experiment purpose is to give a better estimation for the dephasing noise. In Ramsey experiment we we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
+ "This experiment purpose is to give a better estimation for the dephasing noise. In Ramsey experiment, we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
"The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. By using echo pulse ($Rx(\\pi)$) we can reduce the effect of the inhomogeneous broadening and get better estimation."
]
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " ┌───┐┌─────────────────┐┌─────────┐ ░ ┌───┐ ░ ┌─┐\n",
- "q_0: ┤ H ├┤ Delay(1e-06[s]) ├┤ Rz(π/5) ├─░─┤ H ├─░─┤M├\n",
- " └───┘└─────────────────┘└─────────┘ ░ └───┘ ░ └╥┘\n",
- "c: 1/═══════════════════════════════════════════════╩═\n",
- " 0 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import qiskit\n",
"from qiskit_experiments.library import T2Ramsey\n",
@@ -479,31 +344,8 @@
]
},
{
- "cell_type": "code",
- "execution_count": 29,
+ "cell_type": "raw",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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cT69ltKCgYcIFBdbQMM46dnSPZWWuK6IxxhjTWFhQEGedOrnHDRssKDDGGNO4WFAQZ/6g4IUX4JxzUpsXY0xtw4cPjzrRkjHZzIKCOPPfPigvh7VrYdYsN5OiMcYYk+4sKIgjVXj1Vff3nj2wbh2MHg0dOsD997vtxhhjTLpKSVAgIjeJyCoR2SMiS0Uk4rybInKViHwiIhUislFEnhORdkFpLhWRZSKy1/f434ktRV3jx8Nzz9Vet2uXCxCmTHHbjTHGmHSV9KBARIYB04BJQB/gPeBVEekUJv0pwFzgWaAncDHQA3g+IE1/YL5v3fG+xwUiEnky8Djatg0mT4bdu0Nvr6hw233jjBhjkuCbb75h0KBB9OjRg969e7NgwYJUZ8mYtJaKmoIxwGxVnaWqX6jqrUAZMCpM+v7AOlX9paquUtX3gSeBwAv+HUCpqj7sO+bDwCLf+qR48UUIM0jcAbm5YN9JxiRPkyZNmDp1KsuWLeO1117jjjvuYNeuXanOljFpK6nDHItIHtAXmBy06TVgQJjd3gUmichFwF+A1sAVwCsBafrjAoVAC4FbwuRjBDAC3FjrixYt8pT/8vLysGmLiuBnP3N/P/NMLz7/vA3XXvs5xx23uU46jy+XMpHKmUkaSzn37NnDzp0767VvVVVVvfdNtpEjR/Ldd9/V69d8uHL6hxfeuXMnzZs3p1WrVqxdu5aO/hbBjUxjOp/15bWMe/bsaRT/v+Gk7fdPqBGNErUAHXBDZp4etH48sDzCfpcA3wP7ffu/BjQL2L4PuC5on+uAvdHyFK8RDWfOVG3e3I1mGG5p3tylS3epHmkrWRpLOTNlRMPrr78+5PC5H3/8saqqbt++Xbdt26aqbujdm2++2fOxvZRzyZIl2rNnz/pkvUHefPNNveiii7RDhw51hncOZ9KkSdqvXz9t0aKFtmnTRi+88EL97LPPwpZz0qRJCtR6z0INL11SUhKvYiWMjWiYHDTWEQ1FpAeuFmAirpbhXKAd8OtU5ivY0KHRByuqqnIzKRqTrc466yzKyspqLf5JdA4++OBaEyDF09atW7nuuuuYOXNmQo4fSXl5Ob169WLatGk0a9bM0z6LFi3ipptu4r333uMf//gHTZo04ayzzqo18ZLf+++/z8yZM+ndu3edbUcffXSt9zp4dkNjgiU7KNgCVAElQetLgI11kwNwD/Chqj6mqp+q6kLgJuBaEfHXAW6M8ZhxV1wMY8dCYWHo7YWFbrvNnGiyWX5+/oFZ/PyLf0rn4cOHc+GFFzJ8+HDefPNNpk+fjoggIqxevTrk8YYNG0br1q2ZPn36gXVffPEFhYWFzJs3D4C9e/dy8cUXc/fddzNgQLi7lIlz/vnnM2nSJIYOHUpOjrev3IULF3LDDTfQq1cvjj32WObOncvmzZv54IMPaqXbsWMHV199Nb/73e8oLi6uc5wmTZrUeq/btm0blzKZzJXUoEBV9wFLgeBJ0ofgeiGEUogLJAL5n/vzvzjGYybEhAkwZgzk5dWsKyyEggK3fsKEZObGmMZp2rRp9O/fnxtuuOHAL9zDDjssZNqpU6dy1VVX8Ytf/AJwAcCVV17J0KFDueKKK1BVhg8fzhlnnMG1114b9bUnTZp0oB1CuOXtt9+Oa3m92LlzJ9XV1XVqUkaMGMHQoUMZPHhwyP1WrlxJhw4dOPzww7niiitYuXJlEnJrGrOkNjT0mQLMFZEPcY0IR+LaGjwNICJzAFT1Ol/6l4FZIjIK13iwPTAV+KeqrvWlmQa8JSJ3Ay8B/w0MBk5NQnkOEIGJE2HYMDj2WMjJgXHjYNQoqyEwBuBvf/sbRUVFB56fdtppvOof8cvn4IMPJi8vj8LCQtq1axd8iFrat2/PnXfeyVNPPcWaNWuYOnUq33///YGag3fffZf58+fTu3dvXnrpJQDmzp3LscceG/J4I0eO5PLLL4/4moceemi0Ysbd7bffzvHHH8+JJ554YN2sWbP4+uuveS54cBSfk046idmzZ9O9e3c2bdrEQw89xIABA/j8889p3bp1srJuGpmkBwWqOl9EWgPjcBf4fwPnq+oaX5JOQelni0gLXE+Cx4EdwD+AnwakeU9ErgAeAiYA/wGGqWrturYk6d7d1Rbs2wfnnWcBgTF+p59+eq37+l7vsUfSpUsXWrZsyaOPPsrMmTN56623aNGiBQCnnnoq1dXVno/VqlUrWrVq1eA8xdOYMWN45513eOedd8j19Xtevnw59957L++88w5NmzYNud95551X6/nJJ59M165defbZZxkzZkzC820ap1TUFKCqM4AZYbYNCrHuSep2OQxO8yLwYjzy11BNmkD79rBmjZst8dhjo49hYEw2KCws5Mgjj4z7cXv16sWMGTN48MEH6d+/f72PM2nSJCZNmhQxzauvvsppp0UchDVuRo8ezbx58ygtLaVr164HuuotXryYLVu20LNnzwNpq6qqeOutt3j66afZtWsX+fn5tY5VVFREz549WbFiRVLybhqnlAQF2aBjx5qgoKrKggJjYpGXl0dVDHOPqyo9e/Zk3LhxDXrddLp9cPvttzN//nxKS0vp3r17rW0XX3wx/fr1q7XuhhtuoFu3btx7773kBTZs8tmzZw9ffvll2PYHxoAFBQnjbxflDwqMMd516dKFDz/8kNWrV1NUVESrVq3CttyfPn067777LkcfffSB6vX6SsTtg/Lycr7++msAqqurWbt2LZ988gmtWrWik2+u9aeeeoqnnnqKL7/8EoCbb76ZuXPn8tJLL1FcXMzGja4jlarSokULWrZsWafRoX9wJn8Xz7Fjx3LRRRfRqVMnNm3axMSJE9m1axfXX399XMtnMkvaj1PQWHXu7B7LyiwoMCZWY8eOJS8vjx49etC2bVvWrl0bMt2yZcu46667+PGPf8yKFSuoqKhIck6jW7JkCX369KFPnz7s3r2bBx54gD59+jA+YIa0LVu2sHz58gPPZ8yYwc6dOznzzDNp3779geWJJ57w/Lrr1q3jyiuv5Oijj+aSSy4hPz+f999/n87+LydjQrCaggTx/99t2AD796c2L8akg9mzZ3veftRRR7F48eKI6ffu3ctVV13FxRdfzMSJE/ntb3/Lp59+ysknnxyH3MbPoEGD/KOshvXggw/y4IMPHngeLn2k4X+Dh8z1j9NgTCyspiBBunRxj2VlsHdvSrNiTEa6++672bFjB7/61a8oLCykW7duTJs2LWytgjEmOgsKEiSwpsCCAmPi67XXXuOpp57iueee4+CDDwbgvvvu4x//+IfdMzemAez2QYL4awo2boQ0vM1pTKN29tlnsz/ovty1117radRCY0x4VlOQIIWF0Lata2T47bfW2NAYY0z6s6AggfzdmTdsgMrK1ObFGGOMicaCggQKHKvAggJjjDHpzoKCBPKNS2JBgTHGmEbBgoIECmxsuGdPSrNijDHGRGVBQQL5uyWuX29BgTHGmPRnQUEC+SeDW7vWRjU0xhiT/mycggTq1g1E4Jtv3ABGNluiqY/27dvXmRHPqz179lBQUBDnHKUfK2fm8FrG9u3bJyE32ceCggRq3hzatXNDHa9f72oOLCgwsXr55Zfrve+iRYsYNGhQ/DKTpqycmSMbypjO7PZBAonUNDZcs8YGMDLGGJPeLChIsK5d3aO1KzDGGJPuLChIsMDGhjYxkjHGmHRmQUGC+ac0t6DAGGNMurOgIMEKC93j6tUWFBhjjElvFhQk0LZtsGOH+3vtWti82RobGmOMSV8pCQpE5CYRWSUie0RkqYicFiHtbBHREMuugDSDwqTpnpwS1aYK998PHTq4GgKA6moYNMitV01FrowxxpjIkh4UiMgwYBowCegDvAe8KiKdwuxyO9A+aFkJ/CFE2p5B6VbENfMejR8PU6a4oY2rq2vW79sH06a57cYYY0y6SUVNwRhgtqrOUtUvVPVWoAwYFSqxqu5Q1Y3+BTgC6ArMCpF8U2BaVU16Zf22bTB5MlRUhN5eUeG2b9+e1GwZY4wxUYkmsS5bRPKACuBKVV0QsH460EtVB3o4xmygn6r2Clg3CCgF1gD5wDLgIVUtDXOMEcAIgJKSkr7z/F0EoigvL6eoqChimi1b3LDGgTUEpaWH8de/HsGpp67j4ou/JicHDjsM2rTx9LJJ56WcmSAbypkNZQQrZybJhjJC6ss5ePDgpapad/x0VU3aAnQAFDg9aP14YLmH/Q/GBRW3B60/GhgJ9AX6AzOAauC0aMfs27evelVaWho1zYQJqiKqruVA6EXEpUtXXsqZCbKhnNlQRlUrZybJhjKqpr6cwBINcU1sbHMfXIO75TE3cKWqLgeWB6xaLCJdgLuAt5OWO9xcB4WFsGtX+DSFhS6dMcYYk06S3aZgC1AFlAStLwE2etj/x8AfVXWrh7QfAN1iy17DDR0avdthVRVcdlly8mOMMcZ4ldSgQFX3AUuBIUGbhuB6IYQlIicCxxG6gWEox+MaMCZVcTGMHVszaFGw/HwYPRpatkxqtowxxpioUnH7YAowV0Q+BN7FtQXoADwNICJzAFT1uqD9RgArVHVR8AFF5A5gNfA5kIe7zXAxcGkC8h/VhAnucfJk1w2xuhpyctzjWWfBuHGpyJUxxhgTWdKDAlWdLyKtgXG4sQT+DZyvqmt8SeqMVyAiLYArgAlhDpsHPAZ0BHbjgoMLVPWVOGffExGYOBHGjIGTT3aBQdu28NFHcMIJNqqhMcaY9JSShoaqOgPXQyDUtkEh1u0EwvbdUNVHgUfjlb94KS6G5b7mj/ff74KCb75xcyC0aJHavBljjDHBbO6DJDnqKPf4v/9rEyMZY4xJTxYUJIk/KNi71w1/bIwxxqQbCwqSpG1b97hnD8yd60Y+NMYYY9KJBQUJ5p8xsWfPmnWPPOKGObYZE40xxqSTxjaiYaMTOGOin79NwZQp7nHixOTnyxhjjAlmNQUJZDMmGmOMaUwsKEigF1+E3NzIaXJyYMGCyGmMMcaYZLCgIIE2bgxfS+BXUQHPP29tC4wxxqSeBQUJ5J8xMZrFi13bA2OMMSaVLChIIC8zJoIbBtnaFhhjjEk1CwoSyD9jYl5e9LS5uda2wBhjTGpZUJBgEyZA//7R01VUuDYIxhhjTKpYUJBgInD11dHbFhQWujYIxhhjTKpYUJAEQ4dCdXXkNFVVcNllycmPMcYYE4oFBUngb1sQrragsNBtb9kyqdkyxhhjarFhjpNkwgT3+NhjNcMcFxS4xzFjarYbY4wxqWI1BUki4uY4WL0amjZ16667DlascOtFUpo9Y4wxxoKCZDvkEDjnHPd3ly7QrFlKs2OMMcYcYEFBkuXkQPfu7u+vv649e6IxxhiTShYUpEDPnu5xxQrYvTu1eTHGGGP8LChIgeOOc49ffeW6KnoZCtkYY4xJtJQEBSJyk4isEpE9IrJURE6LkHa2iGiIZVdQuoG+Y+0RkZUiMjLxJamfbt1cW4Jvv4UrroDKylTnyBhjjElBUCAiw4BpwCSgD/Ae8KqIdAqzy+1A+6BlJfCHgGMeDrziO1Yf4OfAkyJyaYKK0SB5eXDEEe7v3bth//7U5scYY4yB1NQUjAFmq+osVf1CVW8FyoBRoRKr6g5V3ehfgCOArsCsgGQjgQ2qeqvvmLOAZ4GxiS1K/TRt6noeAJSVwaxZsG1bSrNkjDHGJDcoEJE8oC/wWtCm14ABHg/zY+BzVX0vYF3/EMdcCPQTkab1yWuiqML48fDqq+75jh0wbhx06AD33++2G2OMMakgmsSrkIh0ANYDA1X1rYD144GrVfXoKPsfjKtVuEdVpwWs/wp4TlUnBKw7HXgT6KCqZUHHGQGMACgpKek7b948T/kvLy+nqKjIU9pwNmxwbQmWLWvFb37Tm65dt3PTTZ8ArrtiSYkLEFIpHuVsDLKhnNlQRrByZpJsKCOkvpyDBw9eqqr96mxQ1aQtQAdAgdOD1o8HlnvY/2ZgD9AqaP1XwPigdaf7Xqt9pGP27dtXvSotLfWcNpStW1ULClRdfUDopaBAddu2Br1MgzW0nI1FNpQzG8qoauXMJNlQRtXUlxNYoiGuicluU7AFqAJKgtaXABs97P9j4I+qujVo/cYwx6z0vWZaePFFyM2NnCY3FxYsSE5+jDHGmEBJDQpUdR+wFBgStGkIrudAWCJyInActRsY+i0Oc8wlqpo2bfs3boSKishpKipcOmOMMSbZUtH7YAowXERuFJFjRGQa7rbC0wAiMkdE5oTYbwSwQlUXhdj2NHCoiEz1HfNGYDgwOSElqKd27cJPn+zXrJlLZ4wxxiRb0oMCVZ0P3AGMAz4BTgXOV9U1viSdfMsBItICuAL4TZhjrgLOx7Uj+AS4D7hNVf8Y9wI0wNCh0UcvrKhwIx1aLwRjjDHJ1iQVL6qqM4AZYbYNCrFuJxCxmaaqvgmcEI/8JUpxMYwdC1OmRL6NMGMGFBS4KZWNMcaYZLG5D5JswgQYFXKYphoVFTB5MmzfnpQsGWOMMYAFBUknAkcfHb1tgfVCMMYYk2wWFKTAxo3Rp0y2XgjGGGOSzYKCFPDSC6Gw0HohGGOMSS4LClLASy+Eqiq47LLk5McYY4wBCwpSwt8LIVxtQWGh296yZVKzZYwxJsulpEuicb0QwPUy2LPH/Z2f7xoijhlTs90YY4xJFqspSBERNw7BqlU1NQYXXggff+zWi6Q2f8YYY7KPBQUpdsghNeMWFBVBXl5q82OMMSZ7WVCQYjk5cPzx7u9PP4XKyuiNEI0xxphEsKAgDZx4ontctgz274d9+1KbH2OMMdnJgoI00KEDdO4Me/fCsGFw1lmpzpExxphsZEFBGmjaFHr1cn/v2gXV1anNjzHGmOxkQUEayMuDY491f3/3HXzzDcyaBdu2pTZfxhhjsosFBWniq6/c486dsH49jB7tbivcfz+opjZvxhhjsoMNXpQGxo+HV16pvW7XLvc4ZYp7nDgxuXkyxhiTfaymIMW2bas9qmGwigq3ffv2pGbLGGNMFoopKBCRk0XkQRH5m4h8KiIrRGSxiMwWkRtEpDhRGc1UL74IubmR0+TmwoIFycmPMcaY7OUpKBCR60XkM+A9YDRQCKwAPgC2AScBvwHW+wKEwxOU34yzcaOrDYikosKlM8YYYxIpapsCEfkUaAvMAa4DPlGt2/RNRA4GLgSuBpaJyHBVnR/n/Gacdu3c3Af+NgShFBa6dMYYY0wieakp+C1wuKr+VFU/DhUQAKjqDlV9XlXPB04Gtscxnxlr6NDowxpXVcFllyUnP8YYY7JX1KBAVaepaphmcGH3+ZeqLqx/trJHcTGMHVszU2KwwkK3vWXLpGbLGGNMFkpJ7wMRuUlEVonIHhFZKiKnRUmfJyITfPvsFZG1InJbwPbhIqIhloLEl6bhJkyAMWOgICC3TZpAfr5bP2FC6vJmjDEme3gep0BELgZ+CPQAWvlWbwWWAf+nqi95PM4wYBpwE/CO7/FVEemhqmvD7DYP6AiMwDVwLAGaBaWpAI4IXBFrDUeqiLhxCMaMge7dYdMmOOwwmD8fevd2240xxphE89LQsBh4GRgArAU+B3zj79EKGARcLyKLgQtVNdrgvGOA2ao6y/f8VhE5FxgF3BPi9c8GzgSOUNUtvtWrQxxXVbVRt9EvLoZOnVxQsGGDqznYu9fVGBhjjDGJ5uX2weNAJ2CgqnZR1QtU9VrfcoGqHg6cDhwKTI50IBHJA/oCrwVteg0XdIRyMfARMEZE1vnGRnhCRIqC0jUTkTW+NH8RkT4eypZ23ngDjj7aBQPLlkXulWCMMcbEk4TpTFCTQGQLcJOq/iFKumHADFVtHSFNB2A9LsB4K2D9eOBqVT06xD5/w9VG/B2YALQEngQ+VdWhvjT9gaOAfwEtgNuB84HjVHVFiGOOwN2KoKSkpO+8efMiFe2A8vJyioqCY5H4UoWpU4/kz3/uyPXXr+LKK9ckvaYgGeVMB9lQzmwoI1g5M0k2lBFSX87BgwcvVdV+dTaoasQF2AkM8ZDuHGBnlDQdAAVOD1o/HlgeZp/XgN3AwQHrzvYdpyTMPrnAZ8AT0fLdt29f9aq0tNRz2vqqrFSdOlUVVE87TXX5ctW9exP+srUko5zpIBvKmQ1lVLVyZpJsKKNq6ssJLNEQ10Qvtw8WA/eJSItwCXzb7sGNeBjJFqAK11AwUAkQrj1AGbBeVXcErPvC99gp1A6qWgUsAbpFyU/ayc2F/v3d30uWwP79sG9favNkjDEmO3jpfXAHsAhYIyJ/Bf6NG9oYoBjoCVyAu9gPjnQgVd0nIkuBIUDgaP5DgD+G2e1d4DIRKVLVct+6o3yPa0LtICIC9MbdTmh0unZ1y8qV8OWX0KYNZEFtmjHGmBSLGhSo6jIROQ74CXARcBXg7ySnuJ4Ac4DHVHWDh9ecAswVkQ9xF/yRuNsKTwOIyBzf617nS/974H7gGRF5ENemYBrwoqpu8u3zAPA+rrviQcBtuKBglIf8pJ3CQujb1wUF//wn9GmUTSaNMcY0Np4GL1LVMlUdrapHAs1xPQ0OBYpU9QjfNi8BAermQ7gDGAd8ApwKnK+q/l/9nQi4LeCrHTgLOBjXC+EPwJvAjwIO2xKYibut8Jovb6er6ode8pRu8vLgBz9wf3/wgbuFsH9/avNkjDEm83kevMhP3YBAZQ15UVWdAcwIs21QiHXLcY0Lwx1vNG72xozQpAkM8HXQ/OgjqK52XRSbNk1tvowxxmS2qDUFInJJrAcVkfYicnL9smTAjVXQsSN8/z385z+wc2eqc2SMMSbTebl98KSIfCIiI0WkVaSEInKaiMwEvsbd0zf15G9XAK5dQXm5G8PAGGOMSRQvtw+6AWNxAwc9KSJf4Fr1bwb24nogdAX64e77v4Ub1yBa90QTwXnnwVrfTBAffABXXAEDB0JODixalNKsGWOMyVBeeh9UABNE5BHgv4FzgZNwPQYKgO+AL3E9Auar6peJy272EIGDDnJ/v/++e6yqckGBMcYYkwieGxr6xhj4O25GxEYx+2BjtW0blJVBRQW0aAFbt8Knn7pJkgBmzYKhQ90ESsYYY0y8eGlomCsiD4rINuBb4HsR+aOItEx47rKMKtx/P3ToAF9/DevWucAA4PLL3e2E1ath9GiX5v77rZ2BMcaY+PFSUzASNzfBItw4AV1xtxG+B25IWM6y0PjxMGUK7Amoh6mqco9uNgT3t3/mxClT3OPEicnLozHGmMzl5Q71j4FZqnqGqv5UVS8Dbgau8U2FbOJg2zaYPLmmZsCLigq3z/btCcuWMcaYLOIlKOhK7XkKAObjZiLsHPccZakXX3STIcUqNxcWBJ8dY4wxph68BAVFuFsFgfxD6YSdOdHEZuPG2GoJ/Coq3L7GGGNMQ3ntfXCoiHQNeJ4bsH57YEJVXRmPjGWbdu3cgEX+9gJeFRa6fY0xxpiG8hoUvBhm/Ush1tWjEtwMHQq33Rb7flVVcNll8c+PMcaY7OMlKLAeBklQXAxjx8Ljj8Pu3d72KSyEMWOgZcuEZs0YY0yW8DKi4bPJyIiBCRPcPAevvBI9bdOmLiCYMCHx+TLGGJMdbNDcNCICJ5/sHqMZOdKNT+AlrTHGGOOFBQVpxt/gMJK8PDjkEKiuTk6ejDHGZAcLCtLM0KE1oxiGU1kJ55zjve2BMcYY44UFBWnG3+AwUm2BKtx0E3wfPHqEMcYY0wAWFKShCRNcI8KCgpqpkps3h/x8FzSoQnm5W+wWgjHGmHixoCANibhGhBs2wJFHQpcu8MtfupkTL7nEpVm7FubNq5lO2RhjjGkoCwrSWHExtG8PnTvDjTfCr34Fc+a4bRUVMGmSCxpsCmVjjDHxYEFBmlu0yC3jx8PUqbB/f822PXtg7143hXKXLjBoUEqyaIwxJkOkJCgQkZtEZJWI7BGRpSJyWpT0eSIywbfPXhFZKyK3BaW5VESW+bYvE5H/TmwpkifatMoVFbBuneuVYIwxxtRX0oMCERkGTAMmAX2A94BXRaRThN3mAecCI4CjgcuATwOO2R83nfPzwPG+xwUiclICipB0XqZVVoUvv4RZs1wQYYwxxsQqFTUFY4DZqjpLVb9Q1VuBMmBUqMQicjZwJnC+qr6uqqtV9QNVXRSQ7A6gVFUf9h3zYWCRb32j52VaZVX47jsYPRo6dLB2BsYYY2InmsQrh4jkARXAlaq6IGD9dKCXqg4Msc8M4CjgQ+A6YDfwKnCvqpb70qwFnlTVxwL2uwu4RVU7hzjmCFytAyUlJX3nzZvnKf/l5eUUFRV5LG38bNkC33xTu/vhO+8cyksvdeOYY7bwP//z7zr75ORASYkLEGKVqnImWzaUMxvKCFbOTJINZYTUl3Pw4MFLVbVfnQ2qmrQF6AAocHrQ+vHA8jD7/A3YA/wVOAk4B/gKeDEgzT7guqD9rgP2RstT37591avS0lLPaeNp61bVggJV99vf+1JQoLptW+yvl6pyJls2lDMbyqhq5cwk2VBG1dSXE1iiIa6JjaH3QQ4ukLhK3W2DhcAtwKUiUpLarCWHl1EOQ8nNhQULoqczxhhjIPltCrYAVUDwxbwE2BhmnzJgvaruCFj3he/R3zhxY4zHbHT8oxzm53vfp6LCtUcwxhhjvEhqUKCq+4ClwJCgTUNwvRBCeRfoICKBN1+O8j2u8T0ujvGYjY5/lMNbbvE+XXJhoZt10RhjjPEiFbcPpgDDReRGETlGRKbh2ho8DSAic0RkTkD63wPfAc+ISE8ROQXXpfFFVd3kSzMNOENE7haR7iJyDzAYmJqkMiXFtm0wfbr3XgW7d8Nll9U8HzTIBjgyxhgTXpNkv6CqzheR1sA4oD3wb1x3Q/+v/k5B6ctF5CzgSeAjYBvwEnB3QJr3ROQK4CFgAvAfYJiqfpDg4iSVl/EK/HJyoGNHaNkyoVkyxhiTQZIeFACo6gxgRphtg0KsWw6cHeWYLwIvxiN/6crLeAUATZq4rohduiQ8S8YYYzJIY+h9YHzatYveA6FpU7jnHjft8tq1NSMcbtsGZWWwZo2NemiMMSY0CwoakaFDoaoqcpqqKnj0UTfN8urVcMcd0LYtHHJIzTob9dAYY0woFhQ0Il7GK1B1Myf6Rz+sqHCBQmVlzbpdu9wMi1OmuNkXjTHGGLCgoNHxj1dQUOAaE4ILEpr4WofE8su/osLNvrh9e9yzaYwxphGyoKCR8Y9XsGEDHHmka0z4+OPwk5/UBAmxsFEPjTHG+FlQ0EgVF0P79tC5M4wcCfv3154wySsb9dAYY4yfBQUZokuX2IZA9rNRD40xxvilZJwCEx+LFrlHVVi50jUwjFXwqIfGGGOyl9UUZIDx4+FXv4p9Pxv10BhjTCALChq5bdtcD4JoIx3m5tY0RGze3N1qaNnSNVy0wYyMMcaABQWNnpf5EAoK4Be/cL0VOnd2kyKJuK6Ia9bYYEbGGGMca1PQyHmZD2HvXti82fVWWLUKXn21dk+FXbvc45Qp7vHMMxOTV2OMMenNagoaOS/zIeTnuzTz58OmTeG7LvoHM4o2lLIxxpjMZEFBI+dlPoR9++Cii+DZZ6PfasjNtfYFxhiTrSwoaOS8zIdQXQ07dsD69dFvNVRUuIGQjDHGZB9rU5ABJkxwj5Mnu1qB6mrXw6CyEo46Cj77DB5+2NUoRGtIWFjopl82xhiTfaymIAOEmg/hl7+EZcvg6aehRQt44w14553ox6qqcrUPxhhjso8FBRkkcD6EH//YPbZtC6NGue3RbgsUFrpbEdHaHRhjjMlMFhRkmEWLaoY/zs2FNm3cxV4k+r6DB9fcijDGGJN9LCjIcAcd5HoTeBmU6KSTvAUPxhhjMpMFBRkuNxe6doVmzSKna97cZks0xphsZ0FBFrjmmvADFvlVVbnZEgcNgq++Skq2jDHGpJmUBAUicpOIrBKRPSKyVEROi5B2kIhoiKV7QJrhYdIUJKdE6a1VKze/QbjaAn8Dw3jMljhokFuMMcY0PkkPCkRkGDANmAT0Ad4DXhWRTlF27Qm0D1hWBG2vCNreXlX3xDHrjdagQfDee64XQl5ezfrcXDcE8o9+BA8+6NoelJW5uRJs5kRjjMk+qagpGAPMVtVZqvqFqt4KlAGjouy3SVU3BizBg/tq0PaNCcl9IyUCjz0G778Phx7qnldVufENbrkFfvITN1Pi11+7AZBs5kRjjMk+okn8xheRPNwv+itVdUHA+ulAL1UdGGKfQUApsAbIB5YBD6lqaUCa4cBvgXVALvAJcL+qfhwmHyOAEQAlJSV9582b5yn/5eXlFBUVeUqbLqqq4MsvXZuC9u3h4IPdhf/dd9vz/PNHU1hYyUMPfQTsPdDuoGPHctatc+XMyYGSEhcgxPpaxcXpPeZBYzyfscqGMoKVM5NkQxkh9eUcPHjwUlXtV2eDqiZtAToACpwetH48sDzMPkcDI4G+QH9gBlANnBaQpj9wPXA8cBrwIi746BYtT3379lWvSktLPadNtepq1XHjVAsKVHNyVEG1eXP3/I47VJcvVz3nHLdexD36l8mTS2s9LyhQ3batfq81bpzbno4a0/msr2woo6qVM5NkQxlVU19OYImGuCam/dwHqrocWB6warGIdAHuAt72pVkMLPYnEJH3cLUFtwK3JSuv6WT8eJgyBfYEtKrYtcs9zpzpRjf8+c9dW4OdOyMfKzcXFixwoyTG+lpTprjHiRPrVw5jjDHJk+w2BVuAKqAkaH0JEEsbgA+AbuE2qmtvsCRSmky2bZubHCncjIgVFfDb37rA4Jxzoh9v1y6YNCn0tlNOcdsivdbkybB9u6esG2OMSaGkBgWqug9YCgwJ2jQE1wvBq+NxjRNDEhEBekdKk8lefDH6vfzcXHj3Xdi8OfrxcnJq91oI7Ha4ZUv0/f01DcYYY9JbKm4fTAHmisiHwLu49gIdgKcBRGQOgKpe53t+B7Aa+BzIA64BLgYu9R9QRB4A3sd1UzwId8ugN9F7NGSkjRvD/3L3q6iAl16CDz+su2379vxaz1XdrYFZs2Do0Npp/VM1R3utjdYXxBhj0l7SgwJVnS8irYFxuPEE/g2cr6prfEmCxyvIAx4DOgK7ccHBBar6SkCalsBMoB2wA/gY15gxxCUv87Vr5wYk8t/XD6VZM1i8OPTMib/73bF11q1d67op3nqrGxK5RQuYOtW9hkjkbouFhTaEsjHGNAYpaWioqjNwvQhCbRsU9PxR4NEoxxsNjI5X/hq7oUPhtijNK/ftc7cEQgUFGzbU7ibjv+D7g4y9e2HrVhckeOEfQtlkJv+tJP/snMaYxsvmPshAxcVu2OLCwtDbCwtdA8Hdu0Nvb958X9zyEs8hlI0xxiSWBQUZasIEGDMGCgpcQ0Fw1f4FBW79VVeFDxpGjvxXg1+/sLDmtSZMaPDhTJryD429Zo0NjW1MJrCgIEOJuLEBNmyAI4+ELl3gl790X+ATJ7rq/KrggaJ92reP0BjB42tfemnNa4k06HCNTjZMCqXqhsD2D429enX2Do2dyPPt9djZ8JkzyWFBQYYrLnZDDnfu7AYf8lfjR7vF0BCq0K2b3TLIZIEDVvl7n+za5Z5PmeK2G2MaHwsKssCiRaEbgYW7xdDQX/bNm2dvb4NsqE73MjhWtgxYlcjz7fXY2fCZM8ljQUEWC77F0LlzfKogY+ltkCnVntlUne51cKxMHrAqkec70rE3bKg5djZ95kzyWFBgDtxiUIXS0oZ9meTlwc03h751EGsAkI4BQ7g8ZWJ1eriyeh0cK5MHrErk+Y507G+/rTl2Jn7mTOpZUGAAN17BunXRv+yjUYXp07Prl0q2Vaf7B8eKJJMHrErk+Y527Opqt3316uz6zJnksaDAADB8uBvlsKH27w/9SyXUfc9I90LT8T5pqDwNGgQnn5xd1elDh4bvueKXyQNWJfL2iddjjx8fex7SsebNpB8LCgzgrUo4Fv5fKtu21b3veccd0LYtHHJI3Xuh48al333SSPduV61yIzxmWnV6pKDMy+BYmTxglZf/lUgzizb02BUVsH595n3mTHpIyTDHJv14mS/BLyfHVWPm5kb+xZiTA9dc43o+7NlTsz7Ul5n/dX/xC9cAMnD4Zf+2KVPc48SJ0fMYD/5fVaedVnPvNjhP33zjem80beqGjg6nsVSnq7pfoZMn10x2NXq0GzZ77FjXY0WkZkCqwHTNm7vPQzoOWBXPoZi9/K8Ezywaz2MXFsKhh3pL5//M+YO8fftqJjYrLq67T7KHrE71ENmpfv10ZDUFBvBWJSwCnTrB00/DXXd5mx1x4cLYaiAqK0PPx+A/Xn3uk3qpNg2XZv/+yPduVd1w0ZECAmg81eleG69FGxzLS7fWeFVnDxoEX33V8ON45eV/BVxtWCKOXVXlgi4v6YYOTb+aN5PeLCgwgLcq4cMOg8MPd4MgdeuWmIGPoknWvXn/L6vly6MHP9Eksjo9nveJ69OALtTgWOl279pr+xSv+Y72v9KsmXsf1q+PvT1MtGPn5LjtXbp4u4UzZUr69lBIx3ZDkH6f32SzoMAc4B/MSCT0fAmrV9dUs3n9teQlTSxivU8a6xdPcPuB776LXgsQTuB7l27V6aHUtwFduMGxIonXBcF/nL176x4nkf34Qw38VVjo3p/9+13gVN/XizRvSUlJzWcp2vwmo0fHFuQl6yId7bwkSyqCkkYRcKhqVi99+/ZVr0pLSz2nbczeeKNUjzpKtUsX1ZkzVbdtC51u3DjVwkJV92+enKV5c5enaKqrXf4KClRzcmr2LShw66ura87n1q16oLwXXKDarFnD8iii2rp15PcuXgYOdEs4sXxmJ0xweY9WtgkTwh8j8L2cOdM9D+TlvHgRfJzJk0vrHCfS57Ow0G0PFO29jFbeCy6I7fViObb/sxTqfIZKp+r+bt48+v/Tr38dn3PiVbTz8vzzdcsYT5E+g3feqdqtW/jPbyxCfZ4C16X6egIs0RDXxJRflFO9WFBQV2lpqacvyFD/XIleCgq8XWi9XBBKS0sTlv8uXWJ/32O9KEW7AKvWfGa9HNvrRSRUUOb1Yh/rhTqc4ONMnlxa6zh33ule2+tnyct7Gc7AgaoDBsT2ev79op2T4DThvoNCHctrkDdwYHyDmUi2bo3+Pj3+eGnE//H6BG+Bov2Y8b9nDQ2MGmtQYLcPTL0FNjY7/PDEz4bo9d6813vj69bVvd8aD82bw733xu94wVQTUy3ekPEHvDRQjNegP16OM21aTZV6OLm58Ic/NPy9XLTIjfORiLELQt2aCVUFHSqdl0GmmjWD995L3iBIXm5RiSSu3VC0zw7UnPOGtL3wMi5LvG+txosFBSakWO4TFxe7f/REBQWFheHvzYf6gvTyxZOTA5s2xXdsBj8vPQ0acm8x1uFtvd47re/4A14u0pMmwQ9+0PALp9fBokS8jSXwk5/EpyFerEM/J+N+tpcgb98+1502kng27vXyPlVXJ258BS/fDcFiCYxCBezhxmX517/SsweIBQUmLq65JrYPd9Om3vpx5+RA375u/ILLL3f/mBUVrutiOF6/oOtDxP26Cpf3+vY0iGVGPK+/tlVdLU4sv4KjNV4L1WDS6xet/9xFEtyQNFTwtG9f9OPs3x/9YicC338fn1/JXod+LilJXhdBL0HeKae4LrWR1GcQpHBBr5f3KScn/JgeDQ2m6jtIm9fAKFTAXlHhgrPKytqBp2rqe4CEYkGBiQsv/+yBIl3UA1VXw9tvw+23Q+/ebrnkEvcL74kn4D//ccv06e6Xv6q3vDRtWr8vYBHo08e9fiwXzkCBX2wzZ7ovbq8XiVh6CIwf7ybQieVXcH3GH/D666+iIvqFOtIgT/73bedOb8eJxsv593ox8HrrZcWKhtVMVFXFdlGMFuRddVXs81g0pJbLy/tUXV23pi1et8xi/Z7yixYYDRrkAqxotyZCHTc48Ex5D4VQDQ2yabGGhnXVp5xeGhDVd8nNVT3++MjHF3EN3Pr1U334YdWmTSMfs0kT1SlTSmPKR3Cjq3CtvsMJ1RgvUj5DNfLy2njsnnvc6wQ2wIu14ZvXBl1eGih6XUI1/uvc2bXsj9QgNLicBQWqY8c2vHdMqN4W4d6XaI0oY238GOqz8/jjpfXqIRDus+rl/zYnR/WUU7x/LqI12oz0PuXkqD7xRGnM722nTt4+q/X9norW62ngQFfmWP4P/J/Z4GM3tCGlV6RTQ0MRuUlEVonIHhFZKiKnRUg7SEQ0xNI9KN2lIrJMRPb6Hv878SUxftGqKkNp4nGQ7bw8uPRSWLoUXnkFzjyz7q9lVfcLY8kSuO++8KMigvuVOXgwfP31wZ5eP1wtQKiBeyIJVbUYKZ+hfkV4raZet67h9++9tivxOmZFNP5bLwcfXPtX4Zo18Ne/em8Q6j/Oo4/W/ZUcrYYhmAg895y3tNF+lR91VP3Pif+z4/+cQ+QahuBfm+E+q15uMXTsWPO/Gqn6XtXbr/lw71N+fk2+Ao/t5ZbZunV1ax9D/eKuz/cURG4n5H9PNm3yNkx8sGS3NYkm6UGBiAwDpgGTgD7Ae8CrItIpyq49gfYBy4qAY/YH5gPPA8f7HheIyEnxzr8Jz//Pnp/vLb3XWwh79riLZ6dO7ov1nXeiX4R69HBfOqHs3w+vvw4zZvQJu39OjruANG/uGsjddBO0aAG//jU8/zz8+c/wj3+4C8/06e4LcPNm96VQVVXzBejnpdVzKPv21b5IeK2mPuyw5E2YU98vWj//hbNNG3jrrdDBUyzH8QdvwbdCDjusfvnzOlxxtFsv335bv4mUEj01d6RgZtQo9/+8ejVceGHkC359h8ju3NldvEVcGfbtq33sBQuiB1Oq8OWX9b+lEilYDNdOKDgIqu/7n+y2JtGkYkKkMcBsVZ3le36riJwLjALuibDfJlXdEmbbHUCpqj7se/6wiAz2rb+y4Vk2Xvj/2ceMcf/s27bF58Ps/7Vy4YXuC7ZJEzeCXaR8dOsGs2e7L8yrrnLpTz/d/ZN9/z385S/QqtUOvvvu4JD/zNXVbtm/v34j9uXn1yx5ee71I+U5nOpq+O1vYdky98XYpIlrePnhh6FrGZo2deX8z3/c3x980D7ssZs2dV8+v/+9+/JevtwFFP/v/0H//i4IEqlpRxD4d05O7ecnnwz/9V/wxz+6YM/reW/RAm680V0U7r8ftmxxgVaso0iuXXsQo0a5GqCiItcOJVCzZu6z4B9x0Av/L9ddu+Ddd926nTvde1tZCT/9qXu9Fi3q7ltY6JYePeDzz9268nJ3zMCJtYKJuGO/917Nuv/7v5q/16w5KOy+P/85/PCHdfN4xhkuj/5gI/DYfuedB6ee6s5FZSVce6379f3kk+79UnW/XgP5fxU/9pi7kP3pT+HPW0WFO6+nnlr7/SoshB074I03av4/1qw5qNaxjzkm+i9wVTf66G23wS23uHZHod6DaOX94x9ryltQ4P7/Lr8czj237vs2axa88EL9/q/95fS/N4sWufcv8Fj+Mj/8sHutjz92tT2JJprEEERE8oAK4EpVXRCwfjrQS1UHhthnEFAKrAHygWXAQ6paGpBmLfCkqj4WsO4u4BZV7RwpT/369dMlS5Z4yv+iRYsYlPZjVDZcPMo5cKC74Gza5D7oDfmYFRS4YODii90X05o10Y/XtKm7ANxxR80F4s9/dv/wlZXuS+OGGxZxxhmD2LHD/QLft899AfTu7b4Mfv5z9zh0qPtC37nTtdTetcv9I1dUuC/4igq3fvfu+l/8jTEmkp/9LL49FURkqar2q7M+yUFBB2A9MFBV3wpYPx64WlWPDrHP0cBg4CMgD7gWGOk7xtu+NPuAG1V1TsB+1wGzVLVOZbaIjABGAJSUlPSdN2+ep/yXl5dTVFTksbSNVzzK6Z+17ogj4N//9n6rIFhOjqtaKylx1YOVlaGr56Pt36FDzbqqKnestm3LyckporjY/dIBd3sCah/f/3fwY7i/XQ1DDvv2uWX//hw2b85h40Zh//4cqqqEqqocKivd39XVcmBdVZWgKgfWV1cLRUVC06buJ/mWLW77QQcJVVWwdatLW1Agvh4V4m8ix65dkJNTSXl501rr/fnMyxOqq/1TH8uBMqjWdDFo2tT9ug3cLzhd8PtSUeF/rZolNCEvr6ZGJtR7Gkpg/vzy86vIjVDHrFozA2e04/vvn8fymW3SJFp7BZfn/fsj11T4a178Qa2/5sD/Czwvr4p9+0KXM9pU5k2b1s6jv8Yi+DabavRuivUVnIfAsvlFKmO8Xz+U4Pcl1PsUKt+xysurYv9+V04v32fnn/8NQ4dujnmchXAGDx4cMiio0/IwkQvQAVDg9KD144HlMRznFeDPAc/3AdcFpbkO2BvtWNb7oK54l7NLF2+tcf09CAJbVt93X8OHUva36PYyXn68VFerVlW5pbJSdfPm+vfOKCx0+/70p64V+KmnqpaXq+7c6f4+9VT3986dqt9/X7Ps2KH63HOltd47/7HGjlVdtcpba/hVq1yr7a1b3XC+Awa4v7/7TnXLltDL1197O/bNNzd8romCAtW//a1UN23SiMtXX6nm50f/DN50U+x5KihQXbEi/Gv37++Wb79VHT269ue5WTPXwyY3t/Z5ys93aZcvr8l3uN4k+fnRyxaYx6++Uu3aVfWww1Qff9w99+f18cfr12sjWo+fnBz3moHvy09/Wrc3TXAZRdx719DPiZfzFHiuIr1PofIdajnoILffihU1x+rYUfWss2r3JPH6/sbzu4o06X2wBagCSoLWlwCxNHn6AOgW8HxjHI5pEuTee13DpUiaN3eNkYIbaIk0fCjiwH77yZpG1j/TZE6Oe/02baI3xgs3IqT/NsUTT8AXX7h7n7//vfvF+fbbbikqckuLFjXLQQfBoYfWbvg2dap7Xx97zDW29NIa/vXXXWPC4uKaX1rFxdCqFbRuHXo54ojordpvvtm1l2jIr1J/I7D8fNcgMNLSrRvcdVfkPI0ZA7/7Xex5ys2F0tLwr52X55ZDDnGftcBzcsYZLv9VVbUHvNm71zVsnTs3er7PPDN6b57cXNc49okn3C2y1avhm2/c5753b7e+TRv3PxFr+b02Mm3fvvb7csQR3nrTDB8Od95Zu3FgfUQ7T23burYD774b+X3q2tX7IEz+NlbdurnGrjk57jVUY/tO278/OVNeJzUoUNV9wFJgSNCmIbheCF4dD5QFPF8ch2OaBPHaYn748NrdplTr12I/WEUFrFqV2BbcXkRq5X3nndHnj9i9G7Zujd4y2Wt3tEQPzRuPLnrh1Hda6kTlKVJPjlDvm/+cdOgAf/979M/lmDGRpzU/6SRv5/L3v48eGNdngJ/qajfAmNeujX5evht274Y5c2r3WGjd2tuIqMG89riJ9gPiq6+i5zsvDz74oPa6//3fhg+vnujvqlSMUzAFGC4iN4rIMSIyDXdb4WkAEZkjIoFtA+4QkYtFpJuI9BSRnwMXA08FHHMacIaI3C0i3UXkHlw7hKlJKpOJIJYx9QNb+tdnnPJwx49Hv/2GitRlbfJk94Xpdf6IeNRwJHpo3nh00fPz92Nv1coFN9FGWQwnnnkKFGokRtXI71tpqfeJlF580eXvuONC57t9+/hNfjRkSGzjTkQaFyK4a2NwQBnrOAn+YOroo+tXYxBpxEw/L11Ap093vRxinSckXt9pifyuSnpQoKrzcV0FxwGfAKcC56uqv8NLJ9/ilwc8BnwKvO1Lf4Gq/ingmO8BVwDDfemuA4apalCcZlKlPmPqex2nPNpFIdn99qMJ98s91vkjwPuvhlDdKpM1NG+48noJSkTcr8Jf/tKdm2OPdRdEL4NFJSpPoYQa3MbL7apYa2tyc0PnO56TH73+urdxJ6KNC9GliyvnLbe4i+h//hM6oIz23dClS+3XXbTIVe/XZ2yM3bujT1bm5cK9b5/rKpqo77RoEvpdFaqhQTYt1tCwrkSWM5ahgb0Mn9u8uRsCN9p88KGOFdygKdpQpolW3+GCA/Md/P6+8UZpxNdM5NC8gUIN3VqfIXbDiddntj7D4IYajtrLcQoKVKdO9fYZ95/fSOWMdi4HDvQ2RPaECaGH5G7e3DVmbNXKDT0d6f/Xf76j5SnUsOFPPFF64NiRhkyOddjwnBw3HHI0XoYSB5enwHzH+p0WaQjyWP7n64s0aWhoslwsQwN7/SU7d270iN3rsaL9ikik+g4XXFHhqpFDVVVHm541kUPzBgpVUxFr1XEyeBmd0V8zFelXodeJq1Tj97mM5+RH4W6zhKutCW7HsmiRu38eSzse/3dDfr4bVOjxxyPfsgqVx6eecucv1HvQsWPdWodQvNZg7dzpboVAfL/TAgcFCyeh31WhIoVsWqymoK50Kmd9fmmEi9iDjxUYqYf6tZcKkcob6VdDuNqSyZNLPZUt3HvndQKm4EmDvAr3izTWrlfBn9mGTCoTKU933qnarVv0X4WxvG+xfMa9/G82ZPKjULU+XibKCrXOa01f8C/e0tLSmN6TUK8f/B6sWhV5kqbg989rbVF9Pqv+soWqKfBP7tSpU2zlrw/C1BSk/KKc6sWCgrrSqZyxXjQiXQySOU5BfYUqb7QlPz/8l5j/i6e+Vfz1/WKPVawzTgaLZ1AQLU9ejh3L+xbLZ9zr/2Z9Z3Ksz8UmXBV/fQPKN94ojdstq9NPr1/QGWtwHst752XGy3gFy5FYUBBmsaCgrnQsp9eLhpcv7FD3LtNNYHmjtZk4//zwF6Bw07PGko94fEF70ZALeSKCgoYcpz7vm5fPeEODgnhebKId69e/rl9AuWBBadwC0foGQfUJzmP9P3jjjdKo57uhwXIk4YICa1NgGgWvbRG8TF4UeO+yoa3YEyWwvC+/HPlesdc+6vVprRxLd9KGqs/EU6HEc/rZ+uapPu9brFNxRxIu39G6ZMbSvTMefflD3Rvfvz8+n+eGzC4Z/D55GRch1m6C4XqSBIrnZ8IrCwpMoxGvi4b/WP55DtKVv7zRvsi99FEP7p8daq75cOrTnTQVVNNn+lmo3/sWz894JA292CSyL3/TprF/nkPx2tgz0oXc/z61aBF9ds36BN7JOt+xsKDAmASL5QIcSbgv8kT3rIjnr8tESuYw1l6k+/vWkAuS1wtut26xB0bFxfH5PMc6BkQkeXnxCVTqI9mBgwUFxjQi9e3aF/iLrL7V66moyvSqqir1w1iHk87vW315veB++23sgVFubnxuWXkdsTPahXzRInj//fTv0hwvFhQYk0DxvL8dSbiqapGaX2TxqF5Px+pOcO9rqoexjiRd37f6ivWCG2tgFI9bVvGsQUtm25pUs6DAmARI9v3tcFXVxx1X84ss3arX4ylejdOMN/W54MYSGMXj1ku8L+SNpW1NQ1lQYEwCpOoCHPyLzP/ruSEtsRuDeDVOM94k65dzQ2+9xPNCnu5tROLFggJj4izVF+BQv8ji0RI7ncWrcZrxLlm/nBty6yURF/JMbCMSyIICY+IsHS/A8WyJnY7i1TjNeNeYfjnH+0KeaW1EAiVxqhFjskM6XoD9DcN27QqfprFXr/t/mU6e7Ka2ra52v1yrqjLrnm+68V9wwV1w01WmXsTjzWoKjImzeHWFiqfGMEtkQzWmX67GpCsLCoyJs3S8AGdTl6pMv+ebjjK5Oj3bWFBgTJyl6wU4W7pUgV2kjKkvCwqMSYB0vABb9boxJhpraGhMAvgvwGPGwMknu4Zv997rbhmkujq7sTQMM8YknwUFxiRQul6ArWrdGBOKBQXGJJhdgI0xjYW1KTDGGGMMkKKgQERuEpFVIrJHRJaKyGke9ztVRCpF5N9B64eLiIZYChJTAmOMMSbzJD0oEJFhwDRgEtAHeA94VUQ6RdmvGJgD/D1MkgqgfeCiqnvilW9jjDEm06WipmAMMFtVZ6nqF6p6K1AGjIqy32+BZ4HFYbarqm4MXOKYZ2OMMSbjJTUoEJE8oC/wWtCm14ABEfa7CSgBHopw+GYiskZE1onIX0SkT4MzbIwxxmQRUdXkvZhIB2A9MFBV3wpYPx64WlWPDrHPscAbwMmqukpEHgSGqmqvgDT9gaOAfwEtgNuB84HjVHVFiGOOAEYAlJSU9J03b56n/JeXl1NUVOSxtI2XlTNzZEMZwcqZSbKhjJD6cg4ePHipqvYLXp/WXRJFJB+YD4xV1VXh0qnqYgJuK4jIe8AnwK3AbSHSzwRmAvTr108HDRrkKT+LFi3Ca9rGzMqZObKhjGDlzCTZUEZI33ImOyjYAlThbgUEKgFCtQFoDxwDPCMiz/jW5QAiIpXA+aoafCsCVa0SkSVAt7jl3BhjjMlwSW1ToKr7gKXAkKBNQ3C9EIKtB44Fjg9Ynga+9v0dah9ERIDeuAaMxhhjjPEgFbcPpgBzReRD4F1gJNABd7FHROYAqOp1qrofCB6TYBOwV1X/HbDuAeB9YAVwEO6WQW+i92gwxhhjjE/SgwJVnS8irYFxuNsD/8bdBljjSxJxvIIwWuLaCLQDdgAfA6er6ocNz7ExxhiTHVLS0FBVZwAzwmwbFGXfB4EHg9aNBkbHJ3fGGGNMdkpql8R0JCKbgTVREzptcI0lM52VM3NkQxnByplJsqGMkPpydlbVtsErsz4oiIWILAnVrzPTWDkzRzaUEaycmSQbygjpW06bJdEYY4wxgAUFxhhjjPGxoCA2M1OdgSSxcmaObCgjWDkzSTaUEdK0nNamwBhjjDGA1RQYY4wxxseCAmOMMcYAFhQYY4wxxseCghiISCcReVlEdonIFhF5QkTyUp2vhhARDbGMDEpzrIi8KSK7RWS9iIz3TTqVlkRkmogsEZE9IrI6TJqoZRKRS0VkmYjs9T3+d1IK4FG0copIlzDn99ygdANFZKnvOCuDz38qichxIvKCiHzjO1fLReQnIpITlK7Rnk8vZcyQc9lWRBaKyAbfOfhGRKaLyMFB6RrzuYxaxrQ/l6pqi4cFyAU+AxYBJ+BmdtwAPJnqvDWwXArciJs3wr80C9h+EG5a6z8AvYChwE7gzlTnPUKZngRuxbXuXR1ie9QyAf2BSuA+3PTd9/men5Tq8sVQzi6+83tO0PnNC0hzOLDLd6xjgB8D+4FLU10+X/5+BDwBDAK6Alf4ztW9mXI+PZYxE85la9wEeH2BzsCZwJfAHzLoXHopY1qfy5R/UBrLApwHVAOHBay7BtgDHJTq/DWgXAoMjbB9FPA9tQOFcbhprSXV+Y9StrGEvlhGLRMwH3g9aL83gBdSXa4Yyun/8ukXYd9fACuC1v0GWJzqckXI86PA0kw9n2HKmKnn8jagLMPPZXAZ0/pc2u0D7/oDX6jqNwHrFgL5uKiwMZsm7nbIRyIyMqhqtj/wtqruDli3EDfddZdkZjKOvJSpP/Ba0H4LgQEJz138/UlENonIuyIyNGhbuHL2E5GmyclezA4CtgU8z8TzGVxGv4w5lyLSAbgEeDNgdUadyzBl9EvLc2lBgXftgG+D1m0BqnzbGqvxwDDgLGAe8Dhwb8D2UOX+NmBbY+SlTOHSNKYyl+NqES4Hzgf+DswXkWsC0oQrZxPchC1pRUROAIYDvwpYnVHnM0wZM+Zc+tpPVOB+/e8EbgjYnBHnMkoZ0/pcpmTqZJM+VHViwNNPRCQXd4/uoRRlycSJqm7BBXl+S0SkDfAT4LnU5Kr+RORo4K/AVFX9Y6rzkwjhyphh53I08DPgKODnwFTg/6UyQwkQtozpfi6tpsC7jUBJ0Lo2uAaIG5OfnYT5ADhIRPxlDVXuwG2NkZcyhUvTWMvs9wHQLeB5uHJWkkbT14pId1wj33mqenfQ5ow4n1HKGEqjPJequlFVv1TVP+MulCNE5DDf5ow4l1HKGEranEsLCrxbDBwjIh0D1g0B9gJLU5OlhDge13hyu+/5YuA0ESkISOPvebE6mRmLIy9lWuxbR1Ca9xKeu8Q6HigLeB6unEtUdX+yMhWJiPTAXSwXqOroEEka/fn0UMZQjqeRncsQ/NegfN9joz+XIQSXMZTjSZdzmeqWmY1loaZL4j+APrh78OtpxF0SgYtwXV16AUfguibuAKYFpDkYF7XO86W7BNc6OJ27JB6J+yebgvsyOd635HktE67RUiVwN9AduAfXJSjl3Z5iKOf1wFW4Lk1H4+5j7gNGBxzD3/Vpqi/djb406dKNrSfuXuo8anffahfLZzSdz6fHMmbCubzQV45euEaDFwDLCGhRnwHn0ksZ0/pcpvyD0pgWoBPwF6AC+A7Xtzg/1flqQHnOBT7GNYTZhQt6bgeaBKU7FngLV4NQBjxAGndHxP3i0hBLl1jKhOsj/aXvn/EL4JJUly2Wcvq+fJb5zu33wBLgmhDHGQj8E1frtQoYmeqyBeTtwTBl1Fg/o+l6Pr2UMUPO5Vm4X8Dbgd3AV7iud8UZdC6jljHdz6XNkmiMMcYYwNoUGGOMMcbHggJjjDHGABYUGGOMMcbHggJjjDHGABYUGGOMMcbHggJjjDHGABYUGJOxRGS4iGjAsktEVovI/4rI5SIi9TzuIN/xBsU3xxFfs1ZZEvQa4wJeY10iXsOYdGdBgTGZ7zLcVKznA/fjBkN5AXhdRJqlMmP1cAmuLInwjO/YryTo+MakPZsl0ZjM94mqfh3wfK6ILAAWAI8Ct6YmW/XysaquTsSBVXU9sF5ENifi+MY0BlZTYEwWUjct7/8BPxaRQv96ESkUkV+IyCoR2ed7vE9EIn5XiMjZIvKKiJSJSIWI/FtE7vRNxe1P87KIfBxi38NFpFpERsZaDhHp4qvuHx60vs4tDhE5R0TeE5EdIlIuIstFZHysr2lMJrOgwJjs9Qpu5rZ+ACLSBFiIm3xlGnAe8BvcLYfHohyrK/B34Ee4SWCexY3p/3BAml8Bx4vIiUH7jsCNA/98/YsSmYh0Bf6MG0N+GPBfuImkmifqNY1pjOz2gTHZa63vsb3v8UrgVGCgqr7lW/d3X3vEB0TkF6q6KdSBVPVp/9++BoxvA3nAWBG5V1Wrgb8BK3Hzy3/oS9sUuAF4XlV3xrNwQU7w5WeUqn7vW/ePBL6eMY2S1RQYk738vQ/8rfnPBdYA74lIE/8CvAY0BU4OeyCR9iLyaxFZg5u5bj/wENASOATAFxj8GrhCRA727XoxUOJbn0if+PI0T0SGisghCX49YxolCwqMyV6H+R7LfI+HAJ1xF8/A5UPf9tahDuJrb/Bn3FzyDwFnAD+g5tZBQUDy3wK5wLW+5yOBD1W1TluDePI1tDwH9503F9goIu+LyMBEvq4xjY3dPjAme12Am7N+qe/5d7h77peHSb86zPojcO0SrlXV5/wrReSi4ISq+p2I/AH4fyKyEBiMa8PQUMHfZUUhXrsUKBWRfOAUYALwVxHpoqpb4pAHYxo9CwqMyUIicimusd00Va3wrf4bcClQrqpfxnA4f++F/QHHbwpcHSb9DGAxrhHjDmBeDK8VTq+g52FvdajqXuAfIlKE64FxOGBBgTFYUGBMNjheRNrgGtp1wlXzXwa8DtwTkO55XKO/v4vI48C/fPscgQsgLg4IIAJ9gWuL8LCIVOGCg9HhMqOq7/u6Jp4OPBnmmLG6UUS+AT7G1Vrc4lt/joisBc72vd4rwDdAG1zZNwD/jsPrG5MRLCgwJvMt8D3uATYB/wSuAF5U1QNDBqvqfhE5B7gb103wcFxXwf8Af8U1IKxDVfeJyMXAU8AcYCvwO1zvhlkR8tSH+DUwnAoMBSYBX+MaME4CRgFv4AKc84Cf49pObAXeAa5W1d1xyoMxjZ4EfCcYY0xSiMi7QLWqnuYx/XDcMMRHAmtUtdK3vguuHcQNqjq7gXkSXCPI3wJnqmrHhhzPmMbIagqMMUnha+B3AnAWMAD4YT0O4x+uuV6TOUVxHzDR9/f6BBzfmLRnQYExJlnaA+8B24FJqvrnGPZ9GdfNMZF+i2tsCWFulRiT6ez2gTHGGGMAG7zIGGOMMT4WFBhjjDEGsKDAGGOMMT4WFBhjjDEGsKDAGGOMMT7/HyOWgyQhni+CAAAAAElFTkSuQmCC\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
@@ -513,33 +355,15 @@
},
{
"cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
"source": [
"import qiskit.tools.jupyter\n",
"%qiskit_copyright"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index fe92296b4a..cadd567ae9 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -75,7 +75,6 @@ def _default_experiment_options(cls) -> Options:
options.delays = None
options.unit = "s"
options.conversion_factor = 1
- options.osc_freq = 0.0
options.num_echoes = 1
return options
@@ -180,7 +179,7 @@ def circuits(self) -> List[QuantumCircuit]:
if self.experiment_options.num_echoes % 2 == 1:
circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
else:
- circ.rx(-np.pi / 2, 0) # X90 again since the num of echoes is even
+ circ.rx(-np.pi / 2, 0) # X(-90) again since the num of echoes is even
circ.measure(0, 0) # measure
circ.metadata = {
"experiment_type": self._type,
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 4f5b5c2bdf..e5817f834e 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -16,6 +16,8 @@
import numpy as np
from numpy import isclose
+from typing import List
+from qiskit import QiskitError
from qiskit.providers import BackendV1
from qiskit.providers.models import QasmBackendConfiguration
from qiskit.result import Result
@@ -74,19 +76,51 @@ def _default_options(cls):
"""Default options of the test backend."""
return Options(shots=1024)
- def _qubit_initialization(self) -> dict:
- if self._initialization_error is not None and (
- self._rng.random() < self._initialization_error[0]
- ):
- return {"XY plain": False, "ZX plain": True, "Theta": np.pi}
+ def _qubit_initialization(self, nqubits: int) -> List[dict]:
+ """
+ Initialize the list of qubits state. If initialization error is provided to the backend it will
+ use it to determine the initialized state.
+ Args:
+ nqubits(int): the number of qubits in the circuit.
+
+ Returns:
+ List[dict]: A list of dictionary which each dictionary contain the qubit state in the format
+ {"XY plain": (bool), "ZX plain": (bool), "Theta": float}
+ """
+ qubits_sates = [0 for _ in range(nqubits)]
+ # Making an array with the initialization error for each qubit.
+ initialization_error = self._initialization_error
+ if isinstance(initialization_error, int) or initialization_error is None:
+ initialization_error_arr = [initialization_error for _ in range(nqubits)]
+ elif isinstance(initialization_error, list):
+ if len(initialization_error) == 1:
+ initialization_error_arr = [initialization_error[0] for _ in range(nqubits)]
+ elif len(initialization_error) == nqubits:
+ initialization_error_arr = [err for err in initialization_error]
+ else:
+ raise QiskitError(
+ f"The length of the list {initialization_error} isn't the same as the number "
+ "of qubits."
+ )
else:
- return {
- "XY plain": False,
- "ZX plain": True,
- "Theta": 0,
- }
+ raise QiskitError(
+ f"Initialization error type isn't a list or int"
+ )
- def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
+ for qubit in range(nqubits):
+ if initialization_error_arr[qubit] is not None and (
+ self._rng.random() < initialization_error_arr[qubit]
+ ):
+ qubits_sates[qubit] = {"XY plain": False, "ZX plain": True, "Theta": np.pi}
+ else:
+ qubits_sates[qubit] = {
+ "XY plain": False,
+ "ZX plain": True,
+ "Theta": 0,
+ }
+ return qubits_sates
+
+ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float, frequency: float) -> dict:
"""
Apply delay gate to the qubit. From the delay time we can calculate the probability
that an error has accrued.
@@ -94,6 +128,7 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
qubit_state(dict): The state of the qubit before operating the gate.
delay(float): The time in which there are no operation on the qubit.
t2hahn(float): The T2 parameter of the backhand for probability calculation.
+ frequency(float): The frequency of the qubit for phase calculation.
Returns:
dict: The state of the qubit after operating the gate.
@@ -114,7 +149,7 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float) -> dict:
"Theta": np.pi,
}
else:
- phase = self._frequency[0] * delay
+ phase = frequency * delay
new_theta = qubit_state["Theta"] + phase
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {"XY plain": True, "ZX plain": False, "Theta": new_theta}
@@ -158,8 +193,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
else:
- print("Error - This angle isn't supported. We only support multipication of pi/2")
- print("The angle is:" + str(angle))
+ raise QiskitError(f"Error - the angle {angle} isn't supported. We only support multiplication of pi/2")
else:
if isclose(angle, np.pi):
new_theta = qubit_state["Theta"] + np.pi
@@ -188,8 +222,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
else:
- print("Error - This angle isn't supported. We only support multiplication of pi/2")
- print("The angle is:" + str(angle))
+ raise QiskitError(f"Error - The angle {angle} isn't supported. We only support multiplication of pi/2")
return new_qubit_state
def _measurement_gate(self, qubit_state: dict) -> int:
@@ -242,12 +275,13 @@ def run(self, run_input, **options):
"results": [],
}
for circ in run_input:
+ nqubits = circ.num_qubits
qubit_indices = {bit: idx for idx, bit in enumerate(circ.qubits)}
clbit_indices = {bit: idx for idx, bit in enumerate(circ.clbits)}
counts = dict()
for _ in range(shots):
- qubit_state = self._qubit_initialization() # for parrallel need to make an array
+ qubit_state = self._qubit_initialization(nqubits=nqubits) # for parallel need to make an array
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
@@ -255,12 +289,18 @@ def run(self, run_input, **options):
# The noise will only be applied if we are in the XY plain.
if op.name == "delay":
delay = op.params[0]
+ if qubit >= len(self._t2hahn):
+ print(f"The length of T2 is {len(self._t2hahn)} and the index qubit is {qubit}")
t2hahn = self._t2hahn[qubit] * self._conversion_factor
- qubit_state = self._delay_gate(qubit_state, delay, t2hahn)
+ freq = self._frequency[qubit]
+ qubit_state[qubit] = self._delay_gate(qubit_state=qubit_state[qubit],
+ delay=delay, t2hahn=t2hahn,
+ frequency=freq,
+ )
elif op.name == "rx":
- qubit_state = self._rx_gate(qubit_state, op.params[0])
+ qubit_state[qubit] = self._rx_gate(qubit_state[qubit], op.params[0])
elif op.name == "measure":
- meas_res = self._measurement_gate(qubit_state)
+ meas_res = self._measurement_gate(qubit_state[qubit])
clbit = clbit_indices[cargs[0]]
clbits[clbit] = meas_res
@@ -268,10 +308,10 @@ def run(self, run_input, **options):
for clbit in clbits[::-1]:
clstr = clstr + str(clbit)
- if clstr in counts:
- counts[clstr] += 1
- else:
- counts[clstr] = 1
+ if clstr in counts:
+ counts[clstr] += 1
+ else:
+ counts[clstr] = 1
result["results"].append(
{
"shots": shots,
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 0a4b807b31..4794e03eef 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -16,6 +16,7 @@
import numpy as np
from qiskit.utils import apply_prefix
+from qiskit_experiments.framework import ParallelExperiment
from qiskit.test import QiskitTestCase
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
@@ -74,6 +75,53 @@ def test_t2hahn_run_end2end(self):
self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
self.assertEqual(fitval.unit, "s")
+ def test_t2hahn_parallel(self):
+ """
+ Test parallel experiments of T2Hahn using a simulator.
+ """
+ t2hahn = [30, 25]
+ estimated_freq = [0.1, 0.12]
+ delays = [list(range(1, 60)), list(range(1, 50))]
+
+ osc_freq = [0.11, 0.11]
+
+ exp0 = T2Hahn(0, delays[0])
+ exp2 = T2Hahn(2, delays[1])
+
+ exp0.set_analysis_options(
+ p0={"amp": 0.5, "tau": t2hahn[0], "base": 0.5}, plot=True
+ )
+ exp2.set_analysis_options(
+ p0={"amp": 0.5, "tau": t2hahn[1], "base": 0.5}, plot=True
+ )
+
+ par_exp = ParallelExperiment([exp0, exp2])
+
+ p0 = {
+ "A": [0.5, None, 0.5],
+ "T2": [t2hahn[0], None, t2hahn[1]],
+ "frequency": [osc_freq[0], None, osc_freq[1]],
+ "B": [0.5, None, 0.5],
+ }
+
+ backend = T2HahnBackend(
+ t2hahn=p0["T2"],
+ frequency=p0["frequency"],
+ initialization_error=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ conversion_factor=1,
+ )
+ expdata = par_exp.run(backend=backend, shots=1024).block_for_results()
+
+ for i in range(2):
+ res_t2 = expdata.child_data(i).analysis_results("T2")
+
+ fitval = res_t2.value
+ self.assertEqual(res_t2.quality, "good")
+ self.assertAlmostEqual(fitval.value, t2hahn[i], delta=3)
+ self.assertEqual(fitval.unit, "s")
+
def test_t2hahn_concat_2_experiments(self):
"""
Concatenate the data from 2 separate experiments
From 2b87ab54eecb70d6436874bede3150b081b87b9b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 8 Dec 2021 17:10:10 +0200
Subject: [PATCH 61/93] Changed that the analysis class will be passed to
constructor
---
.../library/characterization/t2hahn.py | 3 +--
test/test_t2hahn.py | 17 +++++++----------
2 files changed, 8 insertions(+), 12 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index cadd567ae9..f0863bd3f3 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -59,7 +59,6 @@ class T2Hahn(BaseExperiment):
:doc:`/tutorials/t2hahn_characterization`
"""
- __analysis_class__ = T2HahnAnalysis
@classmethod
def _default_experiment_options(cls) -> Options:
@@ -100,7 +99,7 @@ def __init__(
QiskitError : Error for invalid input.
"""
# Initialize base experiment
- super().__init__([qubit], backend=backend)
+ super().__init__([qubit], analysis=T2HahnAnalysis(), backend=backend)
# Set experiment options
self.set_experiment_options(delays=delays, unit=unit, num_echoes=num_echoes)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 4794e03eef..887eedd7ef 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -64,7 +64,7 @@ def test_t2hahn_run_end2end(self):
)
for _ in [default_p0, dict()]:
- exp.set_analysis_options(
+ exp.analysis.set_options(
p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
)
expdata = exp.run(backend=backend, shots=1000)
@@ -80,18 +80,16 @@ def test_t2hahn_parallel(self):
Test parallel experiments of T2Hahn using a simulator.
"""
t2hahn = [30, 25]
- estimated_freq = [0.1, 0.12]
delays = [list(range(1, 60)), list(range(1, 50))]
-
osc_freq = [0.11, 0.11]
exp0 = T2Hahn(0, delays[0])
exp2 = T2Hahn(2, delays[1])
- exp0.set_analysis_options(
+ exp0.analysis.set_options(
p0={"amp": 0.5, "tau": t2hahn[0], "base": 0.5}, plot=True
)
- exp2.set_analysis_options(
+ exp2.analysis.set_options(
p0={"amp": 0.5, "tau": t2hahn[1], "base": 0.5}, plot=True
)
@@ -135,7 +133,7 @@ def test_t2hahn_concat_2_experiments(self):
dt_factor = 1
exp0 = T2Hahn(qubit, delays0, unit=unit)
- exp0.set_analysis_options(
+ exp0.analysis.set_options(
p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
)
backend = T2HahnBackend(
@@ -152,16 +150,15 @@ def test_t2hahn_concat_2_experiments(self):
expdata0.block_for_results()
res_t2_0 = expdata0.analysis_results("T2")
-
# second experiment
delays1 = list(range(2, 65, 2))
exp1 = T2Hahn(qubit, delays1, unit=unit)
- exp1.set_analysis_options(
+ exp1.analysis.set_options(
p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
)
- expdata1 = exp1.run(backend=backend, analysis=False, shots=1000).block_for_results()
+ expdata1 = exp1.run(backend=backend, analysis=None, shots=1000).block_for_results()
expdata1.add_data(expdata0.data())
- exp1.run_analysis(expdata1).block_for_results()
+ exp1.analysis.run(expdata1)
res_t2_1 = expdata1.analysis_results("T2")
From 3c7627e2aff8c896180e8cc9262ac6bd90803972 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 8 Dec 2021 17:39:06 +0200
Subject: [PATCH 62/93] Added release notes
---
.../notes/t2-hahn-experiment-84fb05d71b5ef250.yaml | 7 +++++++
1 file changed, 7 insertions(+)
create mode 100644 releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
diff --git a/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml b/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
new file mode 100644
index 0000000000..e3f9cf519d
--- /dev/null
+++ b/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
@@ -0,0 +1,7 @@
+---
+features:
+ - |
+ Hahn Echo experiment is added to the library.
+ This experiment estimates dephasing noise T2.
+
+ See experiment class documentation for details.
From 25a09c02b22a364703ee14b7dcbdc56d99deedd6 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 13:57:59 +0200
Subject: [PATCH 63/93] Changed the code dor recent changes (added config,
removed unit, etc)
Add Hahn analysis class to the lists under __init__ both in "characterization" and "characterization\analysis".
Removed the support in units.
Added experiment.config test and serialization.
---
docs/tutorials/t2hahn_characterization.ipynb | 183 ++++++++++++++----
.../library/characterization/__init__.py | 4 +
.../characterization/analysis/__init__.py | 1 +
.../analysis/t2hahn_analysis.py | 11 --
.../library/characterization/t2hahn.py | 65 +++----
qiskit_experiments/test/t2hahn_backend.py | 8 +-
test/test_t2hahn.py | 117 +++++------
7 files changed, 247 insertions(+), 142 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index deb8b33c70..04469cc7bc 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -71,19 +71,11 @@
"output_type": "stream",
"text": [
" ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐┌─────────┐┌─┐\n",
- "q_0: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
" └─────────┘└─────────────────┘└───────┘└─────────────────┘└─────────┘└╥┘\n",
"c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
" 0 \n"
]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\framework\\base_experiment.py:82: UserWarning: Defining a default BaseAnalysis class for an experiment using the __analysis_class__ attribute is deprecated as of 0.2.0. Use the `analysis` kwarg of BaseExperiment.__init__ to specify a default analysis class.\n",
- " warnings.warn(\n"
- ]
}
],
"source": [
@@ -105,14 +97,10 @@
"metadata": {},
"outputs": [
{
- "ename": "SyntaxError",
- "evalue": "invalid syntax (t2hahn_backend.py, line 83)",
- "output_type": "error",
- "traceback": [
- "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
- " File \u001b[0;32m\"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\"\u001b[0m, line \u001b[0;32m3418\u001b[0m, in \u001b[0;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n",
- "\u001b[1;36m File \u001b[1;32m\"<ipython-input-4-b23a53a6ddf0>\"\u001b[1;36m, line \u001b[1;32m1\u001b[1;36m, in \u001b[1;35m<module>\u001b[1;36m\u001b[0m\n\u001b[1;33m from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\u001b[0m\n",
- "\u001b[1;36m File \u001b[1;32m\"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\test\\t2hahn_backend.py\"\u001b[1;36m, line \u001b[1;32m83\u001b[0m\n\u001b[1;33m qubits_sates[qubit] {\"XY plain\": False, \"ZX plain\": True, \"Theta\": np.pi}\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1e-06\n"
]
}
],
@@ -147,11 +135,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"dt_factor = apply_prefix(1, unit)\n",
"\n",
@@ -166,9 +165,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527131e-03 3.03978270e-03 5.77293057e-07]\n",
+ "- χ²: 0.7488240853426228\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 1.9828300679956625e-05 ± 5.772930568055365e-07 s\n",
+ "- χ²: 0.7488240853426228\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
"source": [
"# Print results\n",
"for result in expdata1.analysis_results():\n",
@@ -185,9 +206,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"exp_with_p0 = T2Hahn(qubit, delays)\n",
"exp_with_p0.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
@@ -200,9 +232,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.78978431e-01 5.02409209e-01 2.01192655e-05] ± [5.09032092e-03 3.07792331e-03 5.78387141e-07]\n",
+ "- χ²: 0.5509343846546172\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 2.011926549231594e-05 ± 5.783871411742618e-07 s\n",
+ "- χ²: 0.5509343846546172\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
"source": [
"# Print results\n",
"for result in expdata_with_p0.analysis_results():\n",
@@ -220,9 +274,36 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
+ " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
+ "c: 1/══════════════════════════════════════════════════════════»\n",
+ " »\n",
+ "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
+ "« q: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
+ "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
+ "«c: 1/══════════════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
+ "« q: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
+ "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
+ "«c: 1/════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌─────────────────┐┌──────────┐┌─┐\n",
+ "« q: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └─────────────────┘└──────────┘└╥┘\n",
+ "«c: 1/════════════════════════════════╩═\n",
+ "« 0 \n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"# set the computation units to microseconds\n",
@@ -247,11 +328,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"\n",
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
@@ -293,9 +385,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ┌───┐┌─────────────────┐┌─────────┐ ░ ┌───┐ ░ ┌─┐\n",
+ " q: ┤ H ├┤ Delay(1e-06[s]) ├┤ Rz(π/5) ├─░─┤ H ├─░─┤M├\n",
+ " └───┘└─────────────────┘└─────────┘ ░ └───┘ ░ └╥┘\n",
+ "c: 1/═══════════════════════════════════════════════╩═\n",
+ " 0 \n"
+ ]
+ }
+ ],
"source": [
"import qiskit\n",
"from qiskit_experiments.library import T2Ramsey\n",
@@ -355,11 +459,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import qiskit.tools.jupyter\n",
"%qiskit_copyright"
diff --git a/qiskit_experiments/library/characterization/__init__.py b/qiskit_experiments/library/characterization/__init__.py
index b845af4d3a..7fc9e63821 100644
--- a/qiskit_experiments/library/characterization/__init__.py
+++ b/qiskit_experiments/library/characterization/__init__.py
@@ -25,6 +25,7 @@
T1
T2Ramsey
+ T2Hahn
QubitSpectroscopy
CrossResonanceHamiltonian
EchoedCrossResonanceHamiltonian
@@ -52,6 +53,7 @@
T1Analysis
T2RamseyAnalysis
+ T2HahnAnalysis
CrossResonanceHamiltonianAnalysis
DragCalAnalysis
FineHalfAngleAnalysis
@@ -69,6 +71,7 @@
RamseyXYAnalysis,
T2RamseyAnalysis,
T1Analysis,
+ T2HahnAnalysis,
CrossResonanceHamiltonianAnalysis,
ReadoutAngleAnalysis,
)
@@ -77,6 +80,7 @@
from .qubit_spectroscopy import QubitSpectroscopy
from .ef_spectroscopy import EFSpectroscopy
from .t2ramsey import T2Ramsey
+from .t2hahn import T2Hahn
from .cr_hamiltonian import CrossResonanceHamiltonian, EchoedCrossResonanceHamiltonian
from .rabi import Rabi, EFRabi
from .half_angle import HalfAngle
diff --git a/qiskit_experiments/library/characterization/analysis/__init__.py b/qiskit_experiments/library/characterization/analysis/__init__.py
index e2acdf3241..1e016922c3 100644
--- a/qiskit_experiments/library/characterization/analysis/__init__.py
+++ b/qiskit_experiments/library/characterization/analysis/__init__.py
@@ -19,6 +19,7 @@
from .fine_frequency_analysis import FineFrequencyAnalysis
from .remsey_xy_analysis import RamseyXYAnalysis
from .t2ramsey_analysis import T2RamseyAnalysis
+from .t2hahn_analysis import T2HahnAnalysis
from .t1_analysis import T1Analysis
from .cr_hamiltonian_analysis import CrossResonanceHamiltonianAnalysis
from .readout_angle_analysis import ReadoutAngleAnalysis
diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index bab825c61b..bc83ecd84f 100644
--- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -43,17 +43,6 @@ def _default_options(cls) -> Options:
return options
- def _generate_fit_guesses(
- self, user_opt: curve.FitOptions
- ) -> Union[curve.FitOptions, List[curve.FitOptions]]:
- """Apply conversion factor to tau."""
- conversion_factor = self._experiment_options()["conversion_factor"]
-
- if user_opt.p0["tau"] is not None:
- user_opt.p0["tau"] *= conversion_factor
-
- return super()._generate_fit_guesses(user_opt)
-
def _evaluate_quality(self, fit_data: curve.FitData) -> Union[str, None]:
"""Algorithmic criteria for whether the fit is good or bad.
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index f0863bd3f3..a29c529f0a 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -17,7 +17,6 @@
from typing import List, Optional, Union
import numpy as np
-from qiskit.utils import apply_prefix
from qiskit import QuantumCircuit, QiskitError
from qiskit.providers.backend import Backend
from qiskit.test.mock import FakeBackend
@@ -58,6 +57,8 @@ class T2Hahn(BaseExperiment):
# section: tutorial
:doc:`/tutorials/t2hahn_characterization`
+ # section: analysis_ref
+ :py:class:`T2HahnAnalysis`
"""
@classmethod
@@ -66,14 +67,10 @@ def _default_experiment_options(cls) -> Options:
Experiment Options:
delays (Iterable[float]): Delay times of the experiments.
- unit (str): Unit of the delay times. Supported units are
- 's', 'ms', 'us', 'ns', 'ps', 'dt'.
"""
options = super()._default_experiment_options()
options.delays = None
- options.unit = "s"
- options.conversion_factor = 1
options.num_echoes = 1
return options
@@ -83,7 +80,6 @@ def __init__(
delays: Union[List[float], np.array],
num_echoes: int = 1,
backend: Optional[Backend] = None,
- unit: str = "s",
):
"""
Initialize the T2 - Hahn Echo class
@@ -92,8 +88,8 @@ def __init__(
qubit: the qubit whose T2 is to be estimated
delays: Total delay times of the experiments.
backend: Optional, the backend to run the experiment on.
- unit: Optional, time unit of `delays`.
- Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.
+ num_echoes: The number of echoes to preform.
+ backend: Optional, the backend to run the experiment on..
Raises:
QiskitError : Error for invalid input.
@@ -102,7 +98,7 @@ def __init__(
super().__init__([qubit], analysis=T2HahnAnalysis(), backend=backend)
# Set experiment options
- self.set_experiment_options(delays=delays, unit=unit, num_echoes=num_echoes)
+ self.set_experiment_options(delays=delays, num_echoes=num_echoes)
self._verify_parameters()
def _verify_parameters(self):
@@ -131,50 +127,51 @@ def _set_backend(self, backend: Backend):
timing_constraints=timing_constraints, scheduling_method=scheduling_method
)
- # Set conversion factor
- if self.experiment_options.unit == "dt":
- try:
- dt_factor = getattr(self.backend.configuration(), "dt")
- conversion_factor = dt_factor
- except AttributeError as no_dt:
- raise AttributeError("Dt parameter is missing in backend configuration") from no_dt
- elif self.experiment_options.unit != "s":
- conversion_factor = apply_prefix(1, self.experiment_options.unit)
- else:
- conversion_factor = 1
- self.set_experiment_options(conversion_factor=conversion_factor)
-
def circuits(self) -> List[QuantumCircuit]:
"""
- Return a list of experiment circuits
+ Return a list of experiment circuits.
+
+ Each circuit consist with RX(π/2) followed by a sequence of delay gate, RX(π) for echo and delay gate again.
+ The sequence repeats for the number of echoes and finish with RX(±π/2).
Returns:
- The experiment circuits
+ The experiment circuits.
Raises:
ValueError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
- if self.backend:
- self._set_backend(self.backend)
- prefactor = self.experiment_options.conversion_factor
- if prefactor is None:
- raise ValueError("Conversion factor is not set.")
+ if self.backend and hasattr(self.backend.configuration(), "dt"):
+ dt_unit = True
+ dt_factor = self.backend.configuration().dt
+ else:
+ dt_unit = False
+
circuits = []
for delay_gate in np.asarray(self.experiment_options.delays, dtype=float):
- total_delay = delay_gate * (self.experiment_options.num_echoes * 2)
+ if dt_unit:
+ delay_dt = round(delay_gate / dt_factor)
+ real_delay_in_sec = delay_dt * dt_factor
+ else:
+ real_delay_in_sec = delay_gate
- delay_gate = np.round(delay_gate, decimals=12)
+ total_delay = real_delay_in_sec * (self.experiment_options.num_echoes * 2)
circ = QuantumCircuit(1, 1)
# First X rotation in 90 degrees
circ.rx(np.pi / 2, 0) # Bring to qubits to X Axis
for _ in range(self.experiment_options.num_echoes):
- circ.delay(delay_gate, 0, self.experiment_options.unit)
- circ.rx(np.pi, 0)
- circ.delay(delay_gate, 0, self.experiment_options.unit)
+ if dt_unit:
+ circ.delay(delay_dt, 0, "dt")
+ circ.rx(np.pi, 0)
+ circ.delay(delay_dt, 0, "dt")
+ else:
+ circ.delay(delay_gate, 0, "s")
+ circ.rx(np.pi, 0)
+ circ.delay(delay_gate, 0, "s")
+
if self.experiment_options.num_echoes % 2 == 1:
circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
else:
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index e5817f834e..73bf22998b 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -40,12 +40,10 @@ def __init__(
initialization_error=None,
readout0to1=None,
readout1to0=None,
- conversion_factor=1,
):
"""
Initialize the T2Hahn backend
"""
- conversion_factor_in_ns = conversion_factor * 1e9 if conversion_factor is not None else None
configuration = QasmBackendConfiguration(
backend_name="T2Hahn_simulator",
backend_version="0",
@@ -59,7 +57,6 @@ def __init__(
memory=False,
max_shots=int(1e6),
coupling_map=None,
- dt=conversion_factor_in_ns,
)
self._t2hahn = t2hahn
@@ -67,7 +64,6 @@ def __init__(
self._initialization_error = initialization_error
self._readout0to1 = readout0to1
self._readout1to0 = readout1to0
- self._conversion_factor = conversion_factor
self._rng = np.random.default_rng(seed=SEED)
super().__init__(configuration)
@@ -289,9 +285,7 @@ def run(self, run_input, **options):
# The noise will only be applied if we are in the XY plain.
if op.name == "delay":
delay = op.params[0]
- if qubit >= len(self._t2hahn):
- print(f"The length of T2 is {len(self._t2hahn)} and the index qubit is {qubit}")
- t2hahn = self._t2hahn[qubit] * self._conversion_factor
+ t2hahn = self._t2hahn[qubit]
freq = self._frequency[qubit]
qubit_state[qubit] = self._delay_gate(qubit_state=qubit_state[qubit],
delay=delay, t2hahn=t2hahn,
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 887eedd7ef..79914150e7 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -13,17 +13,17 @@
"""
Test T2Hahn experiment
"""
-import numpy as np
-from qiskit.utils import apply_prefix
+from test.base import QiskitExperimentsTestCase
+import numpy as np
from qiskit_experiments.framework import ParallelExperiment
-from qiskit.test import QiskitTestCase
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
+from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
import unittest
-class TestT2Hahn(QiskitTestCase):
+class TestT2Hahn(QiskitExperimentsTestCase):
"""Test T2Hahn experiment"""
__tolerance__ = 0.1
@@ -32,48 +32,38 @@ def test_t2hahn_run_end2end(self):
"""
Run the T2Hahn backend on all possible units
"""
- for unit in ["s"]:
- if unit in ("s", "dt"):
- dt_factor = 1
- else:
- dt_factor = apply_prefix(1, unit)
- osc_freq = 0.1 / dt_factor
- estimated_t2hahn = 20
- # Set up the circuits
- qubit = 0
- if unit == "dt": # dt requires integer values for delay
- delays = list(range(1, 46))
- else:
- delays = np.append(
- (np.linspace(1.0, 15.0, num=15)).astype(float),
- (np.linspace(16.0, 45.0, num=59)).astype(float),
- )
- exp = T2Hahn(qubit=qubit, delays=delays, unit=unit)
- default_p0 = {
- "A": 0.5,
- "T2": estimated_t2hahn,
- "B": 0.5,
- }
- backend = T2HahnBackend(
- t2hahn=[estimated_t2hahn],
- frequency=[osc_freq],
- initialization_error=[0.0],
- readout0to1=[0.02],
- readout1to0=[0.02],
- conversion_factor=dt_factor,
- )
+ osc_freq = 0.1
+ estimated_t2hahn = 20
+ # Set up the circuits
+ qubit = 0
+ delays = np.append(
+ (np.linspace(1.0, 15.0, num=15)).astype(float),
+ (np.linspace(16.0, 45.0, num=59)).astype(float),
+ )
+ exp = T2Hahn(qubit=qubit, delays=delays)
+ default_p0 = {
+ "A": 0.5,
+ "T2": estimated_t2hahn,
+ "B": 0.5,
+ }
+ backend = T2HahnBackend(
+ t2hahn=[estimated_t2hahn],
+ frequency=[osc_freq],
+ initialization_error=[0.0],
+ readout0to1=[0.02],
+ readout1to0=[0.02],
+ )
- for _ in [default_p0, dict()]:
- exp.analysis.set_options(
- p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
- )
- expdata = exp.run(backend=backend, shots=1000)
- expdata.block_for_results() # Wait for job/analysis to finish.
- result = expdata.analysis_results("T2")
- fitval = result.value
- self.assertEqual(result.quality, "good")
- self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
- self.assertEqual(fitval.unit, "s")
+ for _ in [default_p0, dict()]:
+ exp.analysis.set_options(
+ p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True
+ )
+ expdata = exp.run(backend=backend, shots=1000)
+ expdata.block_for_results() # Wait for job/analysis to finish.
+ result = expdata.analysis_results("T2")
+ fitval = result.value
+ self.assertEqual(result.quality, "good")
+ self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
def test_t2hahn_parallel(self):
"""
@@ -108,7 +98,6 @@ def test_t2hahn_parallel(self):
initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
- conversion_factor=1,
)
expdata = par_exp.run(backend=backend, shots=1024).block_for_results()
@@ -118,23 +107,20 @@ def test_t2hahn_parallel(self):
fitval = res_t2.value
self.assertEqual(res_t2.quality, "good")
self.assertAlmostEqual(fitval.value, t2hahn[i], delta=3)
- self.assertEqual(fitval.unit, "s")
def test_t2hahn_concat_2_experiments(self):
"""
Concatenate the data from 2 separate experiments
"""
- unit = "s"
estimated_t2hahn = 30
# First experiment
qubit = 0
delays0 = list(range(1, 60, 2))
osc_freq = 0.08
- dt_factor = 1
- exp0 = T2Hahn(qubit, delays0, unit=unit)
+ exp0 = T2Hahn(qubit, delays0)
exp0.analysis.set_options(
- p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
+ p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True
)
backend = T2HahnBackend(
t2hahn=[estimated_t2hahn],
@@ -142,7 +128,6 @@ def test_t2hahn_concat_2_experiments(self):
initialization_error=[0.0],
readout0to1=[0.02],
readout1to0=[0.02],
- conversion_factor=1,
)
# run circuits
@@ -152,9 +137,9 @@ def test_t2hahn_concat_2_experiments(self):
res_t2_0 = expdata0.analysis_results("T2")
# second experiment
delays1 = list(range(2, 65, 2))
- exp1 = T2Hahn(qubit, delays1, unit=unit)
+ exp1 = T2Hahn(qubit, delays1)
exp1.analysis.set_options(
- p0={"amp": 0.5, "tau": estimated_t2hahn / dt_factor, "base": 0.5}, plot=True
+ p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True
)
expdata1 = exp1.run(backend=backend, analysis=None, shots=1000).block_for_results()
expdata1.add_data(expdata0.data())
@@ -164,11 +149,10 @@ def test_t2hahn_concat_2_experiments(self):
fitval = res_t2_1.value
self.assertEqual(res_t2_1.quality, "good")
- self.assertAlmostEqual(res_t2_1.value.value, estimated_t2hahn, delta=3)
- self.assertEqual(fitval.unit, "s")
+ self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
self.assertAlmostEqual(
- res_t2_1.value.value,
+ fitval.value,
estimated_t2hahn,
delta=TestT2Hahn.__tolerance__ * res_t2_1.value.value,
)
@@ -176,6 +160,25 @@ def test_t2hahn_concat_2_experiments(self):
self.assertLessEqual(res_t2_1.value.stderr, res_t2_0.value.stderr)
self.assertEqual(len(expdata1.data()), len(delays0) + len(delays1))
+ def test_experiment_config(self):
+ """Test converting to and from config works"""
+ exp = T2Hahn(0, [1, 2, 3, 4, 5])
+ loaded_exp = T2Hahn.from_config(exp.config())
+ self.assertNotEqual(exp, loaded_exp)
+ self.assertTrue(self.experiments_equiv(exp, loaded_exp))
+
+ def test_roundtrip_serializable(self):
+ """Test round trip JSON serialization"""
+ exp = T2Hahn(0, [1, 2, 3, 4, 5])
+ self.assertRoundTripSerializable(exp, self.experiments_equiv)
+
+ def test_analysis_config(self):
+ """ "Test converting analysis to and from config works"""
+ analysis = T2HahnAnalysis()
+ loaded = T2HahnAnalysis.from_config(analysis.config())
+ self.assertNotEqual(analysis, loaded)
+ self.assertEqual(analysis.config(), loaded.config())
+
if __name__ == "__main__":
unittest.main()
From c567fb2afe8ca0f7baca01caeb1cfe9d4452a611 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 14:02:53 +0200
Subject: [PATCH 64/93] Passed black and pylint
---
.../analysis/t2hahn_analysis.py | 8 +++++--
.../library/characterization/t2hahn.py | 4 ++--
test/test_t2hahn.py | 21 ++++---------------
3 files changed, 12 insertions(+), 21 deletions(-)
diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index bc83ecd84f..03df20c534 100644
--- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -12,7 +12,7 @@
"""
T2 Hahn echo Analysis class.
"""
-from typing import Union, List
+from typing import Union
import qiskit_experiments.curve_analysis as curve
from qiskit_experiments.data_processing import DataProcessor, Probability
@@ -35,7 +35,11 @@ def _default_options(cls) -> Options:
options.data_processor = DataProcessor(
input_key="counts", data_actions=[Probability(outcome="0")]
)
- options.p0 = {"amp": 0.5, "tau": 0.000001, "base": 0.5} # The analysis will not work without initial guess
+ options.p0 = {
+ "amp": 0.5,
+ "tau": 0.000001,
+ "base": 0.5,
+ } # The analysis will not work without initial guess
options.xlabel = "Delay"
options.ylabel = "P(0)"
options.xval_unit = "s"
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index a29c529f0a..f7289c9f49 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -131,7 +131,8 @@ def circuits(self) -> List[QuantumCircuit]:
"""
Return a list of experiment circuits.
- Each circuit consist with RX(π/2) followed by a sequence of delay gate, RX(π) for echo and delay gate again.
+ Each circuit consist with RX(π/2) followed by a sequence of delay gate,
+ RX(π) for echo and delay gate again.
The sequence repeats for the number of echoes and finish with RX(±π/2).
Returns:
@@ -147,7 +148,6 @@ def circuits(self) -> List[QuantumCircuit]:
else:
dt_unit = False
-
circuits = []
for delay_gate in np.asarray(self.experiment_options.delays, dtype=float):
if dt_unit:
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 79914150e7..180af9a823 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -20,7 +20,6 @@
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-import unittest
class TestT2Hahn(QiskitExperimentsTestCase):
@@ -76,12 +75,8 @@ def test_t2hahn_parallel(self):
exp0 = T2Hahn(0, delays[0])
exp2 = T2Hahn(2, delays[1])
- exp0.analysis.set_options(
- p0={"amp": 0.5, "tau": t2hahn[0], "base": 0.5}, plot=True
- )
- exp2.analysis.set_options(
- p0={"amp": 0.5, "tau": t2hahn[1], "base": 0.5}, plot=True
- )
+ exp0.analysis.set_options(p0={"amp": 0.5, "tau": t2hahn[0], "base": 0.5}, plot=True)
+ exp2.analysis.set_options(p0={"amp": 0.5, "tau": t2hahn[1], "base": 0.5}, plot=True)
par_exp = ParallelExperiment([exp0, exp2])
@@ -119,9 +114,7 @@ def test_t2hahn_concat_2_experiments(self):
osc_freq = 0.08
exp0 = T2Hahn(qubit, delays0)
- exp0.analysis.set_options(
- p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True
- )
+ exp0.analysis.set_options(p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True)
backend = T2HahnBackend(
t2hahn=[estimated_t2hahn],
frequency=[osc_freq],
@@ -138,9 +131,7 @@ def test_t2hahn_concat_2_experiments(self):
# second experiment
delays1 = list(range(2, 65, 2))
exp1 = T2Hahn(qubit, delays1)
- exp1.analysis.set_options(
- p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True
- )
+ exp1.analysis.set_options(p0={"amp": 0.5, "tau": estimated_t2hahn, "base": 0.5}, plot=True)
expdata1 = exp1.run(backend=backend, analysis=None, shots=1000).block_for_results()
expdata1.add_data(expdata0.data())
exp1.analysis.run(expdata1)
@@ -178,7 +169,3 @@ def test_analysis_config(self):
loaded = T2HahnAnalysis.from_config(analysis.config())
self.assertNotEqual(analysis, loaded)
self.assertEqual(analysis.config(), loaded.config())
-
-
-if __name__ == "__main__":
- unittest.main()
From ad8be9bc6edeb47ea10aa54b292c59b022954659 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 14:39:53 +0200
Subject: [PATCH 65/93] passed pylint and black
---
.../library/characterization/t2hahn.py | 3 --
qiskit_experiments/test/t2hahn_backend.py | 49 +++++++++++++------
2 files changed, 35 insertions(+), 17 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index f7289c9f49..4c6469ebde 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -137,9 +137,6 @@ def circuits(self) -> List[QuantumCircuit]:
Returns:
The experiment circuits.
-
- Raises:
- ValueError: if unit is 'dt', but 'dt' parameter is missing in the backend configuration
"""
if self.backend and hasattr(self.backend.configuration(), "dt"):
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 73bf22998b..4ad8dfe245 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -14,9 +14,9 @@
Temporary backend to be used for t2hahn experiment
"""
+from typing import List
import numpy as np
from numpy import isclose
-from typing import List
from qiskit import QiskitError
from qiskit.providers import BackendV1
from qiskit.providers.models import QasmBackendConfiguration
@@ -82,26 +82,35 @@ def _qubit_initialization(self, nqubits: int) -> List[dict]:
Returns:
List[dict]: A list of dictionary which each dictionary contain the qubit state in the format
{"XY plain": (bool), "ZX plain": (bool), "Theta": float}
+
+ Raises:
+ QiskitError: Raised if initialization_error type isn't 'None'', 'float' or a list of 'float'
+ with length of number of the qubits.
+ ValueError: Raised if the initialization error is negative.
"""
qubits_sates = [0 for _ in range(nqubits)]
# Making an array with the initialization error for each qubit.
initialization_error = self._initialization_error
- if isinstance(initialization_error, int) or initialization_error is None:
+ if isinstance(initialization_error, float) or initialization_error is None:
initialization_error_arr = [initialization_error for _ in range(nqubits)]
elif isinstance(initialization_error, list):
if len(initialization_error) == 1:
initialization_error_arr = [initialization_error[0] for _ in range(nqubits)]
elif len(initialization_error) == nqubits:
- initialization_error_arr = [err for err in initialization_error]
+ initialization_error_arr = initialization_error
else:
raise QiskitError(
f"The length of the list {initialization_error} isn't the same as the number "
"of qubits."
)
else:
- raise QiskitError(
- f"Initialization error type isn't a list or int"
- )
+ raise QiskitError("Initialization error type isn't a list or float")
+
+ for err in initialization_error_arr:
+ if not isinstance(err, float):
+ raise QiskitError("Initialization error type isn't a list or float")
+ if not err < 0:
+ raise ValueError("Initialization error value can't be negative.")
for qubit in range(nqubits):
if initialization_error_arr[qubit] is not None and (
@@ -161,8 +170,12 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
angle(float): The angle of the rotation.
Returns:
- dict: The state of the qubit after operating the gate.
+ dict: The state of the qubit after operating the gate.
+
+ Raises:
+ QiskitError: if angle is not ±π/2 or ±π. Those are the only supported angles.
"""
+
if qubit_state["XY plain"]:
if isclose(angle, np.pi):
new_theta = -qubit_state["Theta"]
@@ -189,7 +202,9 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
else:
- raise QiskitError(f"Error - the angle {angle} isn't supported. We only support multiplication of pi/2")
+ raise QiskitError(
+ f"Error - the angle {angle} isn't supported. We only support multiplication of pi/2"
+ )
else:
if isclose(angle, np.pi):
new_theta = qubit_state["Theta"] + np.pi
@@ -218,7 +233,9 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
else:
- raise QiskitError(f"Error - The angle {angle} isn't supported. We only support multiplication of pi/2")
+ raise QiskitError(
+ f"Error - The angle {angle} isn't supported. We only support multiplication of pi/2"
+ )
return new_qubit_state
def _measurement_gate(self, qubit_state: dict) -> int:
@@ -277,7 +294,9 @@ def run(self, run_input, **options):
counts = dict()
for _ in range(shots):
- qubit_state = self._qubit_initialization(nqubits=nqubits) # for parallel need to make an array
+ qubit_state = self._qubit_initialization(
+ nqubits=nqubits
+ ) # for parallel need to make an array
clbits = np.zeros(circ.num_clbits, dtype=int)
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
@@ -287,10 +306,12 @@ def run(self, run_input, **options):
delay = op.params[0]
t2hahn = self._t2hahn[qubit]
freq = self._frequency[qubit]
- qubit_state[qubit] = self._delay_gate(qubit_state=qubit_state[qubit],
- delay=delay, t2hahn=t2hahn,
- frequency=freq,
- )
+ qubit_state[qubit] = self._delay_gate(
+ qubit_state=qubit_state[qubit],
+ delay=delay,
+ t2hahn=t2hahn,
+ frequency=freq,
+ )
elif op.name == "rx":
qubit_state[qubit] = self._rx_gate(qubit_state[qubit], op.params[0])
elif op.name == "measure":
From d55e9509050dc44f7c3c95aa85a9eb9ad5dca428 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 16:40:38 +0200
Subject: [PATCH 66/93] fixed bug
---
qiskit_experiments/test/t2hahn_backend.py | 2 +-
test/test_t2hahn.py | 3 +--
2 files changed, 2 insertions(+), 3 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 4ad8dfe245..d7decdbad1 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -109,7 +109,7 @@ def _qubit_initialization(self, nqubits: int) -> List[dict]:
for err in initialization_error_arr:
if not isinstance(err, float):
raise QiskitError("Initialization error type isn't a list or float")
- if not err < 0:
+ if err < 0:
raise ValueError("Initialization error value can't be negative.")
for qubit in range(nqubits):
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 180af9a823..3995c216f9 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -9,7 +9,6 @@
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
-
"""
Test T2Hahn experiment
"""
@@ -124,7 +123,7 @@ def test_t2hahn_concat_2_experiments(self):
)
# run circuits
- expdata0 = exp0.run(backend=backend, shots=1000).block_for_results()
+ expdata0 = exp0.run(backend=backend, shots=1000)
expdata0.block_for_results()
res_t2_0 = expdata0.analysis_results("T2")
From c20d85726791a09c11e9d27e64b5d21e8330bc65 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 16:53:29 +0200
Subject: [PATCH 67/93] fixed docs
fixed docs spaces and blank lines
---
.../library/characterization/t2hahn.py | 42 +++++++++----------
1 file changed, 20 insertions(+), 22 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 4c6469ebde..dfd81a0883 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -11,7 +11,6 @@
# that they have been altered from the originals.
"""
T2Hahn Echo Experiment class.
-
"""
from typing import List, Optional, Union
@@ -28,38 +27,37 @@
class T2Hahn(BaseExperiment):
r"""T2 Hahn Echo Experiment.
+ # section: overview
- # section: overview
-
- This experiment is used to estimate T2 noise of a single qubit.
-
- See `Qiskit Textbook <https://qiskit.org/textbook/ch-quantum-hardware/\
- calibrating-qubits-pulse.html>`_ for a more detailed explanation on
- these properties.
+ This experiment is used to estimate T2 noise of a single qubit.
- This experiment consists of a series of circuits of the form
+ See `Qiskit Textbook <https://qiskit.org/textbook/ch-quantum-hardware/\
+ calibrating-qubits-pulse.html>`_ for a more detailed explanation on
+ these properties.
+ This experiment consists of a series of circuits of the form
- .. parsed-literal::
+ .. parsed-literal::
┌─────────┐┌──────────┐┌───────┐┌──────────┐┌─────────┐┌─┐
q_0: ┤ Rx(π/2) ├┤ DELAY(t) ├┤ RX(π) ├┤ DELAY(t) ├┤ RX(π/2) ├┤M├
└─────────┘└──────────┘└───────┘└──────────┘└─────────┘└╥┘
c: 1/════════════════════════════════════════════════════════╩═
0
- for each *t* from the specified delay times
- and the delays are specified by the user.
- The delays that are specified are delay for each delay gate while
- the delay in the metadata is the total delay which is delay * (num_echoes +1)
- The circuits are run on the device or on a simulator backend.
-
- # section: tutorial
- :doc:`/tutorials/t2hahn_characterization`
-
- # section: analysis_ref
- :py:class:`T2HahnAnalysis`
- """
+
+ for each *t* from the specified delay times
+ and the delays are specified by the user.
+ The delays that are specified are delay for each delay gate while
+ the delay in the metadata is the total delay which is delay * (num_echoes +1)
+ The circuits are run on the device or on a simulator backend.
+
+ # section: tutorial
+ :doc:`/tutorials/t2hahn_characterization`
+
+ # section: analysis_ref
+ :py:class:`T2HahnAnalysis`
+ """
@classmethod
def _default_experiment_options(cls) -> Options:
From 933d98cccff57d4fc0ffd7f23e6eecbd502a2ded Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 17:03:32 +0200
Subject: [PATCH 68/93] Update t2hahn.py
---
qiskit_experiments/library/characterization/t2hahn.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index dfd81a0883..df6a8a22e1 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -45,7 +45,7 @@ class T2Hahn(BaseExperiment):
└─────────┘└──────────┘└───────┘└──────────┘└─────────┘└╥┘
c: 1/════════════════════════════════════════════════════════╩═
0
-
+
for each *t* from the specified delay times
and the delays are specified by the user.
The delays that are specified are delay for each delay gate while
From a8028e2d178501ae45c38d628b0a95b1402808f9 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 17:38:43 +0200
Subject: [PATCH 69/93] Raising error when delay applied and the qubit isn't in
the XY plain or theta isn't pi or 0
---
qiskit_experiments/test/t2hahn_backend.py | 10 ++++++++--
test/test_t2hahn.py | 5 ++++-
2 files changed, 12 insertions(+), 3 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index d7decdbad1..f04031d55e 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -88,7 +88,7 @@ def _qubit_initialization(self, nqubits: int) -> List[dict]:
with length of number of the qubits.
ValueError: Raised if the initialization error is negative.
"""
- qubits_sates = [0 for _ in range(nqubits)]
+ qubits_sates = [{} for _ in range(nqubits)]
# Making an array with the initialization error for each qubit.
initialization_error = self._initialization_error
if isinstance(initialization_error, float) or initialization_error is None:
@@ -137,7 +137,12 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float, frequency:
Returns:
dict: The state of the qubit after operating the gate.
+
+ Raises:
+ QiskitError: Raised if initialization_error type isn't 'None'', 'float' or a list of 'float'
+ with length of number of the qubits.
"""
+ new_qubit_state = qubit_state
if qubit_state["XY plain"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
@@ -159,7 +164,8 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float, frequency:
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {"XY plain": True, "ZX plain": False, "Theta": new_theta}
else:
- new_qubit_state = qubit_state
+ if not isclose(qubit_state["Theta"], np.pi) and not isclose(qubit_state["Theta"], 0):
+ raise QiskitError("Delay gate supported only if the qubit is on the XY plain.")
return new_qubit_state
def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 3995c216f9..5a4ec43e3f 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -19,7 +19,7 @@
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-
+import unittest
class TestT2Hahn(QiskitExperimentsTestCase):
"""Test T2Hahn experiment"""
@@ -168,3 +168,6 @@ def test_analysis_config(self):
loaded = T2HahnAnalysis.from_config(analysis.config())
self.assertNotEqual(analysis, loaded)
self.assertEqual(analysis.config(), loaded.config())
+
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
From 038be8af7e79dbcb4edac1eb2d40bfc7e77a00f5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 12 Dec 2021 17:50:38 +0200
Subject: [PATCH 70/93] Update test_t2hahn.py
---
test/test_t2hahn.py | 5 +----
1 file changed, 1 insertion(+), 4 deletions(-)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 5a4ec43e3f..3995c216f9 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -19,7 +19,7 @@
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-import unittest
+
class TestT2Hahn(QiskitExperimentsTestCase):
"""Test T2Hahn experiment"""
@@ -168,6 +168,3 @@ def test_analysis_config(self):
loaded = T2HahnAnalysis.from_config(analysis.config())
self.assertNotEqual(analysis, loaded)
self.assertEqual(analysis.config(), loaded.config())
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
From f74ef7ceb1bfa936bff299725bb8a8ff8a802a4d Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 15 Dec 2021 10:52:55 +0200
Subject: [PATCH 71/93] edited comment and change 'plain' to 'plane'
---
.../library/characterization/t2hahn.py | 6 +-
qiskit_experiments/test/t2hahn_backend.py | 56 +++++++++----------
2 files changed, 31 insertions(+), 31 deletions(-)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index df6a8a22e1..9cb31a52f7 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -129,9 +129,9 @@ def circuits(self) -> List[QuantumCircuit]:
"""
Return a list of experiment circuits.
- Each circuit consist with RX(π/2) followed by a sequence of delay gate,
+ Each circuit consist of RX(π/2) followed by a sequence of delay gate,
RX(π) for echo and delay gate again.
- The sequence repeats for the number of echoes and finish with RX(±π/2).
+ The sequence repeats for the number of echoes and terminates with RX(±π/2).
Returns:
The experiment circuits.
@@ -156,7 +156,7 @@ def circuits(self) -> List[QuantumCircuit]:
circ = QuantumCircuit(1, 1)
# First X rotation in 90 degrees
- circ.rx(np.pi / 2, 0) # Bring to qubits to X Axis
+ circ.rx(np.pi / 2, 0) # Brings the qubit to the X Axis
for _ in range(self.experiment_options.num_echoes):
if dt_unit:
circ.delay(delay_dt, 0, "dt")
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index f04031d55e..338137f512 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -81,7 +81,7 @@ def _qubit_initialization(self, nqubits: int) -> List[dict]:
Returns:
List[dict]: A list of dictionary which each dictionary contain the qubit state in the format
- {"XY plain": (bool), "ZX plain": (bool), "Theta": float}
+ {"XY plane": (bool), "ZX plane": (bool), "Theta": float}
Raises:
QiskitError: Raised if initialization_error type isn't 'None'', 'float' or a list of 'float'
@@ -116,11 +116,11 @@ def _qubit_initialization(self, nqubits: int) -> List[dict]:
if initialization_error_arr[qubit] is not None and (
self._rng.random() < initialization_error_arr[qubit]
):
- qubits_sates[qubit] = {"XY plain": False, "ZX plain": True, "Theta": np.pi}
+ qubits_sates[qubit] = {"XY plane": False, "ZX plane": True, "Theta": np.pi}
else:
qubits_sates[qubit] = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": 0,
}
return qubits_sates
@@ -143,29 +143,29 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float, frequency:
with length of number of the qubits.
"""
new_qubit_state = qubit_state
- if qubit_state["XY plain"]:
+ if qubit_state["XY plane"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
if self._rng.random() < prob_noise:
if self._rng.random() < 0.5:
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": 0,
}
else:
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": np.pi,
}
else:
phase = frequency * delay
new_theta = qubit_state["Theta"] + phase
new_theta = new_theta % (2 * np.pi)
- new_qubit_state = {"XY plain": True, "ZX plain": False, "Theta": new_theta}
+ new_qubit_state = {"XY plane": True, "ZX plane": False, "Theta": new_theta}
else:
if not isclose(qubit_state["Theta"], np.pi) and not isclose(qubit_state["Theta"], 0):
- raise QiskitError("Delay gate supported only if the qubit is on the XY plain.")
+ raise QiskitError("Delay gate supported only if the qubit is on the XY plane.")
return new_qubit_state
def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
@@ -182,29 +182,29 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
QiskitError: if angle is not ±π/2 or ±π. Those are the only supported angles.
"""
- if qubit_state["XY plain"]:
+ if qubit_state["XY plane"]:
if isclose(angle, np.pi):
new_theta = -qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": True,
- "ZX plain": False,
+ "XY plane": True,
+ "ZX plane": False,
"Theta": new_theta,
}
elif isclose(angle, np.pi / 2):
new_theta = angle - qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": new_theta,
}
elif isclose(angle, -np.pi / 2):
new_theta = np.abs(angle - qubit_state["Theta"])
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": new_theta,
}
else:
@@ -216,8 +216,8 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
new_theta = qubit_state["Theta"] + np.pi
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": False,
- "ZX plain": True,
+ "XY plane": False,
+ "ZX plane": True,
"Theta": new_theta,
}
elif isclose(angle, np.pi / 2):
@@ -226,16 +226,16 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
) # its theta -pi/2 but we added 2*pi
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": True,
- "ZX plain": False,
+ "XY plane": True,
+ "ZX plane": False,
"Theta": new_theta,
}
elif isclose(angle, -np.pi / 2):
new_theta = np.pi / 2 - qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
- "XY plain": True,
- "ZX plain": False,
+ "XY plane": True,
+ "ZX plane": False,
"Theta": new_theta,
}
else:
@@ -254,11 +254,11 @@ def _measurement_gate(self, qubit_state: dict) -> int:
int: The result of the measurement after applying read-out error.
"""
# Here we are calculating the probability for measurement result depending on the
- # where the qubit is on the bloch sphere.
- if qubit_state["XY plain"]:
+ # location of the qubit on the Bloch sphere.
+ if qubit_state["XY plane"]:
meas_res = self._rng.random() < 0.5
else:
- # Since we are not in the XY plain, we need to calculate the probability for
+ # Since we are not in the XY plane, we need to calculate the probability for
# measuring output. First, we calculate the probability and later we are
# tossing to see if the event did happened.
z_projection = np.cos(qubit_state["Theta"])
@@ -307,7 +307,7 @@ def run(self, run_input, **options):
for op, qargs, cargs in circ.data:
qubit = qubit_indices[qargs[0]]
- # The noise will only be applied if we are in the XY plain.
+ # The noise will only be applied if we are in the XY plane.
if op.name == "delay":
delay = op.params[0]
t2hahn = self._t2hahn[qubit]
From b3984662fe99d1d5501c2380d713f6cc5a54b660 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 26 Dec 2021 12:53:19 +0200
Subject: [PATCH 72/93] added tests for number of echoes
---
test/test_t2hahn.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 3995c216f9..efe2437ec5 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -15,20 +15,23 @@
from test.base import QiskitExperimentsTestCase
import numpy as np
+from ddt import ddt, data, unpack
from qiskit_experiments.framework import ParallelExperiment
from qiskit_experiments.library.characterization.t2hahn import T2Hahn
from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
-
+@ddt
class TestT2Hahn(QiskitExperimentsTestCase):
"""Test T2Hahn experiment"""
__tolerance__ = 0.1
- def test_t2hahn_run_end2end(self):
+ @data([1], [2])
+ @unpack
+ def test_t2hahn_run_end2end(self, num_of_echoes: int):
"""
- Run the T2Hahn backend on all possible units
+ Run the T2Hahn backend with one echo.
"""
osc_freq = 0.1
estimated_t2hahn = 20
@@ -38,7 +41,7 @@ def test_t2hahn_run_end2end(self):
(np.linspace(1.0, 15.0, num=15)).astype(float),
(np.linspace(16.0, 45.0, num=59)).astype(float),
)
- exp = T2Hahn(qubit=qubit, delays=delays)
+ exp = T2Hahn(qubit=qubit, delays=delays, num_echoes=num_of_echoes)
default_p0 = {
"A": 0.5,
"T2": estimated_t2hahn,
From a90bc26ad30b523fb2d5ba22e7c85dc22b44966f Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 27 Dec 2021 15:35:41 +0200
Subject: [PATCH 73/93] Fixed Black
---
test/test_t2hahn.py | 1 +
1 file changed, 1 insertion(+)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index efe2437ec5..0b811c99f5 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -21,6 +21,7 @@
from qiskit_experiments.library.characterization import T2HahnAnalysis
from qiskit_experiments.test.t2hahn_backend import T2HahnBackend
+
@ddt
class TestT2Hahn(QiskitExperimentsTestCase):
"""Test T2Hahn experiment"""
From 6184579c9de5af45d31b5cacb27ed1b273641720 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 27 Dec 2021 15:52:50 +0200
Subject: [PATCH 74/93] rerun pylint
---
test/test_t2hahn.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 0b811c99f5..57abc9598a 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -108,7 +108,7 @@ def test_t2hahn_parallel(self):
def test_t2hahn_concat_2_experiments(self):
"""
- Concatenate the data from 2 separate experiments
+ Concatenate the data from 2 separate experiments.
"""
estimated_t2hahn = 30
# First experiment
From 06cf69c43f3a0c735f1544d64161daad3b401261 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 27 Dec 2021 18:29:33 +0200
Subject: [PATCH 75/93] Added to tutorial both experiments
---
docs/tutorials/t2hahn_characterization.ipynb | 231 ++++++++++---------
1 file changed, 119 insertions(+), 112 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 04469cc7bc..ce57269212 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -41,30 +41,13 @@
" 2. delay\n",
" 3. measurement\n",
"\n",
- "The user provides as input a series of delays and the time unit for the delays, e.g., seconds, milliseconds, etc. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle will converge after the delay gates as following $\\theta_{new} = \\theta_{old} + \\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
+ "The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle after the delay gates will be $\\theta_{new} = \\theta_{old} + \\pi$. after wating the same delay time, the angle will be approximly $0$ or $\\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
- "source": [
- "# set the computation units to microseconds\n",
- "unit = \"us\" # microseconds\n",
- "qubit = 0\n",
- "# set the desired delays\n",
- "conversion_factor = 1e-6\n",
- "delays = list(range(1, 50, 1) )\n",
- "delays = [float(_) * conversion_factor for _ in delays]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "scrolled": true
- },
"outputs": [
{
"name": "stdout",
@@ -79,6 +62,12 @@
}
],
"source": [
+ "qubit = 0\n",
+ "# set the desired delays\n",
+ "conversion_factor = 1e-6 # our delay will be in micro-sec\n",
+ "delays = list(range(1, 50, 1) )\n",
+ "delays = [float(_) * conversion_factor for _ in delays]\n",
+ "\n",
"# Create a T2Hahn experiment. Print the first circuit as an example\n",
"exp1 = T2Hahn(qubit, delays)\n",
"print(exp1.circuits()[0])"
@@ -93,34 +82,21 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 18,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1e-06\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
- "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
- "from qiskit_experiments.test.utils import FakeJob\n",
"\n",
- "\n",
- "estimated_t2hahn = 20\n",
+ "estimated_t2hahn = 20 * conversion_factor\n",
"# The behavior of the backend is determined by the following parameters\n",
"backend = T2HahnBackend(\n",
- " t2hahn=[20],\n",
+ " t2hahn=[estimated_t2hahn],\n",
" frequency=[100100],\n",
" initialization_error=[0.0],\n",
" readout0to1=[0.02],\n",
" readout1to0=[0.02],\n",
- " conversion_factor=conversion_factor,\n",
- ")\n",
- "print(conversion_factor)"
+ ")"
]
},
{
@@ -129,20 +105,27 @@
"source": [
"The resulting graph will have the form:\n",
"$f(t) = a \\cdot e^{-\\frac{t}{T_2}}+ b$\n",
- "where *t* is the delay and $T_2$ is the decay factor.\n",
- "`conversion_factor` is a scaling factor that depends on the measurement units used. It is 1E-6 here, because the unit is microseconds."
+ "where *t* is the delay and $T_2$ is the decay factor."
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_fit.py:137: RuntimeWarning: invalid value encountered in sqrt\n",
+ " popt_err = np.sqrt(np.diag(pcov))\n"
+ ]
+ },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -152,10 +135,7 @@
}
],
"source": [
- "dt_factor = apply_prefix(1, unit)\n",
- "\n",
- "# exp1.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
- "\n",
+ "exp1.analysis.set_options(p0=None, plot=True)\n",
"expdata1 = exp1.run(backend=backend, shots=2000)\n",
"expdata1.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
@@ -174,16 +154,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527131e-03 3.03978270e-03 5.77293057e-07]\n",
- "- χ²: 0.7488240853426228\n",
+ "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527149e-03 3.03978247e-03 5.77292515e-07]\n",
+ "- χ²: 0.7488240853426195\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 1.9828300679956625e-05 ± 5.772930568055365e-07 s\n",
- "- χ²: 0.7488240853426228\n",
+ "- value: 1.9828300732126065e-05 ± 5.772925151075391e-07 s\n",
+ "- χ²: 0.7488240853426195\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -206,12 +186,12 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -222,7 +202,7 @@
],
"source": [
"exp_with_p0 = T2Hahn(qubit, delays)\n",
- "exp_with_p0.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
+ "exp_with_p0.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5})\n",
"expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n",
"expdata_with_p0.block_for_results()\n",
"\n",
@@ -306,10 +286,8 @@
],
"source": [
"import numpy as np\n",
- "# set the computation units to microseconds\n",
- "unit2 = \"us\" # microseconds\n",
- "qubit2 = 0\n",
"\n",
+ "qubit2 = 0\n",
"# set the desired delays\n",
"conversion_factor = 1e-6\n",
"\n",
@@ -345,27 +323,17 @@
}
],
"source": [
- "\n",
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
- "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
- "from qiskit_experiments.test.utils import FakeJob\n",
"\n",
- "\n",
- "estimated_t2hahn2 = 20\n",
+ "estimated_t2hahn2 = 20 * conversion_factor\n",
"# The behavior of the backend is determined by the following parameters\n",
"backend2 = T2HahnBackend(\n",
- " t2hahn=[20],\n",
+ " t2hahn=[estimated_t2hahn2],\n",
" frequency=[100100],\n",
" initialization_error=[0.0],\n",
" readout0to1=[0.02],\n",
- " readout1to0=[0.02],\n",
- " conversion_factor=conversion_factor,\n",
- ")\n",
- "\n",
+ " readout1to0=[0.02],)\n",
"\n",
- "dt_factor2 = apply_prefix(1, unit2)\n",
- "\n",
- "# exp2.set_analysis_options(p0={\"amp\": 0.5, \"tau\": 20 * conversion_factor, \"base\": 0.5})\n",
"\n",
"expdata2 = exp2.run(backend=backend2, shots=2000)\n",
"expdata2.block_for_results() # Wait for job/analysis to finish.\n",
@@ -378,88 +346,127 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### $T_{2}$ v.s $T_{2}^{\\ast}$\n",
- "This experiment purpose is to give a better estimation for the dephasing noise. In Ramsey experiment, we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
- "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. By using echo pulse ($Rx(\\pi)$) we can reduce the effect of the inhomogeneous broadening and get better estimation."
+ "### $T_{2}$ versus $T_{2}^{\\ast}$\n",
+ "This experiment purpose is to give a better estimate for the dephasing noise. In Ramsey experiment, we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
+ "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. This affect the qubit frequncy.\n",
+ "In Ramsey experiment, we estimate the frequency of the qubit while in Hahn Echo experiment there is no need.\\\n",
+ "Firslty, let us get backend property from the the quantum computer."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- " ┌───┐┌─────────────────┐┌─────────┐ ░ ┌───┐ ░ ┌─┐\n",
- " q: ┤ H ├┤ Delay(1e-06[s]) ├┤ Rz(π/5) ├─░─┤ H ├─░─┤M├\n",
- " └───┘└─────────────────┘└─────────┘ ░ └───┘ ░ └╥┘\n",
- "c: 1/═══════════════════════════════════════════════╩═\n",
- " 0 \n"
+ "ibmqfactory.load_account:WARNING:2021-12-27 17:11:38,733: Credentials are already in use. The existing account in the session will be replaced.\n"
]
}
],
"source": [
+ "from qiskit import IBMQ\n",
+ "from qiskit_experiments.library.characterization.t2hahn import T2Hahn\n",
"import qiskit\n",
"from qiskit_experiments.library import T2Ramsey\n",
- "from qiskit_experiments.test.t2ramsey_backend import T2RamseyBackend\n",
- "# FakeJob is a wrapper for the backend, to give it the form of a job\n",
- "from qiskit_experiments.test.utils import FakeJob\n",
"\n",
- "# set the computation units to microseconds\n",
- "unit_ramsey = \"us\" # microseconds\n",
- "qubit_ramsey = 0\n",
- "# set the desired delays\n",
- "delays_ramsey = list(range(1, 50, 1))\n",
+ "def backend_fetcher():\n",
+ " # TOKEN = \"\"\n",
+ " # IBMQ.save_account(TOKEN)\n",
+ " IBMQ.load_account() # Load account from disk\n",
+ " provider = IBMQ.get_provider(hub='ibm-q')\n",
+ " backend = provider.get_backend('ibmq_manila')\n",
+ " backend_properties = backend_manila.properties()\n",
+ " return backend, backend_properties\n",
"\n",
- "conversion_factor_ramsey = 1e-6\n",
- "# defining backend for the experiment\n",
- "backend_ramsey = T2RamseyBackend(\n",
- " p0={\n",
- " \"A\": [0.5],\n",
- " \"T2star\": [20.0],\n",
- " \"f\": [100100],\n",
- " \"phi\": [0.0],\n",
- " \"B\": [0.5],\n",
- " },\n",
- " initial_prob_plus=[0.0],\n",
- " readout0to1=[0.02],\n",
- " readout1to0=[0.02],\n",
- " conversion_factor=conversion_factor_ramsey,\n",
- ")\n",
+ "\n",
+ "backend, backend_properties = backend_fetcher()\n",
+ "estimated_t2hahn = backend_properties.t2(0)\n",
+ "\n",
+ "# Hahn Echo experiment parameters\n",
+ "qubit_hahn = 0\n",
+ "conversion_factor = 1e-6\n",
+ "delays2 = np.append(\n",
+ " (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
+ " (np.linspace(10.5, 45.0, num=70)).astype(float),\n",
+ " )\n",
+ "delays2 = [float(_) * conversion_factor for _ in delays2]\n",
+ "num_echoes = 4\n",
+ "\n",
+ "exp3_hahn = T2Hahn(qubit_hahn, delays2, num_echoes=num_echoes, backend=backend)\n",
+ "exp3_hahn.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5}, plot=True)\n",
+ "expdata_hahn = exp3_hahn.run(backend=backend_manila, shots=2000).block_for_results()\n",
+ "\n",
+ "# Ramsey experiment parameters\n",
+ "qubit_ramsey = 0\n",
+ "delays_ramsey = list(range(1, 350, 2))\n",
+ "delays_ramsey = [float(_) * conversion_factor for _ in delays_ramsey]\n",
"\n",
"# Create a T2Ramsey experiment. Print the first circuit as an example\n",
- "exp1_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, unit=unit_ramsey, osc_freq=1e5, backend=backend_ramsey)\n",
+ "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=freq_manila, backend=backend_manila)\n",
"\n",
- "# Run the experiment\n",
- "expdata1_ramsey = exp1_ramsey.run(backend=backend_ramsey, shots=2000)\n",
- "expdata1_ramsey.block_for_results() # Wait for job/analysis to finish.\n",
+ "backend, backend_properties = backend_fetcher()\n",
+ "# Analysis\n",
+ "default_p0 = {\n",
+ " \"A\": 0.5,\n",
+ " \"T2star\": estimated_t2hahn,\n",
+ " \"f\": backend_properties.frequency(0),\n",
+ " \"phi\": 0,\n",
+ " \"B\": 0.5,\n",
+ " }\n",
+ "exp_ramsey.analysis.set_options(p0=default_p0, plot=True)\n",
"\n",
- "# Printing a circuit for example\n",
- "print(exp1_ramsey.circuits()[0])"
+ "# Run the Ramsey experiment\n",
+ "expdata_ramsey = exp_ramsey.run(backend=backend, shots=2000).block_for_results()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can see that while both backends have $T_2 = 20 [\\mu s]$, the estimation of the Hahn Echo experiment is better."
+ "We can see that the backend has $T_2 = 20 [\\mu s]$. We can see the estimate $T_2$ from both experiments:"
]
},
{
- "cell_type": "raw",
- "metadata": {},
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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PhB1mnDNH3fzSvJWWlvKNb3xjx6Nbt26Nct2DDz6YX/3qV9x0000ceeSRDb5ONnTvd+rUicLCQlatWlXv+KpVq+gSMau3rKyMfv361Tunb9++Oyb7zZ8/nzVr1nDAAQdQVFREUVERL730EjNmzKCoqIitW7emtR7SvKmlnwbBcr6qKnj77eSb9Gzfbr0DY8dCmiY5i2SF4uJiqlPIZe29p2/fvtx000279LkjRozg3HPPTXhOY92oxFNcXMyAAQP4+9//zjnnnLPj+N///nfOPvvsHc+PPvpoPv7443rv/eSTT+jZsycAZ5555k5DEcOGDWOfffbhhhtuoLi4OI21kOYuI0HfOXc5cC1QBnwAXOO9j3ub7Zy7ArgS6AUsBX7hvX+0CYq6Sy64wLL3hREk67nssvSWSSSTevXqxRtvvMHixYtp06YNHTt2jDvzffr06bz88svss88+ccf+w0pH9/7mzZv59NNPAeuSX7p0Ke+88w4dO3akR48eANx3333cd999fPTRRwCMGTOGiy66iMMOO4yjjz6amTNnsnz5ckaMGLHjuqNHj+aoo47iF7/4Beeddx5vv/02v/zlL5k4cSIA7du332kJZOvWrenYseOOZZIi8TR5975z7jxgGjAROAR4Dfibc65HnPNHAncCE4ADgFuA6c65M5qmxA0XTO4rCnFrpWQ9kg/GjRtHcXEx/fr1Y4899qi3Pj3Shx9+yLXXXssVV1zBf//7XyrCTJBpYm+++SaHHHIIhxxyCJWVldxyyy0ccsghjI8Yq1uzZk29Vvt5553H1KlTuf322+nfvz+vvPIKzz333I5WPMChhx7KM888w+9//3sOPPBAbrzxRm677TYuv/zyJq2f5CjvfZM+gH8DD0YdWwTcEef814B7oo5NBl5J9lkDBgzwqZg7d25K54dRU+P9SSd5byvy4z9at/Z+1qxG//idpKOO2Sgf6pmojqn+3c8mW7Zs8QcffLC/4IILfHl5uS8oKPDz58/PdLHSbuPGjZkuQoOk8nctH/5dep/5egJv+jhxsUlb+s65YmAAMCfqpTlAvGlvLYEtUccqgcOcc1k/ddU5+N3voGXLxOdVVUHEMJ9I3rruuuvYsGED999/P6Wlpey9995MmzYtbq+AiITX1N37nYBCYFXU8VVAvKS0LwA/ds4d6sxA4CdAi9rrZb2OHW1sP9EqpupqmDxZGfokv82ZM4f77ruPxx9/nHbt2gE2JPDPf/6TSy65JMOlE2n+nG/CKOOc6wp8CRznvX854vh44ELv/X4x3lMCTAcuAhx2g/A48DOgi/d+VdT5w4HhAJ07dx4QpOwMY/PmzbRp0ybVaoX2xRewYgXMnNmfzz5rz2GHreDcc+vG+woKoHNn6No1bUVIex2zRT7UM1Edr7zySubPn9/EJUqP6urqXZ7I1xw013oeeeSR3HfffaHOzYd/l5D5eg4ePHiB9z52tql4/f7peADFQBVwTtTx6cBLSd7bAuiO9RSMBDYCBYnekw1j+pG++sr74uLEY/utWnm/bl36ypDpsaamkg/1zNUx/WjNdaw7Vc21nhrT31mm60m2jOl777cBC4Do7WlOxCbsJXrvdu/9F977auB84C/e+/ApubLAH/+YfCZ/sHRPRESksWVinf4U4DHn3BvAq8AIoCswE8A59yiA9/7i2uf7AocDrwMdgDHAgUCzG+BbuTJ5oh4t3RMRkXRp8qDvvX/SObc7cBOWnOd94FTv/ZLaU6LX6xdigX4/YDswFzjKe7+4aUrceLp0sQ15ysvjn1NSYueJiIg0toxk5PPezwBmxHltUNTzhVgSn2ZvyBC4+urE51RXw/e+1zTlERGR/KINd5pQsu13i4psk54774QHHoB165q2fCIiktsU9JvYhAm2Zr9VKwv+zkGwO2ZVFcydazv0jRljS/duvllr90VEpHFol70mFmy/O2aMzdJ/5BGItZw6SDU+ZYr9vO22piujNH9lZWU77cTWXG3ZsoVWrVpluhhp11zrWVZWlukiSAoU9DOkQwdYtsy23k3Ukq+o0La7krpnn30200VoNPPmzWPQoEGZLkba5Us9JbPUvZ8h69ZZME+2hA8sU5/W7ouIyK5S0M+Qp5+2RDxhVFTAb36jsX0REdk1CvoZsnJl3bh9GPPnQ8Q23SIiIilT0M+QIFFPWNu22XDA+vVpK5KIiOQ4Bf0MGTLEEvGkoqYGZs9OS3FERCQPKOhnSLJEPbFs2wbXXqu1+yIi0jBaspdBEybYz0mTrBW/bVvy91RVae2+iIg0jFr6GRQk6lm+3FLvJtt2NxCs3df4voiIpEJBPwt06ADXXAPXXRe+u19r90VEJFUK+lkkyMsfpsVfWWk9BCIiImEp6GeRoLv/7ruhuDjxuS1bQps2TVMuERHJDQr6WeiSS6z7PpEtW+CPf4QlS5qmTCIi0vwp6GehsMv5Xn0V9t4bbrxRS/hERCQ5Bf0sNWECjByZ/LzqarjrLjj9dNvER0REJB4F/SzlHOy3X7jZ/FVV8NxzsOeecNppsHZt+ssnIiLNj4J+Flu5MtzWu4Eg+HfurKx9IiKyM2Xky2LBpjzl5am9r6oKJk+Gt96CI46w6wwZYnMFREQkf6mln8UasilPoLLSWv3jx8Po0dC1q7X+RUQkfynoZ7FgFn9Jya5dp7zclvhNmaKEPiIi+UxBP8tNmABjx0Jh4a5fq6LC5gkoZ7+ISH5S0M9yQZa+1attZv6uBn/nlLNfRCRfKeg3Ex07wl/+Al99ZcE/7I580WpqrLUvIiL5R0G/menQAZ591nbka0irv6DAZvOLiEj+UdBvhqK7/IuKoEWLcO/1Hs45J73lExGR7KR1+s1Y0OX/9dfw4IO2+c5DD8G2bbHPLy21Vn779k1aTBERyRIK+jmgY0fYuBEefhi2b9/59ZISG8u/5hpbry8iIvlJ3fs5YPx4mDYNtm6NnXr3iCNg/ny4/PKmL5uIiGQPtfSbuXXrYNIkS74Tz8svQ6tWsHmzpegVEZH8pJZ+M/f008ln8VdXwwUXwJ/+ZL0BmzY1TdlERCS7KOg3cytXWqa9ZN59F265BT7+GH7+88Q9AyIikpsU9Ju5YCe+MLZsgcrKQn71K7jySnX1i4jkGwX9Zi7VnfiefHJ/tm+HX/8arrjCZvWLiEh+UNBv5oKd+MK29t97b48df374YXuviIjkBwX9HDBhAowZYzP0W7cO/77t22HGDFi6NH1lExGR7KGgnwOCtLzLl8M998D3vgfFxbHPPfHExfWeFxbC7NlQWZn2YoqISIYp6OeQDh3gssssiBfE+Wajg35Fha3znzwZVq1KexFFRCSDFPRzUDDOH6u1H+tmYNMmy+rXpYtt4LN2bfrLKCIiTU9BP0dNmABXXRX+/CB973PPQefOcPPNsVP6iohI86U0vDnKOeu2B7j33vg778VSVQVTptifb7ut8csmIiKZkZGWvnPucufc5865Lc65Bc65Y5Oc/0Pn3DvOuQrn3Ern3OPOuS5NVd7m7O67U2vxB4Kx/vXrG71IIiKSIU0e9J1z5wHTgInAIcBrwN+ccz3inH808BjwCHAAcCbQD/hNU5S3uXMO9tsv/Dr+SAUF8NRTjV8mERHJjEy09McAs733D3rvF3rvrwJWACPjnH8k8IX3/h7v/efe+9eBe4HDm6i8zd7KlQ1bkldZCYsW2U1D797w4IO2q5+IiDRPTRr0nXPFwABgTtRLc4Cj4rztVaDMOXeGM52A84Hn0lfS3JJKfv5IhYW27v/TT2HxYhg9Grp21SQ/EZHmqqlb+p2AQiB6RfgqIOYYvfd+PhbkfwNsA74CHHBJ+oqZW1LNzx+oqrJHkJ+/vNw27ZkyxZb4iYhI8+J8EzbZnHNdgS+B47z3L0ccHw9c6L3fL8Z7+gF/B6YCLwBlwN3AO977i2OcPxwYDtC5c+cBTzzxROjybd68mTZt2qRSpWZj+XJLvtO162a++CJ+HT/9tD0PPngQ1dUFnHnmIo455suY5zkHBx9svQHZKJe/y0A+1BFUz1ySD3WEzNdz8ODBC7z3A2O+6L1vsgdQDFQB50Qdnw68FOc9jwF/jDp2DOCB7ok+b8CAAT4Vc+fOTen85qSmxvubbvJ+8uS5vqDAe/C+pMR75+xhHfbhH61bez9rVqZrFV8uf5eBfKij96pnLsmHOnqf+XoCb/o4cbFJu/e999uABcCJUS+diM3ij6UUiO6cDp4ruVBIQX7+gw+GmTMtec+0aZZ9b8yY+Ln64ykvh5/9TJP7RESak0wk55kCPOacewObpDcC6ArMBHDOPQrg67runwUedM6NpK57fyrwlvde+8OlqLDQ8vMH1q2D6dNTS94TWL/eJvddfbWl/Z0wwW4uREQkOzV50PfeP+mc2x24CQvg7wOneu+X1J7SI+r82c65tsCVwGRgA/BP4OdNV+rc9fTTuzYuX15uP5XBT0Qk+2Wke9x7P8N738t739J7P8BHTOrz3g/y3g+KOv9e7/0B3vtS732Z9/5C7/0XTV7wHLRypWXf21XK4Ccikv00Jp7nGrqGP5bCQmXwExHJZgr6ea6ha/hjqaiwngMREclOCvp5rkMHm4TXGK390lLrORARkeykoC9MmGDL9lq1sk12wAJ4QUHd8zCqq+Gcc9JTRhER2XWZWLInWSZYwz9mjM3mX7nSWuwnngh9+1rq3WRKSuDHP4bWrdNfXhERaRgFfdmhQ4f6a/jBuv6nTEk8w79FCxg+HK68Er74AvbaC4r0N0tEJOuoe18Siuz6b93aegVatbKZ+mVldk5REZxwgg0JrFsH++wDvXopW5+ISLZR0JeEgq7/5cttm92bb4YbboC334Y33rAx/MpKuOgiuPZaOOooWLoUlizRVrwiItlGnbASSmTXf0WFBXbvLSHP5s3wt7/Bb39b/z3K1icikl3U0peUlZZCt27W+h80CN56K/H5FRVw++22wY+6+0VEMkdBX1LmPdx1Fxx3HHz+OaxaFe59Y8equ19EJJPUvS8pGz/euuy3bk3tfdXV9lB3v4hIZqilLylZt87G8Xdlkx5tziMikhkK+pKSXd2KN6DNeUREmp6CvqSksbbiLS+HiRN3/ToiIhJeSkHfOXeEc+5W59zzzrn/55xb5Jyb75yb7Zwb5pzrkK6CSnZozK14N25UAh8RkaYUKug75y5xzr0HvAaMBkqBRcC/gXXA4cCvgC9rbwB6p6m8kmGNuRXv2rVK4CMi0pSSzt53zv0/YA/gUeBi4B3vd/7v2TnXDjgduBD40Dk31Hv/ZCOXVzIs2Io3WT7+sJTAR0Sk6YRp6f8a6O29/7n3/u1YAR/Ae7/Be/8b7/2pwBHA+kYsp2SRIB9/y5Y7v1ZUZJP0Up3spxn9IiLpl7Sl772flupFvffvAu82qESS9aK34v3887rd9Xr1snz83sNVV8GTT0JVVbjrFhTYjP7onf4SGTTIfs6bl2IlRETykJLzSIPF2oo30n77pTb+X1lpNw9hrVsHK1bAtm02IXDIECuTiIjEFnr2vnPuTOfcw865f9fO2l9U++eHnXNnprGM0kylOtO/VSto0cKCfyLe28S/rl3h009h8WJNCBQRCSNp0HfOdXDOvQL8HzAYWAO8XvtYAwwC/s8596qW7EmkVGf6V1bC9u3w/vuJJwkGaYC3bIGaGjtWXm7Pp0yx10VEZGdhWvqTgR7Acd77Xt7707z3F9U+TvPe9wa+DXQDJqWzsNK8BDP9U2ntT5oExx5rm/Ns2LDz68nSAAcTAhtrWaGISC4JE/S/B4zz3v8r3gne+1eAnwNnNlK5JEcEM/1btbIJgMlUVNhGPo88Atddt3PinjBpgAsLlfBHRCSWMEG/JZaAJ5n1QPEulUZyTjDTf/lyeOABuwmYPBmuvTZx8K6shJkzoV8/uOceS+QD4dIAV1TYMIGIiNQXZvb+fOBG59zr3vtNsU5wzrUFrscy9onsJHqm/wMPQHFx8kl7K1fCjTfC9dfbUEGPHjZcECT1iaW01CYEiohIfWGC/jXAPGCJc+6vwPvUtfw7AAcApwHV2EQ/kaRWr7aJd2EENwZTpsDIkcnH66urtXRPRCSWMMl5PnTOHQz8DDgD+CEQjM56YDGWovdu7/3yNJVTckywnC9Riz1aZSVMnw6XX249BbG6+UtLbQ5BY2z/KyKSa0Kt0/fer/Dej/befwNojc3U7wa08d7vXfuaAr6E1tCNewoKoGNHGDXKJgcW1P4Nbt3ano8ZY/MGRERkZyln5PPebwFWpKEskkcaunHPli02qW/ECLjiCnjuORv379LF0v+2b5+2IouINHthdtk7y3v/f6lc1DlXBvT03r/e4JJJzgta5JMmWSrdINFOIi1bQlmZteo3bYJzz4V27dJbThGRXBGme/9e59w7zrkRzrmOiU50zh3rnJsFfAoc1CgllJwVuZxv5kyboZ9s1v2WLZbTv6gI2rSx9371lVLvioiEEaZ7fx9gHDABuwFYiO2g9xWwFZvB3wcYCLQDXgZO9N5r+Z6EErmcr7Awfpd/QYH1Bpx1Fvz2t3DMMbDbbtbdv22bdfFrAp+ISHxJW/re+wrv/QSgO/AjYAEwAPgxMBqb0V8ITAMO8N4PVsCXhorM4BdM0ispsW79YcNsdn51NVxwATz0kPUWtG1rM/uXLbPgLyIisYWeyOe93+acexH4U+1kPpFGF3T5jxkDRxxhKXkvvdQC+qOP1gX1mhrbUe+ZZ+CPf7SbgS1bYMkS6N49o1UQEclaYXbZK3TO3eqcWwesAjY65/7gnGuf9tJJ3urQAT7+2LbNrayExx+3G4DosfsFC2DwYBsOaNXKegSWLKm/HHDQIHuIiOS7MBP5RgDjgbexXfT+BHwfuCeN5RIBbOOce+5JnK73v/+F73/fJvUFE/y2b7esf19/DStW2I3Agw9qIx4RyW9hgv5lwIPe++O99z/33p8DXAH8yDmnDXYkrcLsquccfPghnHaatfwLCuw9t94K3brBp59aj8Ho0dC1qw0LxJrtrx4BEcl1YYJ+H+CpqGNPYpP3ejZ6iUQihNlVz3vYay9r2Q8ZAr//vb1v9mwbEgjW/5eX27j/lCkwfnz9a6xbpx4BEcl9YYJ+G2Bj1LFgt722jVsckfqCHP2JlJZaPv5LLrGJfqNHw69/vXfcIYGKCksItH693TDcfLP1AITtERARaa7Czt7v5pzrE/G8MOL4+sgTvfefNUbBRMBa7ldfnfic6mob07/4Yth/f9uK96WX9kr4nsJCeOopWLrUWv6RO/4FmwBNmWI/b7ut7rWg+3/evJSqISKSFUJtuAM8DSyKeHxUe/yZqOOLGrl8kueCHP3xWvulpfCTn0Bx7eySiy+2HPytWydesF9RAZ9/bi3+eMMHkT0CoCEAEWn+wrT0hzX2hzrnLgeuBcqAD4BrvPf/inPubOCSGC9VeO9bN3bZJPvEytHfurW18MeMsW74FSushd66NbzxBowevYDbbz8y7jWdg1/9KvkkwcJCmyOwbFn9zx892nogxo2z8jmX+DoiItkgadD33j/SmB/onDsPy953OfBK7c+/Oef6ee+XxnjLKOC6qGOvYul+JQ9EJux5+unYu+rttResWmXd9dXV0L791oTXrKmxZYBBV348FRWW8vc//wk/BCAikq1S3lq3EYwBZnvvH6x9fpVz7mRgJHB99Mne+w3AhuC5c+5obEXBRU1QVskikTn6ozkHM2bUtcaTcQ42b05+XkkJvPaarfuPJRgCGDtW2/qKSPYLO6bfKGrX9Q8A5kS9NAc4KuRlLgM+UH5/iTR+fN2EvDBb9Iadlb9tW/Kd/4JJgSIi2a5Jgz7QCZv5vyrq+CqgS7I3O+faAecCDyY7V/LHunWJJ+RBXcKeVJSWwtFHJ84GCPa5K1emdm0RkUxwvgkXIjvnugJfAsd571+OOD4euNB7v1+S918BTAa6eu/XxjlnODAcoHPnzgOeeOKJ0OXbvHkzbdq0CX1+c5SLdVyzxibaRbbwu3ffzBdf1NUzmGhXXQ1//3sv/vGPnnjv2Geftfzwhwtp27au/76gwHoCunSxVQHR145WUGBzCjp1auyaJZaL32UsqmfuyIc6QubrOXjw4AXe+4ExX/TeN9kDKAaqgHOijk8HXgrx/neA34T9vAEDBvhUzJ07N6Xzm6NcrOOECd47572FantMmjS33vOwj6OP9n7iRO/feMP7DRu8X7vW+1atEr+nVSvv161r+nrn4ncZi+qZO/Khjt5nvp7Amz5OXGzS7n3v/TZgAXBi1EsnAgnH6J1zhwEHo659iRIma19xcfKx+aIiOP10y+zXpYtt4LNli60aSJQnYNw4TeITkeahqcf0AaYAQ51zP3HO9XXOTQO6AjMBnHOPOucejfG+4cAi7/28piuqNAdDhtTfSjcW56wbPpGqKpuQ9+WXNv6/2242Xj9sGFx1lW3dG1yjdWt7PmZMXR4BEZFs1+RB33v/JHANcBPWXX8McKr3fkntKT1qHzs459oC5wO/arKCSrMRJmvftdfaI945xcUWyN96C048Ef7yFzteUmI9BEOHwv/7f7YscMIE2+534UJL3NOnT/0MfdqtT0SyVSZa+njvZ3jve3nvW3rvB/iISX3e+0He+0FR52/y3rfx3t/V5IWVZmHCBGt1J2qNxzqnpARatoSRI2H+fAv4GzbAT39qNwnl5Rb027a1EfyTTrIbjKVLoW/fnTfpGTfOhgWUqldEslFGgr5IYwuy9i1fDjNnWgC+5x5Lz3vbbfZ69Dm33GIpfF95xQL87rvDww/D7bfbjcBvf2tB/q237L1t2tjPMWNg8uT6OQGCbXsnT9ZufSKSvTKRkU8kbYKsffPmxe9ij8zsV1MDX31lLfLWrW0sf9gwOPJIuPJK68I/80wL4FddZWP8Dz8MWxNk+Q0CvFL1iki2UUtf8lpBAXTuDN26WRKeIBHP/vtbl35ZmU0SnDTJgv/DD6ee5Cd6tz4RkUxR0BfBAnzv3jZ+v2mTtfy/+soC/PDhtoTv7bdh6tTEmf/iUapeEckGCvoitVq0gO7dbQLegAE2Ge+LL+Dxx2HtWpu4V1XVsGsrVa+IZAMFfZEIt9wC999vY/bBJL2KCtt4Z/Fim9jXEKWl1lsgIpJJCvoitZJt3FNZCS+9ZBn7kiX6iVZdDeeck/gcre8XkXRT0Bep9fTTySfpFRZCv342k78o5NqXMKl6162z5YVa3y8i6aSgL1Jr5crkk/QqK2H1avjZz+Ddd6FHD0vwEwjyAYAF+5Yt4eKLLflPrPkA3ts6/q5dtb5fRNJP6/RFagUb9wTr62MpKbEWe2Wl/Zw/346/8ALccEPdZL22beH6622ZX7t2djPx2We2PHC33WDwYDvv2GNtHf+WLXWfofX9IpIuaumL1AqzcU9NjaXoLS6GjRvrJvt997swdy5cdJE937QJHnoIPvzQ1uefdBKccAL88peWAfDLL+0m4M474/cuaH2/iDQ2BX2RWmE27hk3DvbYw5b2lZVZYA4S+uy2G/zv/8L//R/svbd11w8ZAt/8pnXbf/GF5f//9rfhv/+FZctg+/bEZdL6fhFpTAr6IhHCbNwDNm7frp0l9GnVylr2QS/B4YfD3/9uP8F6A4Kx+WBcP+xYvdb3i0hjUtAXiRC5Kc83vgG9eu28cU+kFi1s0l1ZmY3LB131lZXwzju7Xh6t7xeRxqSJfCIxdOgAH38c7lznrGu/tNRS927YAM8+m3qO/lgqK+GRR+o2CBIR2RVq6Ys0kqIia/HvtZd1yQdj/Q0VrBT48kut3ReRxqGgL9LIWre2Xfoi1++noqTEegm2bbOZ+1q7LyKNRUFfJA3OPbduOV8qOneGo46yuQLV1XXXKC+3OQNTpsD48Y1bVhHJHwr6ImmQbPlfPBs3wrx59ZP1RNLafRHZFQr6ImkSa/lfkK8/OlXvJZfAySfbPIBkCYIKCuCJJ9JXbhHJXQr6ImkSa/nfjBmWpa9nT+jWzbrq334bJk6EX//ahgWSqayEhQttzX+yGwQRkUhasieSZrGW/332mSX0Wb3aJuZ5bzcJAwfacr9EM/9LSuyGobrartOpky0ZjLdEcN06OOIImxh4ww2WJbBDh8arn4g0H2rpi2RAsLa/d29blrdpkwX6005LPgGwqgrOOMO6+UtKLDfA559bfoDI92oHPxGJpqAvkkGFhZbLP0jnW1BgiXgSLffbts2yBG7Y0IKCAmjTxuYFrFplwT/YCGj8+Lod/LQKQERAQV8kK7RsaV32PXpYa3zoUDsWTAAsLbVlfC1a2PMHH4SLLz6cKVPg+9+3uQBt2tjufytWWArgSZNS28Fv0CB7iEjuUtAXySKlpdbq/9//hVdftex+3brBkUfaDUAwca+gACori5g8Gd56yyYHPvywDRO0bWsb/kTvExAtcge/devsZmHJEmX/E8llCvoiWSYY7+/f3wL6974Hr70GW7fWddPX1MDIkW/v+PPGjXDTTXDQQbYSYM2a+Gv9AxUVFug17i+SPxT0RbJUYaHdADz8cOzZ/HvvvWGnY9XVMH06/OlPNkcgkdJSeOONxOP+vXqpy18klyjoi2Sxp59u2G59ixYl3/CnqgpefDHxuP+yZTY5UF3+IrlBQV8ki61cGT8oJ1KQ5F92SYmt3U827u89LF1a1+Xfsyccd1zq5RGR7KCgL5LFunRJPX8/WFf9D35g749UVGQz/IcPh0MPTT7uHwi6/Jctg3ffVctfpLlS0BfJYkOGNCzVbmmpzfh/801L7xuM71dVWS/Apk22/C/V7X+9tyRA11yjyX4izZGCvkgWa+hufZWVcPrp1n1/8snw3//CM8/Ad75jLfaHHoK77grf0o9WUaEkPyLNkYK+SJaLtVtfkKQnFucsUc/TT1tu/8Chh8Ijj8A//gFnnWU9CMlS/iajrX5FmhcFfZEsF2u3vvvug86d498IbNpkCX6OOMLeGzmTv29fO9a9O7Ru3bDVAZEik/yISHZT0BdpJoLd+j7/3Cbide9edyPQrl3dTPxgjL2iwhL6PPoo3Huv3QhUVFi3/re+Ze8tL7eJfUFOgIYoL7eEQKlS2l+RpqegL9KMdegAr79uwX3bttjnVFTAAw/Ybn7Tp8OsWfWz+1VWWld/y5Zw4om2VW8qnLOMgKnM6FfaX5HMUNAXaebCJPApLITf/Q5mzoyftGfLFnj5ZZg3z8b8k631D3gPa9eGm9Gv7X5FMktBX6SZC5PAp6IC5s4Nd3Pw3HM2HPDeezaEkGjSYPRnbNkCkyfDtdfC9u07n6PtfkUyS0FfpJkLk8AneD3ZzUFlpY31gw0H/PvfNodg5EhL7BNGZaVNNHznHbtWRYW14NetS327XxFpXAr6Is1cmAQ+1dVw/PHJbw5KSqCsrG7Sn/c2Zn/TTZaJ7847YeDA5F3/hYU2TBBk8fvsM1suGOZ9Wgkgkj4K+iLNXLIEPqWl9vrFFye/OaipsZUBvXtbS7+iAjZvtkx+7dvDj35kM+6Tjb1XVNiwQ6tW0LatrRBYvDj5JkDB+3aVVgaIxKagL5IDYiXwad3ano8ZY6+HvTlo396CdKdO0KeP5QOoqrLW/5YtsOee4dL3TpsGd99tG/acey78+c/J31dauvN+AanSygCR+DIS9J1zlzvnPnfObXHOLXDOHZvk/GLn3ITa92x1zi11zl3dVOUVyXaxEvjcc48Fv9tuq1uDH+bmIFJhoeUA6N0bevSw8447Ltx+ANXVMHWq7QHwzjsWfLduTf6ec85JsfK1tDJAJLmQU3Maj3PuPGAacDnwSu3Pvznn+nnvl8Z52xNAd2A4sAjoDKS4VYhI7gsS+MQT3ByMGWNL/VautJb1OedYCz/R+0pK7LHHHnDllTBjRvLueucs2G7ZkjzPf0kJDB1qE/mcs1Z/2JUDUH9lQKC83H5OmWI/b7st/PVEclGTB31gDDDbe/9g7fOrnHMnAyOB66NPds6dBJwA7O29X1N7eHFTFFQkV3XoAJdd1rD3tmhh3fYlJTbbfts2mwtQVGTDAJHCtK5btbLzhg+3pX7V1bZnQE2NfUZNjV030eqBYGVAvBuLigrLGjh3LrzySvzrBPMA5s1LXm6R5qhJu/edc8XAAGBO1EtzgKPivO1M4D/AGOfcF865Rc65Xzrn2qSvpCKSSPRwQvfuDb/Wli02d2C33aznoajIhhzatrUeiE8+sV0Cv/jCJhXGGloIk6DIe/joo/jj/JoLIPmgqcf0OwGFwKqo46uAeNN3+gDHAAcDZwNXAicDs9NTRBEJKxhOGD/e0vimomVL2/lvt91g4UK7iRg40JYgPv645Qf46itL8vPnP8OaNXaTEesGIEyCIu/h6693HufXXADJJ8434d9o51xX4EvgOO/9yxHHxwMXeu/3i/GeOcCxQBfv/YbaYycBL9QeWxV1/nBs7J/OnTsPeOKJJ0KXb/PmzbRpk9sdCPlQR8iPemZTHVesqEvqk4rOnaFDhwJef70jzz7bmfff353qamuLFBTUsO++6xg8eBndu2+mVasq9tjD5iAEwRrs5qCqym4OUtkquKDAPh9g1arY7w3O6do1tXp98on93Hff8Odk0/eZLvlQR8h8PQcPHrzAez8w5ove+yZ7AMVAFXBO1PHpwEtx3vMI8GnUsb0ADxya6PMGDBjgUzF37tyUzm+O8qGO3udHPbOpjrNmed+6dRCKwz1KS72/807vR43yvmVL7wsK7HjwM9ajRQvvR4zw/ssv7fHBB9736uV9WZn3RUWpfT7Y57ZqlficVq28X7cu/O9i7Vrv993XyjVrlj0Pc042fZ/pkg919D7z9QTe9HHiYpN273vvtwELgBOjXjoReC3O214FukaN4Qf3z0sat4Qi0hBhsgJGq662oYHoXf+iW9x77103uL59u20a1LevJRs65BDLA7BiRcPK7X3y3oGwWQLDDBMkOmf5cg0lSPplYp3+FGCoc+4nzrm+zrlpQFdgJoBz7lHn3KMR5/8W+Bp42Dl3gHPuaGzJ39Pe+9VNXXgR2VmyxD/RSkvhiivgt79Nvuxv5Mh3dzq2cSO8+GLdygGov3IgyEuQzLZt8bckDkRnCYyX7S/MZkKJzlm1ShsOSfo1edD33j8JXAPcBLyDTdI71XsftNp71D6C8zcD3wHaYbP4fw+8BPy4yQotIknFSvwTrLMPgnBkIqD99ks+476hvLcJgsXFic9zLvk5AIsW2Wz+eDP8w2wmdPfdic+pqYFf/AJ69tTqAUmfjGTk897P8N738t639N4P8BGT+rz3g7z3g6LO/9h7f5L3vtR73817f4X3flOTF1xE4oqVFXD6dJtot88+O2cJXLUq+Yz7XRWdNyBamO59721J4B57WAriWF33Tz0VbslgmM9aurT+tY87TvsISOPJRHIeEclhsbICxsoSGGwJHGTNa2wbNzbetWINQURm+zv00OQ3MMmGEWJde/JkS1C0227W+h8yxH6/Ig2lDXdEJCMaMvkvrJISuOoqGDXKNg5KJFlvQDIVFfDqq8k3EyouDjeUEKmyEtauVe4AaTwK+iKSEWEm/4WdkBetpgZGjoSf/Qzefdcm/XXsGO69DfnM4mJbWZDsugW78D9u9KRAkYZQ0BeRjEm069/YsTY3oKgotRZySQlceqldJ7D//nDddeFWFzSkFV1RYZ+baNvia69NbYVDos+aNMk2JhJJlYK+iGRMoi2BJ02yrHUHHhiuhVxaajcLo0bZNWtqYNMmS9e7datt4pNseeCu1MM520a4Zcv42xbHuslpiG3bwuUOyFXxlk1Kcgr6IpJxweS/zz+33f8it/ktLEzcQnYO2rWDqVPtZuGOO6wrv1cv28inrMwCcbt2FmwTadWqYcsIvYcNG+C11+zPbdtCt25w661Wr5tuqitr5E3OXnultn1woKYG/vKX/FzWp42Rdo2CvohkvUTDADfeaP/xR98sgAXUtm1tAtzIkcm77r236ySblBdPZWVdwp+zz4azzrJx+CVL4PDDrSdgwwa7/sKFtjzv5z9vWJf/88/n18Q+bYzUOBT0RSTrJRoGuO22cJPvdt89cY9BMBfgZz+D4cPrd9OXlFgPQEFBuM+qrIT777cA36aNrRBYswaWLYN774X33rPAtXQpXHONrTRIdaLftm3ZPbGvsbvgw2Q8lOQU9EWk2Ug0DBBGoh6Da66xjHhFRXD55fDyy9CjB3TvDv/zPxao33vPbjjat08+uXD7dlu/P2AAHHSQtUy//NKGH449FmbMsOC1bh0MG2aTDYPPO+mk8L0N2Tixr7G74MNkPMy230G2UtAXkbyRqMdg4kQL5nvtZa9985uwYAH861/wve9ZS7+4GObNg5/8JPkSPbA8BCtX2s+g+7miwiYWzphhY/0tW9oQRIsWdqPx0kvwy1/C0KH2WpiehbCbAqVbsi74hnr66eRzLbLld5DtlJFPRPJOrKyBkQoKrKVdUmKTAqurLVBXVtqKgN12s96BRKsBSkqsCz5eAqLt2+E3v4FnnoFvfQu6d+/NSSfZnzt1shuCK66AY45J3oKN3hQoUyK74AORmQv79m3YdVeuTJ7xMJ2/g2CYYt689Fy/KamlLyKSRGGhzQXYfXfrHQgzKXD79nAb/pSXW2/C737Xk2HD4OCD4YgjYMQIePJJOP/85F39rVrZY8MGuxHZ1SyDDRGmC37lyoZ1wQcpmxMpLbXzGluurRZQ0BcRSVGnTsknBR5ySP0WbzwjRlgwOffcpRx5pF1z2TJ49lkbipg5M3l+gZoamwewerW997PPrHv9yy8tSFVU2E1IshuVXZl8F6YL3rmGdcGHSdlcXQ3nnJPadRPVN1dXC6h7X0SkASZMsJ+TJlk3fk2NTQqsrrbJgl27wgcfJO6WLimxXAKnngo9e37GAQf0oKrKkhK9+y68/bb9/OCD+EGmsNB6Bj77DPr1s/kBYOXZts0+P5jtXlBg8wSCREYtWtijoKCuRbttW/3NfcJ2bYfpgq+paVgXfJCyecqU2J9RWmq/81QmdsarbyDZUAXYTVlzo5a+iEgDJJoUePvtcMEFybfSra6GwYPrB5aiIgveF1wAd90FL7xgNwHnnWevxbrGSy9ZToD994ejj7Ylh/feaxMD16+3ZYNt21pwDBIJLV9uXdaLFsGVV1oSo6BFe801dtMyblzdecm6tsN0wRcUNLwLPtHKiyDjYaR4rfgwLfhcXi2glr6IyC6INykwTOv06qth333tde8tZbD3FtSKiupa4aWldWvRTz/duupHjIC997YW/nvvWW/Axx9bEFu8GP7617rPat/eJtH162c/+/aF/faznoa77oKHHrKJioGgvJMn282N93YjcPXVVqcJE3ZeVTBkiL2eiPepd8EHgpusMWOsZ2PbNrjhBrtedAs/USs+TAu+R4/wqwUuu6xh9ckUBX0RkTRJNgQQBM/ddoOPPrKu/u3b7dzycgu+1dV1uf1LSqz1HpnE59vfrvvz9u3Wcn//ffjwQ3t88IG1SOfPt0fAOVue+MUXiXskIpcagt0IPPSQBcbnn7cbk6IiS3Oc7CanS5fUcytEDy/EuskKzpk714J65O979Oi6m5XRo+21eHMtghb8mDGZXS2QTgr6IiJpkkrrFCx4FhVZcG/Xzo5VVVkw37rVAk3k7Hzn6noECgvtZ79+9gh4b63ehQvrHh99VJcRMFWVlfbw3oYQTjqpri7DhllvxcyZ8ec5JBMZ5JONu0P9c844A/75z/it+LfeCteCX7bMblKC98aSrtUC6aagLyKSZsnyAiQSeSMQBNfgRmDbNgvAwc0AWDAO3lNUZDcGXbva44QT6q67datlGnzkkYaVK5i7cNtttoTx2mstyF99NfzoRxagg2GIU0+1FQ+ffGLzCYLegcLC+kE4COBbt9owxosvxm6xBz0o0a36xYvjl7eiAubMSb4KoKLCsiKmY7VANlDQFxFpZqJ7BAYNsmA/Z44F2i1bLHgFM/eD4YGgN6Cw0GbxH3CAtViTdWXHE8wDmDbN9hr4wQ9siKJPH/j1r6FnTyuj93ajUl1tywojVyIUFFg+g6lT7RpBAF+ypP5nRY+7B38Osywy8rMKCuwz4nEO/vCHxl8tkC0U9EVEmrHI7u1HH7UW9pAh9trcuRZsq6rqegUiu+cHDUreog1r2zZLJhStrAx697ZHaeleHH64rXTo2RMuusjKcdhh8KtfhQvgFRVw990WnFMJ+EEZY62AiOS9XXevvawHY/r0+PMxmiMFfRGRZsj72JPWrrrKglPbthZIg3HwyHkCNTV1NwNXXWWBLVkCoDCCrvq99rKgvGyZ3ZCsWAGvvQawNw8+WP/8wkJ4443Ukt0kWwoZT+vWdqMzd27i3o2lSy2wV1dbKuQ//9l6UBLNx2gutE5fRKQZirfV7NatsHatdY/HyyAXdKmfeqoF3LFj669/T9Yajqe62h6rVtkmRZ9+Cq+8Ao89Zi3jM8/8guOPr5uMV11tNyypZrfbvj3chkexyvfAAzBqVP36tmhRd05QlmDb3vvvtxwJyXZ2bOythNNFQV9EpJlJljwmkGi/+WBYYOlSW3734Yd1SYamT7cx7cjAmIrKSpvBX1Fh3frHHw+XXgqXX/4p3/xm6t3yjaGkxFYXlJfbDoYvvWT1LitLfNMRJhFPdH7+6ursvQlQ0BcRaWbC5LmPFBm44mWk69cPzj3Xkv0MH27j5kG2wb32qt8aDqOgwCbEbd5c99i+PdxeAo0pmLy33362guCf/7Rli2DLAkeNSl4356zcK1bA11/bCoTycqvHDTfs/Lt8911LmLR4cfZt0qMxfRGRZiZMnvtoQQa5pUvD55SPXGp4883xZ7PHEgw79OplLd+qKnjnndRuVmIpKbGbkq1b4eGH62cSjCUY+njnHXtEChIeJbsJqayEr76qWxURTH6cOhVmz975d+m9DbGsXVuXyXD0aFsiWVjYsN6TxqKgLyLSzAR57hMlj4lWUWHj0vfckzwj3dixO49dR2YX3Lo1+Th8aal1nQfbCw8aBGef3fBWfkmJBfBLL7VJdsGyw5kzrQehpsY+s7racgOMHm3LA5cvt8eKFXU/g8fq1eFvYh56yCYA7rmnPXbbzVrxybYxDq4/dar1tFxzTV1SpeDRoYMNpTQFBX0RkWYmTJ77aKWllnK3oTnlI7MLHnqoDQMkCvyRyWuCMe+aGutKT7ROPtC6tQXU0lJbiTB2rE08LCmx92/dCpdfDhdeaMMS27bZ89NOsyBaWAjdutkjnqoq+O9/4bvfTT4xcP162/UwjDvvPGynY5WVdpNw7LE2l6BjR6tLebkFfAV9ERGJKdlmPrFUV9vY/K7mlO/QwcavE3X3B8lr2rWz84JlhUG+gESCPQFuuinx8jjv7SaiutomIVZX12UpDB7RrfALL7Tr/+53dd3s++5rNwuzZsXuhSgpgR//2LIMrlplvSW33w6bNsWvy1dfxd5usKoKfvjDuuetWlnwv/hiuOOOxL+XxqKgLyLSDMXazCee0lKbxPb0042XUz7MZkKxdrRLJLhZCLNPfZBhMFHPhfdWnpoaWLPGWutbt8Kf/mSt+9JSK/vIkfbzoYfqhgoihxPGjbPPeeKJ+sMJ8Ywb9waTJu3c2gf73bZoUTdHYPnyJp7Y2HQfJSIijSXobg9m2PfsaV3bsfabHznSxr8rK5O3tMPmlI/+/F69bL7AihV2fP36cMsKI8vZ2JnughuD226r24Z42TK7GRk40IL83nvDPvvAL39ps+379LHu94kTbbfC666za91xh/UGbN2aPDlQly6xK11QYDcTr79uwwqLFsFBB8GrrzZenZNRS19EpBmL3sxn3bq6Hf2uv94Cy3331bXGEy1Pa0hO+XibCYVZVuicdW/fcUf6Mt3F6m2ItVqhoMBa4YsW7XyNdevsBmFX8wvU1Ng1Hn7Y5icUFNjyv5qa+LsINja19EVEckgQhD//3Fq1M2bUz9oXOWHNOfuZjpZ22GWFo0YlznS3K5IlMQqTeAdSz4sQj3PWE/KLX9hkyAED7Dtatix+9sTGpqAvIpKDwmbt69Gjfrd8cCOwq4JlhYmke0/6MME6WK2QSNgbmHbtbIjFucQpfisr61IWR6ZQjpc9sTEp6IuI5KAwAa+01GbJp6OlPWRI5vekDxOsk61WgHA3MK1bWxbDv/wFDj64LpNhqsL2PjSUgr6ISA5qrIDXUMGywnjBsrTUXk/njnWN1duQ6g1MYaENsdx8c11yolSE6X1oKAV9EZEclA3d6xMm2DyBWCsKmmJP+sbqbWjoDUxD0iVDem/GFPRFRHJQNnSvRy/rKy5Oz/yBeBqzt6EhNzBhbrzilStdN2MK+iIiOSgbutcjy/Lxx/DNb6Zvpn48jdXbkCwvQawbmDA3XrGk82ZM6/RFRHJUmKx5uS5yz4Agf8ENNzQ8L0C8vATxzk01XXJDciWkQkFfRCRHNXbAa85SCdaNKdaNV2mpZfZzzp435c2Ygr6ISI7LVMCTxDde3jf9zZiCvoiISJrFu/Fq6puxjEzkc85d7pz73Dm3xTm3wDl3bIJzBznnfIzH/k1ZZhERkeauyYO+c+48YBowETgEeA34m3OuR5K3HgCURTxibIsgIiIi8WSipT8GmO29f9B7v9B7fxWwAhiZ5H2rvfcrIx4NWAghIiKSv5o06DvnioEBwJyol+YARyV5+5vOuRXOuRedc4PTUkAREZEc1tQt/U5AIbAq6vgqIF7+oaAX4GzgLOBj4MVE8wBERERkZ86nc+Pe6A9zrivwJXCc9/7liOPjgQu99/uFvM5zQJX3/nsxXhsODAfo3LnzgCeeeCJ0+TZv3kybNm1Cn98c5UMdIT/qmQ91BNUzl+RDHSHz9Rw8ePAC7/3AWK819ZK9NUA10DnqeGcgle0F/g2cH+sF7/0sYBbAwIED/aBBg0JfdN68eaRyfnOUD3WE/KhnPtQRVM9ckg91hOyuZ5N273vvtwELgBOjXjoRm8UfVn+s219ERERCykRyninAY865N4BXgRFAV2AmgHPuUQDv/cW1z68BFgMfAMXAj4AzsTF+ERERCanJg773/knn3O7ATdh6+/eBU733S2pPiV6vXwzcDXQHKrHgf5r3/rkmKrKIiEhOyEgaXu/9DGBGnNcGRT2/C7irCYolIiKS0zKShldERESaXpMu2WtqzrmvgCVJT6zTCVthkMvyoY6QH/XMhzqC6plL8qGOkPl69vTe7xHrhZwO+qlyzr0Zb21jrsiHOkJ+1DMf6giqZy7JhzpCdtdT3fsiIiJ5QkFfREQkTyjo1zcr0wVoAvlQR8iPeuZDHUH1zCX5UEfI4npqTF9ERCRPqKUvIiKSJxT0RURE8oSCfi3nXA/n3LPOuXLn3Brn3C+dc8WZLteucM75GI8RUed80zn3knOu0jn3pXNuvHPOZarMyTjnpjnn3nTObXHOLY5zTtI6OefOds596JzbWvvzB01SgZCS1dM51yvO93ty1HnHOecW1F7ns+jvP5Occwc7537nnFtW+1197Jz7mXOuIOq8Zvt9hqljjnyXezjnXnDOLa/9DpY556Y759pFnddsv0sIV8+s/z6993n/AAqB94B5wLewXf+WA/dmumy7WC8P/AToEvEoiXh9N2xL498DBwJDgE3A2EyXPUGd7gWuwibKLI7xetI6AUcCVcCNQN/an1XA4ZmuXwr17FX7/X436vstjjinN1Bee62+wGXAduDsTNevtnw/Bn4JDAL6YNtlbwJuyJXvM2Qdc+G73B3bPG0A0BM4AfgI+H2ufJcp1DOrv8+M/xKz4QGcAtQAe0Uc+xGwBdgt0+XbhXp5YEiC10cCG6l/I3AT8CW1kzyz9QGMI3YwTFon4Eng71Hv+wfwu0zXK4V6Bv+xDEzw3juBRVHHfgXMz3S9EpT5LmBBrn6fceqYq9/l1cCKXP4u49Qzq79Pde+bI4GF3vtlEcdeAFpid3TN2TRnwxX/cc6NiOo6PRL4l/e+MuLYC9hWx72aspCNKEydjgTmRL3vBeCotJeu8f2fc261c+5V59yQqNfi1XOgc65F0xQvZbsB6yKe5+L3GV3HQM58l865rsBZwEsRh3Puu4xTz0BWfp8K+qYLsCrq2Bqguva15mo8cB7wHeAJYDJwQ8Trseq9KuK15ihMneKd05zqvBnrBTgXOBV4EXjSOfejiHPi1bMIyw2eVZxz3wKGAvdHHM6p7zNOHXPmu6ydv1CBtd43AcMiXs6Z7zJJPbP6+8zI1rrSNLz3t0U8fcc5V4iNkd2eoSJJI/Her8Fu4gJvOuc6AT8DHs9MqRrOObcf8Fdgqvf+D5kuTzrEq2OOfZejgf8B9gXuAKYCP81kgdIkbj2z/ftUS9+sBDpHHeuETfBb2fTFSZt/A7s554K6xqp35GvNUZg6xTunudY58G9gn4jn8epZRRbtdOac2x+bRPuE9/66qJdz4vtMUsdYmuV36b1f6b3/yHv/ZywIDnfO7VX7ck58l5C0nrFkzfepoG/mA32dc90jjp0IbAUWZKZIadEfm5y4vvb5fOBY51yriHOClQuLm7JgjShMnebXHiPqnNfSXrr06g+siHger55veu+3N1WhEnHO9cOC4VPe+9ExTmn232eIOsbSn2b2XcYQxJeWtT+b/XcZR3Q9Y+lPtnyfmZ75mA0P6pbs/RM4BBsD/5JmvGQPOANbBnIgsDe2dG8DMC3inHbYHecTteedhc2uzeYle9/A/gFNwf6z6F/7KA5bJ2xSUBVwHbA/cD22XCYrlgWFrOclwA+x5T77YWOI24DREdcIlgVNrT3vJ7XnZMsyrwOwccwnqL+0qUsqf0ez+fsMWcdc+C5Pr63HgdikvNOAD4mYjd7cv8sU6pnV32fGf4nZ8gB6AH8BKoCvsbW1LTNdrl2oz8nA29gkk3LspmYUUBR13jeBl7EegBXALWTxcj2sxeRjPHqlUidsjfBHtf/QFgJnZbpuqdSz9j+WD2u/243Am8CPYlznOOAtrNfqc2BEpusWUbZb49TRp/p3NFu/zzB1zJHv8jtY63U9UAl8gi1L65Ar32XYemb796kNd0RERPKExvRFRETyhIK+iIhInlDQFxERyRMK+iIiInlCQV9ERCRPKOiLiIjkCQV9kWbKOTfUOecjHuXOucXOuT865851zrkGXndQ7fUGNW6JE35mvbqk6TNuiviML9LxGSLZTkFfpPk7B9uq81TgZizZx++AvzvnSjJZsAY4C6tLOjxce+3n0nR9kaynXfZEmr93vPefRjx/zDn3FPAUcBdwVWaK1SBve+8Xp+PC3vsvgS+dc1+l4/oizYFa+iI5yNvWrX8CLnPOlQbHnXOlzrk7nXOfO+e21f680TmX8P8C59xJzrnnnHMrnHMVzrn3nXNja7drDs551jn3doz39nbO1TjnRqRaD+dcr9ru+KFRx3cagnDOfdc595pzboNzbrNz7mPn3PhUP1Mklynoi+Su57CdvwYCOOeKgBewzT2mAacAv8KGBO5Ocq0+wIvAj7FNRh7B8sr/IuKc+4H+zrnDot47HMtD/puGVyUx51wf4M9YDvPzgO9hmxW1TtdnijRH6t4XyV1La3+W1f68ADgGOM57/3LtsRdr5/vd4py703u/OtaFvPczgz/XThD8F1AMjHPO3eC9rwGeBz7D9hd/o/bcFsAw4Dfe+02NWbko36otz0jv/cbaY/9M4+eJNEtq6YvkrmD2fjAb/mRgCfCac64oeABzgBbAEXEv5FyZc+4B59wSbPez7cDtQHtgT4DawP8AcL5zrl3tW88EOtceT6d3asv0hHNuiHNuzzR/nkizpKAvkrv2qv25ovbnnkBPLDhGPt6ofX33WBepHe//M7aX+O3A8cCh1HXtt4o4/ddAIXBR7fMRwBve+53G+htT7UTG72L/pz0GrHTOve6cOy6dnyvS3Kh7XyR3nYbtW76g9vnX2Jj3uXHOXxzn+N7YvICLvPePBwedc2dEn+i9/9o593vgp865F4DB2ByCXRX9f1WbGJ89F5jrnGsJHA1MAP7qnOvlvV/TCGUQafYU9EVykHPubGwy2zTvfUXt4eeBs4HN3vuPUrhcMPt/e8T1WwAXxjl/BjAfmyS4AXgihc+K58Co53GHIrz3W4F/OufaYCsYegMK+iIo6Ivkgv7OuU7YRLYeWDf8OcDfgesjzvsNNqnuRefcZODd2vfsjd0gnBlxgxBpITYX4BfOuWos+I+OVxjv/eu1S/e+Ddwb55qp+olzbhnwNtbrcGXt8e8655YCJ9V+3nPAMqATVvflwPuN8PkiOUFBX6T5e6r25xZgNfAWcD7wtPd+R0pb7/1259x3geuwZXS9saV0/wX+ik3Q24n3fptz7kzgPuBRYC3wELY64MEEZTqExpvANxUYAkwEPsUmCE4ERgL/wG5gTgHuwOYurAVeAS703lc2UhlEmj0X8X+CiEijcM69CtR4748Nef5QLE3uN4Al3vuq2uO9sHkIw7z3s3exTA6bZPhr4ATvffdduZ5Ic6SWvog0itoJdN8CvgMcBXy/AZcJ0gk3aLOgJG4Ebqv985dpuL5I1lPQF5HGUga8BqwHJnrv/5zCe5/FlgGm06+xyYwQZyhDJNepe19ERCRPKDmPiIhInlDQFxERyRMK+iIiInlCQV9ERCRPKOiLiIjkCQV9ERGRPPH/AZslX+DeKM8LAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
- "display(expdata1_ramsey.figure(0), expdata2.figure(0))"
+ "display(expdata_hahn.figure(0), expdata_ramsey.figure(0))"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 17,
"metadata": {
"scrolled": false
},
From 0730710eb7e7777262bbcf33f5cf8a10def8e29a Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 2 Jan 2022 16:40:41 +0200
Subject: [PATCH 76/93] updated code + tutorial per instructions
There is one thing missing, The analysis of Hahn echo doesn't work for num_echoes=0.
---
docs/tutorials/t2hahn_characterization.ipynb | 154 ++++++++++++------
.../library/characterization/t2hahn.py | 2 +-
qiskit_experiments/test/t2hahn_backend.py | 7 +-
test/test_t2hahn.py | 4 +-
4 files changed, 112 insertions(+), 55 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index ce57269212..4d20f9df66 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -15,19 +15,18 @@
"\n",
"In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for the transverse magnetization to fall to approximately 37% ($\\frac{1}{e}$) of its initial value.\n",
"\n",
- "Since the qubit exposed to other noises (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequency estimation."
+ "Since the qubit is exposed to other types of noise (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequency estimation."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import qiskit\n",
- "from qiskit.utils import apply_prefix\n",
"from qiskit_experiments.library.characterization.t2hahn import T2Hahn"
]
},
@@ -46,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -67,9 +66,10 @@
"conversion_factor = 1e-6 # our delay will be in micro-sec\n",
"delays = list(range(1, 50, 1) )\n",
"delays = [float(_) * conversion_factor for _ in delays]\n",
+ "number_of_echoes = 1\n",
"\n",
"# Create a T2Hahn experiment. Print the first circuit as an example\n",
- "exp1 = T2Hahn(qubit, delays)\n",
+ "exp1 = T2Hahn(qubit=qubit, delays=delays, num_echoes=number_of_echoes)\n",
"print(exp1.circuits()[0])"
]
},
@@ -82,7 +82,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
@@ -181,7 +181,7 @@
"metadata": {},
"source": [
"### Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameters $A$ and $B$ is $0.5$.In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
+ "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. Since if there was no delay we would expect that the probability to measure `1` is $100\\%$, so we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
@@ -201,7 +201,7 @@
}
],
"source": [
- "exp_with_p0 = T2Hahn(qubit, delays)\n",
+ "exp_with_p0 = T2Hahn(qubit=qubit, delays=delays, num_echoes=number_of_echoes)\n",
"exp_with_p0.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5})\n",
"expdata_with_p0 = exp_with_p0.run(backend=backend, shots=2000)\n",
"expdata_with_p0.block_for_results()\n",
@@ -248,42 +248,15 @@
"metadata": {},
"source": [
"### Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increase, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, Hahn Echo experiment let us estimate $T_{2}$ better.\n",
- "Note, that the delay time provided is the for each delay in the circuit and not the total time."
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improve our estimate for $T_{2}$ better. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes.\n",
+ "Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
- " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
- "c: 1/══════════════════════════════════════════════════════════»\n",
- " »\n",
- "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
- "« q: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
- "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
- "«c: 1/══════════════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
- "« q: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
- "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
- "«c: 1/════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌─────────────────┐┌──────────┐┌─┐\n",
- "« q: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └─────────────────┘└──────────┘└╥┘\n",
- "«c: 1/════════════════════════════════╩═\n",
- "« 0 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"\n",
@@ -297,23 +270,73 @@
" )\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"num_echoes = 4\n",
+ "estimated_t2hahn2 = 20 * conversion_factor\n",
"\n",
"\n",
- "# Create a T2Hahn experiment. Print the first circuit as an example\n",
- "exp2 = T2Hahn(qubit2, delays2, num_echoes=num_echoes)\n",
- "print(exp2.circuits()[0])\n"
+ "# Create a T2Hahn experiment with 0 echoes\n",
+ "exp2_0echoes = T2Hahn(qubit2, delays2, num_echoes=0)\n",
+ "exp2_0echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
+ "print(\"The first circuirs of hahn echo experiment with 0 echoes:\")\n",
+ "print(exp2_0echoes.circuits()[0])\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Create a T2Hahn experiment with 4 echoes. Print the first circuit as an example\n",
+ "exp2_4echoes = T2Hahn(qubit2, delays2, num_echoes=4)\n",
+ "exp2_4echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
+ "print(\"The first circuirs of hahn echo experiment with 4 echoes:\")\n",
+ "print(exp2_4echoes.circuits()[0])\n"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 35,
"metadata": {
"scrolled": false
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\lib\\polynomial.py:659: RuntimeWarning: invalid value encountered in true_divide\n",
+ " lhs /= scale\n",
+ "Analysis callback <function BaseAnalysis.run.<locals>.run_analysis at 0x0000024CCD9BFEE0> failed:\n",
+ "Traceback (most recent call last):\n",
+ " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\database_service\\db_experiment_data.py\", line 299, in _wrapped_callback\n",
+ " callback(self, **kwargs)\n",
+ " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\framework\\base_analysis.py\", line 168, in run_analysis\n",
+ " results, figures = analysis._run_analysis(expdata)\n",
+ " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_analysis.py\", line 841, in _run_analysis\n",
+ " fit_options = self._generate_fit_guesses(default_fit_opt)\n",
+ " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\standard_analysis\\decay.py\", line 82, in _generate_fit_guesses\n",
+ " alpha = curve.guess.exp_decay(curve_data.x, curve_data.y)\n",
+ " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\guess.py\", line 188, in exp_decay\n",
+ " coeffs = np.polyfit(x, np.log(y), deg=1)\n",
+ " File \"<__array_function__ internals>\", line 5, in polyfit\n",
+ " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\lib\\polynomial.py\", line 660, in polyfit\n",
+ " c, resids, rank, s = lstsq(lhs, rhs, rcond)\n",
+ " File \"<__array_function__ internals>\", line 5, in lstsq\n",
+ " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\", line 2306, in lstsq\n",
+ " x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)\n",
+ " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\", line 100, in _raise_linalgerror_lstsq\n",
+ " raise LinAlgError(\"SVD did not converge in Linear Least Squares\")\n",
+ "numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares\n",
+ "\n",
+ "Possibly incomplete analysis results: an analysis callback raised an error.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hahn Echoe with 0 echoes:\n",
+ "Hahn Echoe with 4 echoes:\n"
+ ]
+ },
{
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\n",
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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -334,12 +357,46 @@
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
"\n",
+ "# Analysis for Hahn Echoe experiemnt with 0 echoes.\n",
+ "expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
+ "expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
- "expdata2 = exp2.run(backend=backend2, shots=2000)\n",
- "expdata2.block_for_results() # Wait for job/analysis to finish.\n",
+ "# Analysis for Hahn Echoe experiemnt with 4 echoes.\n",
+ "expdata2_4echoes = exp2_4echoes.run(backend=backend2, shots=2000)\n",
+ "expdata2_4echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
"# Display the figure\n",
- "display(expdata2.figure(0))"
+ "print(\"Hahn Echoe with 0 echoes:\")\n",
+ "# display(expdata2_0echoes.figure(0))\n",
+ "print(\"Hahn Echoe with 4 echoes:\")\n",
+ "display(expdata2_4echoes.figure(0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'counts': {'0': 1961, '1': 39},\n",
+ " 'job_id': 0,\n",
+ " 'metadata': {'experiment_type': 'T2Hahn',\n",
+ " 'qubit': 0,\n",
+ " 'xval': 0.0,\n",
+ " 'unit': 's'},\n",
+ " 'shots': 2000,\n",
+ " 'meas_level': <MeasLevel.CLASSIFIED: 2>}"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "expdata2_0echoes.data()[0]"
]
},
{
@@ -431,7 +488,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 26,
"metadata": {
"scrolled": false
},
@@ -460,7 +517,6 @@
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
- "\n",
"display(expdata_hahn.figure(0), expdata_ramsey.figure(0))"
]
},
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 9cb31a52f7..b90d0e67f9 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -129,7 +129,7 @@ def circuits(self) -> List[QuantumCircuit]:
"""
Return a list of experiment circuits.
- Each circuit consist of RX(π/2) followed by a sequence of delay gate,
+ Each circuit consists of RX(π/2) followed by a sequence of delay gate,
RX(π) for echo and delay gate again.
The sequence repeats for the number of echoes and terminates with RX(±π/2).
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 338137f512..ba2d47dd03 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -139,9 +139,10 @@ def _delay_gate(self, qubit_state: dict, delay: float, t2hahn: float, frequency:
dict: The state of the qubit after operating the gate.
Raises:
- QiskitError: Raised if initialization_error type isn't 'None'', 'float' or a list of 'float'
- with length of number of the qubits.
+ QiskitError: Raised if the frequency is 'None' or if the qubit isn't in the XY plane.
"""
+ if frequency is None:
+ raise QiskitError("Delay gate supported only if the qubit is on the XY plane.")
new_qubit_state = qubit_state
if qubit_state["XY plane"]:
prob_noise = 1 - (np.exp(-delay / t2hahn))
@@ -260,7 +261,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
else:
# Since we are not in the XY plane, we need to calculate the probability for
# measuring output. First, we calculate the probability and later we are
- # tossing to see if the event did happened.
+ # tossing to see if the event did happen.
z_projection = np.cos(qubit_state["Theta"])
probability = z_projection ** 2
if self._rng.random() > probability:
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 57abc9598a..b377b34712 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -28,11 +28,11 @@ class TestT2Hahn(QiskitExperimentsTestCase):
__tolerance__ = 0.1
- @data([1], [2])
+ @data([0], [1], [2])
@unpack
def test_t2hahn_run_end2end(self, num_of_echoes: int):
"""
- Run the T2Hahn backend with one echo.
+ Run the T2Hahn backend with 'num_of_echoes' echoes.
"""
osc_freq = 0.1
estimated_t2hahn = 20
From 35527b6d0f5c6dcaa9a44e5b61f4615b6c9db626 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 3 Jan 2022 18:50:28 +0200
Subject: [PATCH 77/93] fixed a bug for 0 echoes case.
---
qiskit_experiments/library/characterization/t2hahn.py | 9 +++++++++
1 file changed, 9 insertions(+)
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index b90d0e67f9..05adaeb5fc 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -167,6 +167,15 @@ def circuits(self) -> List[QuantumCircuit]:
circ.rx(np.pi, 0)
circ.delay(delay_gate, 0, "s")
+ # if number of echoes is 0 then just apply the delay gate
+ if self.experiment_options.num_echoes == 0:
+ if dt_unit:
+ total_delay = real_delay_in_sec
+ circ.delay(delay_dt, 0, "dt")
+ else:
+ total_delay = real_delay_in_sec
+ circ.delay(delay_gate, 0, "s")
+
if self.experiment_options.num_echoes % 2 == 1:
circ.rx(np.pi / 2, 0) # X90 again since the num of echoes is odd
else:
From ddef1802d168b93344edcac0d13666835b7460d5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 9 Jan 2022 11:31:34 +0200
Subject: [PATCH 78/93] fixed issue
added explanation about the frequency.
added experiment with 0 echoes.
---
docs/tutorials/t2hahn_characterization.ipynb | 186 ++++++++++---------
1 file changed, 103 insertions(+), 83 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 4d20f9df66..63a91667e4 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -20,7 +20,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 1,
"metadata": {
"scrolled": true
},
@@ -45,7 +45,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
@@ -82,7 +82,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -110,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 4,
"metadata": {
"scrolled": true
},
@@ -125,7 +125,7 @@
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -145,7 +145,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -154,17 +154,17 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.73150237e-01 5.03648507e-01 1.98283007e-05] ± [5.15527149e-03 3.03978247e-03 5.77292515e-07]\n",
- "- χ²: 0.7488240853426195\n",
- "- quality: good\n",
+ "- value: [-2.31726995e+03 2.31808006e+03 -5.66599325e-01] ± [nan nan nan]\n",
+ "- χ²: 45.44971077076316\n",
+ "- quality: bad\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 1.9828300732126065e-05 ± 5.772925151075391e-07 s\n",
- "- χ²: 0.7488240853426195\n",
- "- quality: good\n",
+ "- value: -0.5665993249904158 ± nan s\n",
+ "- χ²: 45.44971077076316\n",
+ "- quality: bad\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
]
@@ -186,12 +186,12 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -212,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -248,15 +248,49 @@
"metadata": {},
"source": [
"### Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improve our estimate for $T_{2}$ better. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes.\n",
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improve our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, We will add frequency to the qubit and see how the result change due to that (We can see Rabi Oscillations in the `0` echoes case)\n",
"Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The first circuirs of hahn echo experiment with 0 echoes:\n",
+ " ┌─────────┐┌─────────────────┐┌──────────┐┌─┐\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ " └─────────┘└─────────────────┘└──────────┘└╥┘\n",
+ "c: 1/═══════════════════════════════════════════╩═\n",
+ " 0 \n",
+ "The first circuirs of hahn echo experiment with 4 echoes:\n",
+ " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
+ " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
+ "c: 1/══════════════════════════════════════════════════════════»\n",
+ " »\n",
+ "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
+ "« q: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
+ "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
+ "«c: 1/══════════════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
+ "« q: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
+ "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
+ "«c: 1/════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌─────────────────┐┌──────────┐┌─┐\n",
+ "« q: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └─────────────────┘└──────────┘└╥┘\n",
+ "«c: 1/════════════════════════════════╩═\n",
+ "« 0 \n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"\n",
@@ -268,7 +302,21 @@
" (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
" (np.linspace(10.5, 45.0, num=70)).astype(float),\n",
" )\n",
+ "delays2 = np.append(\n",
+ " delays2,\n",
+ " (np.linspace(45.5, 200, num=140)).astype(float),\n",
+ " )\n",
+ "\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
+ "\n",
+ "# Delays for the 0 echo circuit\n",
+ "delays3 = np.append(\n",
+ " (np.linspace(1.0, 10.0, num=110)).astype(float),\n",
+ " (np.linspace(10.5, 25.0, num=137)).astype(float),\n",
+ " )\n",
+ "delays3 = [float(_) * conversion_factor for _ in delays3]\n",
+ "\n",
+ "\n",
"num_echoes = 4\n",
"estimated_t2hahn2 = 20 * conversion_factor\n",
"\n",
@@ -282,7 +330,7 @@
"\n",
"\n",
"# Create a T2Hahn experiment with 4 echoes. Print the first circuit as an example\n",
- "exp2_4echoes = T2Hahn(qubit2, delays2, num_echoes=4)\n",
+ "exp2_4echoes = T2Hahn(qubit2, delays3, num_echoes=4)\n",
"exp2_4echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
"print(\"The first circuirs of hahn echo experiment with 4 echoes:\")\n",
"print(exp2_4echoes.circuits()[0])\n"
@@ -290,53 +338,38 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\lib\\polynomial.py:659: RuntimeWarning: invalid value encountered in true_divide\n",
- " lhs /= scale\n",
- "Analysis callback <function BaseAnalysis.run.<locals>.run_analysis at 0x0000024CCD9BFEE0> failed:\n",
- "Traceback (most recent call last):\n",
- " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\database_service\\db_experiment_data.py\", line 299, in _wrapped_callback\n",
- " callback(self, **kwargs)\n",
- " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\framework\\base_analysis.py\", line 168, in run_analysis\n",
- " results, figures = analysis._run_analysis(expdata)\n",
- " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_analysis.py\", line 841, in _run_analysis\n",
- " fit_options = self._generate_fit_guesses(default_fit_opt)\n",
- " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\standard_analysis\\decay.py\", line 82, in _generate_fit_guesses\n",
- " alpha = curve.guess.exp_decay(curve_data.x, curve_data.y)\n",
- " File \"c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\guess.py\", line 188, in exp_decay\n",
- " coeffs = np.polyfit(x, np.log(y), deg=1)\n",
- " File \"<__array_function__ internals>\", line 5, in polyfit\n",
- " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\lib\\polynomial.py\", line 660, in polyfit\n",
- " c, resids, rank, s = lstsq(lhs, rhs, rcond)\n",
- " File \"<__array_function__ internals>\", line 5, in lstsq\n",
- " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\", line 2306, in lstsq\n",
- " x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)\n",
- " File \"C:\\Users\\014780756\\Anaconda3\\lib\\site-packages\\numpy\\linalg\\linalg.py\", line 100, in _raise_linalgerror_lstsq\n",
- " raise LinAlgError(\"SVD did not converge in Linear Least Squares\")\n",
- "numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares\n",
- "\n",
- "Possibly incomplete analysis results: an analysis callback raised an error.\n"
+ "Hahn Echoe with 0 echoes:\n"
]
},
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Hahn Echoe with 0 echoes:\n",
"Hahn Echoe with 4 echoes:\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -352,11 +385,12 @@
"# The behavior of the backend is determined by the following parameters\n",
"backend2 = T2HahnBackend(\n",
" t2hahn=[estimated_t2hahn2],\n",
- " frequency=[100100],\n",
+ " frequency=[200010],\n",
" initialization_error=[0.0],\n",
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
"\n",
+ "\n",
"# Analysis for Hahn Echoe experiemnt with 0 echoes.\n",
"expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
"expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
@@ -367,36 +401,16 @@
"\n",
"# Display the figure\n",
"print(\"Hahn Echoe with 0 echoes:\")\n",
- "# display(expdata2_0echoes.figure(0))\n",
+ "display(expdata2_0echoes.figure(0))\n",
"print(\"Hahn Echoe with 4 echoes:\")\n",
"display(expdata2_4echoes.figure(0))"
]
},
{
- "cell_type": "code",
- "execution_count": 37,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'counts': {'0': 1961, '1': 39},\n",
- " 'job_id': 0,\n",
- " 'metadata': {'experiment_type': 'T2Hahn',\n",
- " 'qubit': 0,\n",
- " 'xval': 0.0,\n",
- " 'unit': 's'},\n",
- " 'shots': 2000,\n",
- " 'meas_level': <MeasLevel.CLASSIFIED: 2>}"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
- "expdata2_0echoes.data()[0]"
+ "As We can see, the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 20[\\mu s]$ so we can see that the estimation with 4 echoes is better."
]
},
{
@@ -412,14 +426,19 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "ibmqfactory.load_account:WARNING:2021-12-27 17:11:38,733: Credentials are already in use. The existing account in the session will be replaced.\n"
+ "ibmqfactory.load_account:WARNING:2022-01-05 15:49:46,820: Credentials are already in use. The existing account in the session will be replaced.\n",
+ "ibmqfactory.load_account:WARNING:2022-01-05 23:14:31,530: Credentials are already in use. The existing account in the session will be replaced.\n",
+ "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\guess.py:188: RuntimeWarning: invalid value encountered in log\n",
+ " coeffs = np.polyfit(x, np.log(y), deg=1)\n",
+ "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_analysis.py:865: UserWarning: All initial guesses and parameter boundaries failed to fit the data. Please provide better initial guesses or fit parameter boundaries.\n",
+ " warnings.warn(\n"
]
}
],
@@ -428,6 +447,7 @@
"from qiskit_experiments.library.characterization.t2hahn import T2Hahn\n",
"import qiskit\n",
"from qiskit_experiments.library import T2Ramsey\n",
+ "import numpy as np\n",
"\n",
"def backend_fetcher():\n",
" # TOKEN = \"\"\n",
@@ -435,7 +455,7 @@
" IBMQ.load_account() # Load account from disk\n",
" provider = IBMQ.get_provider(hub='ibm-q')\n",
" backend = provider.get_backend('ibmq_manila')\n",
- " backend_properties = backend_manila.properties()\n",
+ " backend_properties = backend.properties()\n",
" return backend, backend_properties\n",
"\n",
"\n",
@@ -454,7 +474,7 @@
"\n",
"exp3_hahn = T2Hahn(qubit_hahn, delays2, num_echoes=num_echoes, backend=backend)\n",
"exp3_hahn.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5}, plot=True)\n",
- "expdata_hahn = exp3_hahn.run(backend=backend_manila, shots=2000).block_for_results()\n",
+ "expdata_hahn = exp3_hahn.run(backend=backend, shots=2000).block_for_results()\n",
"\n",
"# Ramsey experiment parameters\n",
"qubit_ramsey = 0\n",
@@ -462,9 +482,9 @@
"delays_ramsey = [float(_) * conversion_factor for _ in delays_ramsey]\n",
"\n",
"# Create a T2Ramsey experiment. Print the first circuit as an example\n",
- "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=freq_manila, backend=backend_manila)\n",
- "\n",
"backend, backend_properties = backend_fetcher()\n",
+ "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=backend_properties.frequency(0), backend=backend)\n",
+ "\n",
"# Analysis\n",
"default_p0 = {\n",
" \"A\": 0.5,\n",
@@ -483,19 +503,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can see that the backend has $T_2 = 20 [\\mu s]$. We can see the estimate $T_2$ from both experiments:"
+ "We can see that the backend has $T_2 \\approx 103.33 [\\mu s]$. We can see the estimate $T_2$ from both experiments:"
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -505,7 +525,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -530,7 +550,7 @@
{
"data": {
"text/html": [
- "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2021.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+ "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
From 907ae9dbe482ee872f247e8c6541705b4fcc56fd Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 9 Jan 2022 13:16:09 +0200
Subject: [PATCH 79/93] In the test excluded quality check for num_echoes=0
Excluded quality check for num_echoes=0 since it needs to be bad. The reason it is still there is to make sure the backend still compatible with 0 echoes.
---
test/test_t2hahn.py | 5 +++--
1 file changed, 3 insertions(+), 2 deletions(-)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index b377b34712..9d2bb66a96 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -64,8 +64,9 @@ def test_t2hahn_run_end2end(self, num_of_echoes: int):
expdata.block_for_results() # Wait for job/analysis to finish.
result = expdata.analysis_results("T2")
fitval = result.value
- self.assertEqual(result.quality, "good")
- self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
+ if num_of_echoes != 0:
+ self.assertEqual(result.quality, "good")
+ self.assertAlmostEqual(fitval.value, estimated_t2hahn, delta=3)
def test_t2hahn_parallel(self):
"""
From a0aa2aca95e172bc0427769a2bffea0832b4e776 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Wed, 12 Jan 2022 12:41:58 +0200
Subject: [PATCH 80/93] Changed tutorial text and a bit of code
---
docs/tutorials/t2hahn_characterization.ipynb | 46 +++++++++-----------
1 file changed, 21 insertions(+), 25 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 63a91667e4..e8e748a97f 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -181,7 +181,7 @@
"metadata": {},
"source": [
"### Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. Since if there was no delay we would expect that the probability to measure `1` is $100\\%$, so we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
+ "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, so we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
@@ -248,7 +248,7 @@
"metadata": {},
"source": [
"### Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improve our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, We will add frequency to the qubit and see how the result change due to that (We can see Rabi Oscillations in the `0` echoes case)\n",
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, We will add frequency to the qubit and see how the result changes due to that (We can see Rabi Oscillations in the `0` echoes case).\n",
"Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
@@ -298,41 +298,36 @@
"# set the desired delays\n",
"conversion_factor = 1e-6\n",
"\n",
+ "# The delays aren't equally sparse due the behavior of exponential decay curve where the change in the result\n",
+ "# in earlier times is bigger then later times. In addition, since the delay amount is 'delay * 2 * num_of_echoes',\n",
+ "# the construction of the delays for each experiment will be different so they will have matched total length.\n",
"delays2 = np.append(\n",
- " (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
- " (np.linspace(10.5, 45.0, num=70)).astype(float),\n",
- " )\n",
- "delays2 = np.append(\n",
- " delays2,\n",
- " (np.linspace(45.5, 200, num=140)).astype(float),\n",
+ " (np.linspace(1.0, 50.0, num=50)).astype(float),\n",
+ " (np.linspace(25.5, 100.0, num=70)).astype(float),\n",
" )\n",
"\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"\n",
"# Delays for the 0 echo circuit\n",
"delays3 = np.append(\n",
- " (np.linspace(1.0, 10.0, num=110)).astype(float),\n",
- " (np.linspace(10.5, 25.0, num=137)).astype(float),\n",
+ " (np.linspace(0.125, 6.25, num=50)).astype(float),\n",
+ " (np.linspace(3.125, 12.5, num=70)).astype(float),\n",
" )\n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
- "\n",
"num_echoes = 4\n",
"estimated_t2hahn2 = 20 * conversion_factor\n",
"\n",
- "\n",
"# Create a T2Hahn experiment with 0 echoes\n",
"exp2_0echoes = T2Hahn(qubit2, delays2, num_echoes=0)\n",
"exp2_0echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
- "print(\"The first circuirs of hahn echo experiment with 0 echoes:\")\n",
+ "print(\"The first circuit of hahn echo experiment with 0 echoes:\")\n",
"print(exp2_0echoes.circuits()[0])\n",
"\n",
- "\n",
- "\n",
"# Create a T2Hahn experiment with 4 echoes. Print the first circuit as an example\n",
"exp2_4echoes = T2Hahn(qubit2, delays3, num_echoes=4)\n",
"exp2_4echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
- "print(\"The first circuirs of hahn echo experiment with 4 echoes:\")\n",
+ "print(\"The first circuit of hahn echo experiment with 4 echoes:\")\n",
"print(exp2_4echoes.circuits()[0])\n"
]
},
@@ -390,7 +385,6 @@
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
"\n",
- "\n",
"# Analysis for Hahn Echoe experiemnt with 0 echoes.\n",
"expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
"expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
@@ -410,7 +404,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "As We can see, the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 20[\\mu s]$ so we can see that the estimation with 4 echoes is better."
+ "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 20[\\mu s]$, which is close to estimate of the 4 echoes experiment"
]
},
{
@@ -419,8 +413,8 @@
"source": [
"### $T_{2}$ versus $T_{2}^{\\ast}$\n",
"This experiment purpose is to give a better estimate for the dephasing noise. In Ramsey experiment, we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
- "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. This affect the qubit frequncy.\n",
- "In Ramsey experiment, we estimate the frequency of the qubit while in Hahn Echo experiment there is no need.\\\n",
+ "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. This affects the qubit frequency.\n",
+ "In Ramsey experiment, we estimate the frequency of the qubit while in Hahn Echo experiment this is not required.\n",
"Firslty, let us get backend property from the the quantum computer."
]
},
@@ -458,7 +452,6 @@
" backend_properties = backend.properties()\n",
" return backend, backend_properties\n",
"\n",
- "\n",
"backend, backend_properties = backend_fetcher()\n",
"estimated_t2hahn = backend_properties.t2(0)\n",
"\n",
@@ -481,15 +474,18 @@
"delays_ramsey = list(range(1, 350, 2))\n",
"delays_ramsey = [float(_) * conversion_factor for _ in delays_ramsey]\n",
"\n",
- "# Create a T2Ramsey experiment. Print the first circuit as an example\n",
+ "# Create a T2Ramsey experiment\n",
"backend, backend_properties = backend_fetcher()\n",
- "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=backend_properties.frequency(0), backend=backend)\n",
+ "# Guess that the frequency error is about 2%\n",
+ "freq_diff = backend_properties.frequency(0) * 0.02\n",
+ "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=freq_diff, backend=backend)\n",
+ "\n",
"\n",
"# Analysis\n",
"default_p0 = {\n",
" \"A\": 0.5,\n",
" \"T2star\": estimated_t2hahn,\n",
- " \"f\": backend_properties.frequency(0),\n",
+ " \"f\": freq_diff,\n",
" \"phi\": 0,\n",
" \"B\": 0.5,\n",
" }\n",
@@ -503,7 +499,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can see that the backend has $T_2 \\approx 103.33 [\\mu s]$. We can see the estimate $T_2$ from both experiments:"
+ "We can see that the backend has $T_2 \\approx 103.33 [\\mu s]$. Here is the estimate of $T_2$ from both experiments:"
]
},
{
From 6e334b8e757e3f9aebdb8ebc84af6ca4f791d77f Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Thu, 13 Jan 2022 14:22:16 +0200
Subject: [PATCH 81/93] Added bounds and removed p0 from analysis, finished
tutorial
---
docs/tutorials/t2hahn_characterization.ipynb | 229 ++++--------------
.../analysis/t2hahn_analysis.py | 12 +-
2 files changed, 54 insertions(+), 187 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index e8e748a97f..2286d25b7c 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -115,17 +115,9 @@
"scrolled": true
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_fit.py:137: RuntimeWarning: invalid value encountered in sqrt\n",
- " popt_err = np.sqrt(np.diag(pcov))\n"
- ]
- },
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -154,17 +146,17 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [-2.31726995e+03 2.31808006e+03 -5.66599325e-01] ± [nan nan nan]\n",
- "- χ²: 45.44971077076316\n",
- "- quality: bad\n",
+ "- value: [4.73150194e-01 5.03648438e-01 1.98283181e-05] ± [5.15456349e-03 3.04084131e-03 5.77525843e-07]\n",
+ "- χ²: 0.7488240853624647\n",
+ "- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: -0.5665993249904158 ± nan s\n",
- "- χ²: 45.44971077076316\n",
- "- quality: bad\n",
+ "- value: 1.982831812408823e-05 ± 5.775258431912853e-07 s\n",
+ "- χ²: 0.7488240853624647\n",
+ "- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
]
@@ -191,7 +183,7 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -221,16 +213,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.78978431e-01 5.02409209e-01 2.01192655e-05] ± [5.09032092e-03 3.07792331e-03 5.78387141e-07]\n",
- "- χ²: 0.5509343846546172\n",
+ "- value: [4.78978892e-01 5.02410014e-01 2.01190669e-05] ± [5.08967760e-03 3.07896251e-03 5.78613251e-07]\n",
+ "- χ²: 0.5509343873343946\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 2.011926549231594e-05 ± 5.783871411742618e-07 s\n",
- "- χ²: 0.5509343846546172\n",
+ "- value: 2.0119066897403302e-05 ± 5.786132511852634e-07 s\n",
+ "- χ²: 0.5509343873343946\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -254,40 +246,40 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The first circuirs of hahn echo experiment with 0 echoes:\n",
+ "The first circuit of hahn echo experiment with 0 echoes:\n",
" ┌─────────┐┌─────────────────┐┌──────────┐┌─┐\n",
" q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
" └─────────┘└─────────────────┘└──────────┘└╥┘\n",
"c: 1/═══════════════════════════════════════════╩═\n",
" 0 \n",
- "The first circuirs of hahn echo experiment with 4 echoes:\n",
- " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐»\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├»\n",
- " └─────────┘└─────────────────┘└───────┘└─────────────────┘»\n",
- "c: 1/══════════════════════════════════════════════════════════»\n",
- " »\n",
- "« ┌─────────────────┐┌───────┐┌─────────────────┐┌─────────────────┐»\n",
- "« q: ┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├»\n",
- "« └─────────────────┘└───────┘└─────────────────┘└─────────────────┘»\n",
- "«c: 1/══════════════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌───────┐┌─────────────────┐┌─────────────────┐┌───────┐»\n",
- "« q: ┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├»\n",
- "« └───────┘└─────────────────┘└─────────────────┘└───────┘»\n",
- "«c: 1/════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌─────────────────┐┌──────────┐┌─┐\n",
- "« q: ┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └─────────────────┘└──────────┘└╥┘\n",
- "«c: 1/════════════════════════════════╩═\n",
- "« 0 \n"
+ "The first circuit of hahn echo experiment with 4 echoes:\n",
+ " ┌─────────┐┌────────────────────┐┌───────┐┌────────────────────┐»\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
+ " └─────────┘└────────────────────┘└───────┘└────────────────────┘»\n",
+ "c: 1/════════════════════════════════════════════════════════════════»\n",
+ " »\n",
+ "« ┌────────────────────┐┌───────┐┌────────────────────┐»\n",
+ "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
+ "« └────────────────────┘└───────┘└────────────────────┘»\n",
+ "«c: 1/═════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌────────────────────┐┌───────┐┌────────────────────┐»\n",
+ "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
+ "« └────────────────────┘└───────┘└────────────────────┘»\n",
+ "«c: 1/═════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌────────────────────┐┌───────┐┌────────────────────┐┌──────────┐┌─┐\n",
+ "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └────────────────────┘└───────┘└────────────────────┘└──────────┘└╥┘\n",
+ "«c: 1/══════════════════════════════════════════════════════════════════╩═\n",
+ "« 0 \n"
]
}
],
@@ -301,17 +293,19 @@
"# The delays aren't equally sparse due the behavior of exponential decay curve where the change in the result\n",
"# in earlier times is bigger then later times. In addition, since the delay amount is 'delay * 2 * num_of_echoes',\n",
"# the construction of the delays for each experiment will be different so they will have matched total length.\n",
+ "\n",
+ "# Delays for Hahn Echo Experiment with 0 echoes\n",
"delays2 = np.append(\n",
" (np.linspace(1.0, 50.0, num=50)).astype(float),\n",
- " (np.linspace(25.5, 100.0, num=70)).astype(float),\n",
+ " (np.linspace(51, 100.0, num=50)).astype(float),\n",
" )\n",
"\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"\n",
- "# Delays for the 0 echo circuit\n",
+ "# Delays for Hahn Echo Experiment with 4 echoes\n",
"delays3 = np.append(\n",
" (np.linspace(0.125, 6.25, num=50)).astype(float),\n",
- " (np.linspace(3.125, 12.5, num=70)).astype(float),\n",
+ " (np.linspace(6.375, 12.5, num=50)).astype(float),\n",
" )\n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
@@ -333,7 +327,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 9,
"metadata": {
"scrolled": false
},
@@ -347,7 +341,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -364,7 +358,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -376,11 +370,11 @@
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
"\n",
- "estimated_t2hahn2 = 20 * conversion_factor\n",
+ "estimated_t2hahn2 = 30 * conversion_factor\n",
"# The behavior of the backend is determined by the following parameters\n",
"backend2 = T2HahnBackend(\n",
" t2hahn=[estimated_t2hahn2],\n",
- " frequency=[200010],\n",
+ " frequency=[50010],\n",
" initialization_error=[0.0],\n",
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
@@ -404,141 +398,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 20[\\mu s]$, which is close to estimate of the 4 echoes experiment"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### $T_{2}$ versus $T_{2}^{\\ast}$\n",
- "This experiment purpose is to give a better estimate for the dephasing noise. In Ramsey experiment, we can estimate $T_{2}^{\\ast}$ but this is not truly the dephasing noise as the information is not lost.\n",
- "The $\\ast$ indicates that $T_{2}^{\\ast}$ is sensitive to inhomogeneous broadening. This affects the qubit frequency.\n",
- "In Ramsey experiment, we estimate the frequency of the qubit while in Hahn Echo experiment this is not required.\n",
- "Firslty, let us get backend property from the the quantum computer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "ibmqfactory.load_account:WARNING:2022-01-05 15:49:46,820: Credentials are already in use. The existing account in the session will be replaced.\n",
- "ibmqfactory.load_account:WARNING:2022-01-05 23:14:31,530: Credentials are already in use. The existing account in the session will be replaced.\n",
- "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\guess.py:188: RuntimeWarning: invalid value encountered in log\n",
- " coeffs = np.polyfit(x, np.log(y), deg=1)\n",
- "c:\\users\\014780756\\documents\\github_2\\qiskit_experiments\\curve_analysis\\curve_analysis.py:865: UserWarning: All initial guesses and parameter boundaries failed to fit the data. Please provide better initial guesses or fit parameter boundaries.\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "from qiskit import IBMQ\n",
- "from qiskit_experiments.library.characterization.t2hahn import T2Hahn\n",
- "import qiskit\n",
- "from qiskit_experiments.library import T2Ramsey\n",
- "import numpy as np\n",
- "\n",
- "def backend_fetcher():\n",
- " # TOKEN = \"\"\n",
- " # IBMQ.save_account(TOKEN)\n",
- " IBMQ.load_account() # Load account from disk\n",
- " provider = IBMQ.get_provider(hub='ibm-q')\n",
- " backend = provider.get_backend('ibmq_manila')\n",
- " backend_properties = backend.properties()\n",
- " return backend, backend_properties\n",
- "\n",
- "backend, backend_properties = backend_fetcher()\n",
- "estimated_t2hahn = backend_properties.t2(0)\n",
- "\n",
- "# Hahn Echo experiment parameters\n",
- "qubit_hahn = 0\n",
- "conversion_factor = 1e-6\n",
- "delays2 = np.append(\n",
- " (np.linspace(1.0, 10.0, num=37)).astype(float),\n",
- " (np.linspace(10.5, 45.0, num=70)).astype(float),\n",
- " )\n",
- "delays2 = [float(_) * conversion_factor for _ in delays2]\n",
- "num_echoes = 4\n",
- "\n",
- "exp3_hahn = T2Hahn(qubit_hahn, delays2, num_echoes=num_echoes, backend=backend)\n",
- "exp3_hahn.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5}, plot=True)\n",
- "expdata_hahn = exp3_hahn.run(backend=backend, shots=2000).block_for_results()\n",
- "\n",
- "# Ramsey experiment parameters\n",
- "qubit_ramsey = 0\n",
- "delays_ramsey = list(range(1, 350, 2))\n",
- "delays_ramsey = [float(_) * conversion_factor for _ in delays_ramsey]\n",
- "\n",
- "# Create a T2Ramsey experiment\n",
- "backend, backend_properties = backend_fetcher()\n",
- "# Guess that the frequency error is about 2%\n",
- "freq_diff = backend_properties.frequency(0) * 0.02\n",
- "exp_ramsey = T2Ramsey(qubit_ramsey, delays_ramsey, osc_freq=freq_diff, backend=backend)\n",
- "\n",
- "\n",
- "# Analysis\n",
- "default_p0 = {\n",
- " \"A\": 0.5,\n",
- " \"T2star\": estimated_t2hahn,\n",
- " \"f\": freq_diff,\n",
- " \"phi\": 0,\n",
- " \"B\": 0.5,\n",
- " }\n",
- "exp_ramsey.analysis.set_options(p0=default_p0, plot=True)\n",
- "\n",
- "# Run the Ramsey experiment\n",
- "expdata_ramsey = exp_ramsey.run(backend=backend, shots=2000).block_for_results()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see that the backend has $T_2 \\approx 103.33 [\\mu s]$. Here is the estimate of $T_2$ from both experiments:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "\n",
- "display(expdata_hahn.figure(0), expdata_ramsey.figure(0))"
+ "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 30[\\mu s]$, which is close to estimate of the 4 echoes experiment"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 10,
"metadata": {
"scrolled": false
},
diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index 03df20c534..057c9b84f3 100644
--- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -14,6 +14,8 @@
"""
from typing import Union
+import numpy as np
+
import qiskit_experiments.curve_analysis as curve
from qiskit_experiments.data_processing import DataProcessor, Probability
@@ -35,11 +37,11 @@ def _default_options(cls) -> Options:
options.data_processor = DataProcessor(
input_key="counts", data_actions=[Probability(outcome="0")]
)
- options.p0 = {
- "amp": 0.5,
- "tau": 0.000001,
- "base": 0.5,
- } # The analysis will not work without initial guess
+ options.bounds = {
+ "amp": (0.0, 1.0),
+ "tau": (0.0, np.inf),
+ "base": (0.0, 1.0),
+ }
options.xlabel = "Delay"
options.ylabel = "P(0)"
options.xval_unit = "s"
From 9b34c01703e4301c15be38ef50dedadb81578b69 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 16 Jan 2022 11:15:05 +0200
Subject: [PATCH 82/93] Edited tutorial as reviewed
---
docs/tutorials/t2hahn_characterization.ipynb | 23 ++++++++++----------
1 file changed, 11 insertions(+), 12 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 2286d25b7c..7464aa6880 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -40,7 +40,7 @@
" 2. delay\n",
" 3. measurement\n",
"\n",
- "The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle after the delay gates will be $\\theta_{new} = \\theta_{old} + \\pi$. after wating the same delay time, the angle will be approximly $0$ or $\\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
+ "The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle after the delay gates will be $\\theta_{new} = \\theta_{old} + \\pi$. After waiting the same delay time, the angle will be approximately $0$ or $\\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
]
},
{
@@ -62,7 +62,6 @@
],
"source": [
"qubit = 0\n",
- "# set the desired delays\n",
"conversion_factor = 1e-6 # our delay will be in micro-sec\n",
"delays = list(range(1, 50, 1) )\n",
"delays = [float(_) * conversion_factor for _ in delays]\n",
@@ -173,7 +172,7 @@
"metadata": {},
"source": [
"### Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, so we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
+ "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, therefore we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
@@ -240,7 +239,7 @@
"metadata": {},
"source": [
"### Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, We will add frequency to the qubit and see how the result changes due to that (We can see Rabi Oscillations in the `0` echoes case).\n",
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, we will add frequency to the qubit and see how the result changes due to that (We can see Rabi Oscillations in the `0` echoes case).\n",
"Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
@@ -290,9 +289,9 @@
"# set the desired delays\n",
"conversion_factor = 1e-6\n",
"\n",
- "# The delays aren't equally sparse due the behavior of exponential decay curve where the change in the result\n",
- "# in earlier times is bigger then later times. In addition, since the delay amount is 'delay * 2 * num_of_echoes',\n",
- "# the construction of the delays for each experiment will be different so they will have matched total length.\n",
+ "# The delays aren't equally spaced due the behavior of exponential decay curve where the change in the result\n",
+ "# in earlier times is larger than later times. In addition, since the total delay is 'delay * 2 * num_of_echoes',\n",
+ "# the construction of the delays for each experiment will be different, such that their total length will be the same.\n",
"\n",
"# Delays for Hahn Echo Experiment with 0 echoes\n",
"delays2 = np.append(\n",
@@ -379,18 +378,18 @@
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
"\n",
- "# Analysis for Hahn Echoe experiemnt with 0 echoes.\n",
+ "# Analysis for Hahn Echo experiemnt with 0 echoes.\n",
"expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
"expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
- "# Analysis for Hahn Echoe experiemnt with 4 echoes.\n",
+ "# Analysis for Hahn Echo experiemnt with 4 echoes.\n",
"expdata2_4echoes = exp2_4echoes.run(backend=backend2, shots=2000)\n",
"expdata2_4echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
"# Display the figure\n",
- "print(\"Hahn Echoe with 0 echoes:\")\n",
+ "print(\"Hahn Echo with 0 echoes:\")\n",
"display(expdata2_0echoes.figure(0))\n",
- "print(\"Hahn Echoe with 4 echoes:\")\n",
+ "print(\"Hahn Echo with 4 echoes:\")\n",
"display(expdata2_4echoes.figure(0))"
]
},
@@ -398,7 +397,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 30[\\mu s]$, which is close to estimate of the 4 echoes experiment"
+ "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 30[\\mu s]$, which is close to the estimate of the 4 echoes experiment"
]
},
{
From 16c04da6849ccf4d2370797a7cf23ad4780d1400 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 24 Jan 2022 19:28:27 +0200
Subject: [PATCH 83/93] Update t2hahn_characterization.ipynb
---
docs/tutorials/t2hahn_characterization.ipynb | 244 ++++---------------
1 file changed, 51 insertions(+), 193 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 7464aa6880..e13fe06c90 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# T<sub>2</sub> Hahn Characterization"
+ "# T<sub>2</sub> Hahn Characterization And CPMG"
]
},
{
@@ -13,14 +13,26 @@
"source": [
"The purpose of the $T_2$ Hahn Echo experiment is to determine $T_2$ qubit property. \n",
"\n",
- "In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for the transverse magnetization to fall to approximately 37% ($\\frac{1}{e}$) of its initial value.\n",
+ "In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for a single qubit Bloch vector projection on the XY plane, to fall to approximately 37% ($\\frac{1}{e}$) of its initial amplitude.<br>\n",
+ "In Ramsey Experiemnt we were introduced to the term <I>detuning frequency</I>. Hahn Echo expriemnt and CPMG sequence are experiments to estimate $T_2$ which are robust to the <I>detuning frequency</I>.\n",
+ "The decay in amplitude causes the probability function to take the following form:<br>\n",
+ "$$f(t) = a \\cdot e^{-\\frac{t}{T_2}}+ b$$\n",
+ "The diffrence between Hahn Echo and CPMG sequence is that in Hahn Echo experiment, there is only one echo sequences while in CPMG there are multiple echo sequences."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 1. Deohernce Time\n",
+ "Decohernce time is the time that takes for the system to lose it's quantum properties and return to the classical physics regime.\n",
"\n",
- "Since the qubit is exposed to other types of noise (like $T_1$), we are using a $Rx(\\pi)$ pulse for decoupling and to solve our inaccuracy for the qubit frequency estimation."
+ "Since the qubit is exposed to other types of noise (like $T_1$), we are using a $Rx(\\pi)$ pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation."
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
@@ -34,32 +46,24 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The circuit used for the experiment comprises the following:\n",
+ "The circuit used for an experiment with $N$ echoes comprises the following components:\n",
"\n",
- " 1. Rx gate\n",
- " 2. delay\n",
- " 3. measurement\n",
+ "  1.$Rx(\\frac{\\pi}{2})$ gate <br>\n",
+ "  2. $N$ times Echo sequence : <br>\n",
+ "    (a) $Delay(t_{0})$ gate <br>\n",
+ "    (b) $Rx(\\pi)$ <br>\n",
+ "    (c) $Delay(t_{0})$ gate <br>\n",
+ "  3. $Rx(\\pm \\frac{\\pi}{2})$ gate (sign depends on the number of echoes) <br>\n",
+ "  4. Measurement gate\n",
"\n",
"The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle after the delay gates will be $\\theta_{new} = \\theta_{old} + \\pi$. After waiting the same delay time, the angle will be approximately $0$ or $\\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐┌─────────┐┌─┐\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
- " └─────────┘└─────────────────┘└───────┘└─────────────────┘└─────────┘└╥┘\n",
- "c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
- " 0 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"qubit = 0\n",
"conversion_factor = 1e-6 # our delay will be in micro-sec\n",
@@ -81,7 +85,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -109,22 +113,11 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"exp1.analysis.set_options(p0=None, plot=True)\n",
"expdata1 = exp1.run(backend=backend, shots=2000)\n",
@@ -136,31 +129,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DbAnalysisResultV1\n",
- "- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.73150194e-01 5.03648438e-01 1.98283181e-05] ± [5.15456349e-03 3.04084131e-03 5.77525843e-07]\n",
- "- χ²: 0.7488240853624647\n",
- "- quality: good\n",
- "- extra: <4 items>\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n",
- "DbAnalysisResultV1\n",
- "- name: T2\n",
- "- value: 1.982831812408823e-05 ± 5.775258431912853e-07 s\n",
- "- χ²: 0.7488240853624647\n",
- "- quality: good\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Print results\n",
"for result in expdata1.analysis_results():\n",
@@ -171,26 +142,15 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Providing initial user estimates\n",
+ "### 2. Providing initial user estimates\n",
"The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, therefore we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"exp_with_p0 = T2Hahn(qubit=qubit, delays=delays, num_echoes=number_of_echoes)\n",
"exp_with_p0.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5})\n",
@@ -203,31 +163,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DbAnalysisResultV1\n",
- "- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.78978892e-01 5.02410014e-01 2.01190669e-05] ± [5.08967760e-03 3.07896251e-03 5.78613251e-07]\n",
- "- χ²: 0.5509343873343946\n",
- "- quality: good\n",
- "- extra: <4 items>\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n",
- "DbAnalysisResultV1\n",
- "- name: T2\n",
- "- value: 2.0119066897403302e-05 ± 5.786132511852634e-07 s\n",
- "- χ²: 0.5509343873343946\n",
- "- quality: good\n",
- "- device_components: ['Q0']\n",
- "- verified: False\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Print results\n",
"for result in expdata_with_p0.analysis_results():\n",
@@ -238,50 +176,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Number of echoes\n",
+ "### 3. Number of echoes\n",
"The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, we will add frequency to the qubit and see how the result changes due to that (We can see Rabi Oscillations in the `0` echoes case).\n",
"Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The first circuit of hahn echo experiment with 0 echoes:\n",
- " ┌─────────┐┌─────────────────┐┌──────────┐┌─┐\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(-π/2) ├┤M├\n",
- " └─────────┘└─────────────────┘└──────────┘└╥┘\n",
- "c: 1/═══════════════════════════════════════════╩═\n",
- " 0 \n",
- "The first circuit of hahn echo experiment with 4 echoes:\n",
- " ┌─────────┐┌────────────────────┐┌───────┐┌────────────────────┐»\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
- " └─────────┘└────────────────────┘└───────┘└────────────────────┘»\n",
- "c: 1/════════════════════════════════════════════════════════════════»\n",
- " »\n",
- "« ┌────────────────────┐┌───────┐┌────────────────────┐»\n",
- "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
- "« └────────────────────┘└───────┘└────────────────────┘»\n",
- "«c: 1/═════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌────────────────────┐┌───────┐┌────────────────────┐»\n",
- "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├»\n",
- "« └────────────────────┘└───────┘└────────────────────┘»\n",
- "«c: 1/═════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌────────────────────┐┌───────┐┌────────────────────┐┌──────────┐┌─┐\n",
- "« q: ┤ Delay(1.25e-07[s]) ├┤ Rx(π) ├┤ Delay(1.25e-07[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └────────────────────┘└───────┘└────────────────────┘└──────────┘└╥┘\n",
- "«c: 1/══════════════════════════════════════════════════════════════════╩═\n",
- "« 0 \n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"\n",
@@ -295,16 +199,16 @@
"\n",
"# Delays for Hahn Echo Experiment with 0 echoes\n",
"delays2 = np.append(\n",
- " (np.linspace(1.0, 50.0, num=50)).astype(float),\n",
- " (np.linspace(51, 100.0, num=50)).astype(float),\n",
+ " (np.linspace(0.0, 51.0, num=26)).astype(float),\n",
+ " (np.linspace(53, 100.0, num=25)).astype(float),\n",
" )\n",
"\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"\n",
"# Delays for Hahn Echo Experiment with 4 echoes\n",
"delays3 = np.append(\n",
- " (np.linspace(0.125, 6.25, num=50)).astype(float),\n",
- " (np.linspace(6.375, 12.5, num=50)).astype(float),\n",
+ " (np.linspace(0.0, 6.375, num=26)).astype(float),\n",
+ " (np.linspace(6.625, 12.5, num=25)).astype(float),\n",
" )\n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
@@ -326,54 +230,21 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {
"scrolled": false
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hahn Echoe with 0 echoes:\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hahn Echoe with 4 echoes:\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 576x360 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
"\n",
"estimated_t2hahn2 = 30 * conversion_factor\n",
+ "detuning_frequency = 2 * np.pi * 10000\n",
+ "\n",
"# The behavior of the backend is determined by the following parameters\n",
"backend2 = T2HahnBackend(\n",
" t2hahn=[estimated_t2hahn2],\n",
- " frequency=[50010],\n",
+ " frequency=[detuning_frequency],\n",
" initialization_error=[0.0],\n",
" readout0to1=[0.02],\n",
" readout1to0=[0.02],)\n",
@@ -382,7 +253,7 @@
"expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
"expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
- "# Analysis for Hahn Echo experiemnt with 4 echoes.\n",
+ "# Analysis for Hahn Echo experiemnt with 4 echoes\n",
"expdata2_4echoes = exp2_4echoes.run(backend=backend2, shots=2000)\n",
"expdata2_4echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
@@ -402,24 +273,11 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {
"scrolled": false
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import qiskit.tools.jupyter\n",
"%qiskit_copyright"
From 8eb4f5106f35482fe22b8bf4ae69bd873013168d Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 24 Jan 2022 19:51:19 +0200
Subject: [PATCH 84/93] fixed test to be with "self.json_equiv" and fixed lint
---
test/test_t2hahn.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/test/test_t2hahn.py b/test/test_t2hahn.py
index 9d2bb66a96..3fb3becebd 100644
--- a/test/test_t2hahn.py
+++ b/test/test_t2hahn.py
@@ -160,12 +160,12 @@ def test_experiment_config(self):
exp = T2Hahn(0, [1, 2, 3, 4, 5])
loaded_exp = T2Hahn.from_config(exp.config())
self.assertNotEqual(exp, loaded_exp)
- self.assertTrue(self.experiments_equiv(exp, loaded_exp))
+ self.assertTrue(self.json_equiv(exp, loaded_exp))
def test_roundtrip_serializable(self):
"""Test round trip JSON serialization"""
exp = T2Hahn(0, [1, 2, 3, 4, 5])
- self.assertRoundTripSerializable(exp, self.experiments_equiv)
+ self.assertRoundTripSerializable(exp, self.json_equiv)
def test_analysis_config(self):
""" "Test converting analysis to and from config works"""
From 377af038800e16086e7feabc7909eccecc28b4d1 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 24 Jan 2022 20:50:56 +0200
Subject: [PATCH 85/93] added cosmetic fixes to the tutorial
---
docs/tutorials/t2hahn_characterization.ipynb | 201 ++++++++++++++++---
1 file changed, 178 insertions(+), 23 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index e13fe06c90..a07910f265 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -32,7 +32,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"scrolled": true
},
@@ -48,12 +48,12 @@
"source": [
"The circuit used for an experiment with $N$ echoes comprises the following components:\n",
"\n",
- "  1.$Rx(\\frac{\\pi}{2})$ gate <br>\n",
+ "  1.$Rx\\left(\\frac{\\pi}{2} \\right)$ gate <br>\n",
"  2. $N$ times Echo sequence : <br>\n",
- "    (a) $Delay(t_{0})$ gate <br>\n",
- "    (b) $Rx(\\pi)$ <br>\n",
- "    (c) $Delay(t_{0})$ gate <br>\n",
- "  3. $Rx(\\pm \\frac{\\pi}{2})$ gate (sign depends on the number of echoes) <br>\n",
+ "    (a) $Delay \\left(t_{0} \\right)$ gate <br>\n",
+ "    (b) $Rx \\left(\\pi \\right)$ gate <br>\n",
+ "    (c) $Delay \\left(t_{0} \\right)$ gate <br>\n",
+ "  3. $Rx \\left(\\pm \\frac{\\pi}{2} \\right)$ gate (sign depends on the number of echoes) <br>\n",
"  4. Measurement gate\n",
"\n",
"The user provides as input a series of delays in seconds. During the delay, we expect the qubit to precess about the z-axis. Because of the echo gate ($Rx(\\pi)$) for each echo, the angle after the delay gates will be $\\theta_{new} = \\theta_{old} + \\pi$. After waiting the same delay time, the angle will be approximately $0$ or $\\pi$. By varying the extension of the delays, we get a series of decaying measurements. We can draw the graph of the resulting function and can analytically extract the desired values."
@@ -61,9 +61,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐┌─────────┐┌─┐\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
+ " └─────────┘└─────────────────┘└───────┘└─────────────────┘└─────────┘└╥┘\n",
+ "c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
+ " 0 \n"
+ ]
+ }
+ ],
"source": [
"qubit = 0\n",
"conversion_factor = 1e-6 # our delay will be in micro-sec\n",
@@ -85,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -113,11 +125,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"exp1.analysis.set_options(p0=None, plot=True)\n",
"expdata1 = exp1.run(backend=backend, shots=2000)\n",
@@ -129,9 +152,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.73150194e-01 5.03648438e-01 1.98283181e-05] ± [5.15456349e-03 3.04084131e-03 5.77525843e-07]\n",
+ "- χ²: 0.7488240853624647\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 1.982831812408823e-05 ± 5.775258431912853e-07 s\n",
+ "- χ²: 0.7488240853624647\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
"source": [
"# Print results\n",
"for result in expdata1.analysis_results():\n",
@@ -148,9 +193,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"exp_with_p0 = T2Hahn(qubit=qubit, delays=delays, num_echoes=number_of_echoes)\n",
"exp_with_p0.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn, \"base\": 0.5})\n",
@@ -163,9 +219,31 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DbAnalysisResultV1\n",
+ "- name: @Parameters_T2HahnAnalysis\n",
+ "- value: [4.78978892e-01 5.02410014e-01 2.01190669e-05] ± [5.08967760e-03 3.07896251e-03 5.78613251e-07]\n",
+ "- χ²: 0.5509343873343946\n",
+ "- quality: good\n",
+ "- extra: <4 items>\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n",
+ "DbAnalysisResultV1\n",
+ "- name: T2\n",
+ "- value: 2.0119066897403302e-05 ± 5.786132511852634e-07 s\n",
+ "- χ²: 0.5509343873343946\n",
+ "- quality: good\n",
+ "- device_components: ['Q0']\n",
+ "- verified: False\n"
+ ]
+ }
+ ],
"source": [
"# Print results\n",
"for result in expdata_with_p0.analysis_results():\n",
@@ -183,9 +261,38 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The first circuit of hahn echo experiment with 0 echoes:\n",
+ " ┌─────────┐┌───────────────┐┌──────────┐┌─┐\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ " └─────────┘└───────────────┘└──────────┘└╥┘\n",
+ "c: 1/═════════════════════════════════════════╩═\n",
+ " 0 \n",
+ "The first circuit of hahn echo experiment with 4 echoes:\n",
+ " ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌───────────────┐»\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Delay(0.0[s]) ├»\n",
+ " └─────────┘└───────────────┘└───────┘└───────────────┘└───────────────┘»\n",
+ "c: 1/═══════════════════════════════════════════════════════════════════════»\n",
+ " »\n",
+ "« ┌───────┐┌───────────────┐┌───────────────┐┌───────┐┌───────────────┐»\n",
+ "« q: ┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├»\n",
+ "« └───────┘└───────────────┘└───────────────┘└───────┘└───────────────┘»\n",
+ "«c: 1/═════════════════════════════════════════════════════════════════════»\n",
+ "« »\n",
+ "« ┌───────────────┐┌───────┐┌───────────────┐┌──────────┐┌─┐\n",
+ "« q: ┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └───────────────┘└───────┘└───────────────┘└──────────┘└╥┘\n",
+ "«c: 1/════════════════════════════════════════════════════════╩═\n",
+ "« 0 \n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"\n",
@@ -230,11 +337,46 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hahn Echo with 0 echoes:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Hahn Echo with 4 echoes:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
"\n",
@@ -273,11 +415,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>© Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import qiskit.tools.jupyter\n",
"%qiskit_copyright"
From a48b5365a78895853787702702f04d126482bdae Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 30 Jan 2022 16:02:14 +0200
Subject: [PATCH 86/93] Added text and changed functions
I have added linking to Ramsey experiment.
Added the term detuning frequency.
Changed number of echoes in the comparation to '1 v.s. 0' instead then '4 v.s. 0'. I removed it because the there is no pint in doing 4 echoes in mock backend without T1 noise.
---
docs/tutorials/t2hahn_characterization.ipynb | 113 ++++++++++---------
1 file changed, 58 insertions(+), 55 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index a07910f265..11df19de17 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# T<sub>2</sub> Hahn Characterization And CPMG"
+ "# T<sub>2</sub> Hahn Characterization (CPMG)"
]
},
{
@@ -14,20 +14,21 @@
"The purpose of the $T_2$ Hahn Echo experiment is to determine $T_2$ qubit property. \n",
"\n",
"In this experiment, we would like to get a more precise estimate of the qubit's decay time. $T_2$ represents the amount of time required for a single qubit Bloch vector projection on the XY plane, to fall to approximately 37% ($\\frac{1}{e}$) of its initial amplitude.<br>\n",
- "In Ramsey Experiemnt we were introduced to the term <I>detuning frequency</I>. Hahn Echo expriemnt and CPMG sequence are experiments to estimate $T_2$ which are robust to the <I>detuning frequency</I>.\n",
+ "In <a href=\"./t2ramsey_characterization.ipynb\">Ramsey Experiment</a> we were introduced to the term <I>detuning frequency</I> (The difference between the frequency used for the control rotation, and the precise frequency).\n",
+ "<br>Hahn Echo experiment and CPMG sequence are experiments to estimate $T_2$ which are robust to the <I>detuning frequency</I>.\n",
"The decay in amplitude causes the probability function to take the following form:<br>\n",
- "$$f(t) = a \\cdot e^{-\\frac{t}{T_2}}+ b$$\n",
- "The diffrence between Hahn Echo and CPMG sequence is that in Hahn Echo experiment, there is only one echo sequences while in CPMG there are multiple echo sequences."
+ "$$f(t) = A \\cdot e^{-\\frac{t}{T_2}}+ B$$\n",
+ "The difference between Hahn Echo and CPMG sequence is that in Hahn Echo experiment, there is only one echo sequences while in CPMG there are multiple echo sequences."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## 1. Deohernce Time\n",
- "Decohernce time is the time that takes for the system to lose it's quantum properties and return to the classical physics regime.\n",
+ "## 1. Decoherence Time\n",
+ "Decoherence time is the time taken for off-diagonal components of the density matrix to fall to approximately 37% ($\\frac{1}{e}$). For $t\\gg T_2$, the qubit statistics behave like a random bit. It gets the value of `0` with probability of $p$ and the value of `1` with probability of $1-p$.\n",
"\n",
- "Since the qubit is exposed to other types of noise (like $T_1$), we are using a $Rx(\\pi)$ pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation."
+ "Since the qubit is exposed to other types of noise (like <a href=\"./t1.ipynb\"> T<sub>1</sub></a>), we are using a $Rx(\\pi)$ pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation."
]
},
{
@@ -68,18 +69,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- " ┌─────────┐┌─────────────────┐┌───────┐┌─────────────────┐┌─────────┐┌─┐\n",
- " q: ┤ Rx(π/2) ├┤ Delay(1e-06[s]) ├┤ Rx(π) ├┤ Delay(1e-06[s]) ├┤ Rx(π/2) ├┤M├\n",
- " └─────────┘└─────────────────┘└───────┘└─────────────────┘└─────────┘└╥┘\n",
- "c: 1/══════════════════════════════════════════════════════════════════════╩═\n",
- " 0 \n"
+ " ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌─────────┐┌─┐\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(π/2) ├┤M├\n",
+ " └─────────┘└───────────────┘└───────┘└───────────────┘└─────────┘└╥┘\n",
+ "c: 1/══════════════════════════════════════════════════════════════════╩═\n",
+ " 0 \n"
]
}
],
"source": [
"qubit = 0\n",
"conversion_factor = 1e-6 # our delay will be in micro-sec\n",
- "delays = list(range(1, 50, 1) )\n",
+ "delays = list(range(0, 50, 1) )\n",
"delays = [float(_) * conversion_factor for _ in delays]\n",
"number_of_echoes = 1\n",
"\n",
@@ -92,7 +93,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We run the experiment on a simple, simulated backend, created specifically for this experiment's tutorial."
+ "We run the experiment on a simple, simulated backend, tailored specifically for this experiment."
]
},
{
@@ -119,7 +120,7 @@
"metadata": {},
"source": [
"The resulting graph will have the form:\n",
- "$f(t) = a \\cdot e^{-\\frac{t}{T_2}}+ b$\n",
+ "$f(t) = A \\cdot e^{-\\frac{t}{T_2}}+ B$\n",
"where *t* is the delay and $T_2$ is the decay factor."
]
},
@@ -132,7 +133,7 @@
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -161,16 +162,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.73150194e-01 5.03648438e-01 1.98283181e-05] ± [5.15456349e-03 3.04084131e-03 5.77525843e-07]\n",
- "- χ²: 0.7488240853624647\n",
+ "- value: [4.77306456e-01 5.01013038e-01 2.00569581e-05] ± [3.60925695e-03 2.92038248e-03 4.68026484e-07]\n",
+ "- χ²: 0.9207937705047796\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 1.982831812408823e-05 ± 5.775258431912853e-07 s\n",
- "- χ²: 0.7488240853624647\n",
+ "- value: 2.0056958094880182e-05 ± 4.6802648351047525e-07 s\n",
+ "- χ²: 0.9207937705047796\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -188,7 +189,8 @@
"metadata": {},
"source": [
"### 2. Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, therefore we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
+ "The user can provide initial estimates for the parameters to help the analysis process. In the initial guess, the keys `{amp, tau, base}` corresponding to the parameters `{A, T_2, B}` respectively.<br>\n",
+ "Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, therefore we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
{
@@ -198,7 +200,7 @@
"outputs": [
{
"data": {
- "image/png": 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V6A28X+Wh94FgO4OkAduqnCsEDnPOpVRTPq5atLAeAIB334XHHoObboLcXLjjDpscKNKYXHLJJfzqV7+K6Wvm5OTs3Oe+ffv2tG7dmvXr18f0PUQam3hvBtQaSAZWVTm/Cjg+yHPeA37rnPsL8DkWQFwOpJS93oqKhZ1zw4BhYPt0z5o1K+LK5efnR1UeYPlyOPZYmDOnLxs3pjNs2Fd062Zf/5OSbG5Abm5UL9mg7E6bNibbtm3buX96JEpKSqIqXxuGDx/On/70p13Of/LJJxx00EHcc889eO/ZsmULp5xyCj169ODhhx+O2ft/8cUXFBcX06JFi5i0RTRtOmXKFB577DFWrlxJjx49eOCBB0LuanjffffxwAMPVDrXtm3bnZvtRFomkvfesmUL99xzD2+99RZr1qzhoIMO4ne/+x29e/eO6LPFSix+R7dt26a/GxXU2t/RSDYMiNUB5GIbSxxd5fydwHdBnpMBPAsUAzuAZdicAA+0C/V+sdwMqDrr13ufnm4bAwU70tO9r7DvSaOjzYBCi/Z3NLDRSiINGTLEH3/88X7FihWVjuLi4l3KHnPMMf7qq6+O2XuvW7fO9+zZ08+ZMydmrxlpm06fPt03adLET5482X/77bf+mmuu8VlZWX7x4sVBnzNmzBi/3377VWqn1atXR10mkvc+55xzfPfu3f3MmTP9Dz/84MeMGeObNWsWdMOd2hKL31FtBlRZbW0GFO85AGuBEqBdlfPtgJW7FgfvfaH3/jIgE+gMdALygC3AmtqqaCRefdW2CA4lOdnSBYs0JGlpaTu35g0cTcrGwgJDAJdccgkfffQRkyZNwjmHc468vLxqX+/cc89ljz324JFHHtl5bv78+WRmZjJ9+nQAtm/fzhlnnMEtt9wS8lt3bZkwYQKXXHIJQ4cOpUePHjz++OPk5OTw5JNPhnxekyZNKrVTmzZtoi4T7r0LCwv585//zAMPPMCAAQPo2rUrY8eOpWvXrmHrJ41XXAMA730RMA8YWOWhgdhqgFDPLfbeL/XelwDnAW957xO66n7lSigoCF2moMDKiTQ2jz76KEcccQSXXnopK1asYMWKFey5557Vln3kkUe44IILuOuuuwC72J9//vkMGjSI8847D+89l1xyCb/85S8ZPHhw2Pe+7777yM7ODnl8/PHHEX+WoqIi5s2bxwknnFDp/AknnMCnn4b808XChQvJzc2lS5cunHfeeSxcuDCqMpG8944dOygpKSE9Pb1SmYyMDD755JOIP6c0LvGeAwAwAXjROfcZMAcYjg0NPAXgnHsBwHt/cdn9bsDhwD+BltgqggOAIXGveRXt29tSwFCTkTMzrZxIQ/Luu++SnZ298/5RRx3F3/72t0plmjdvTmpqKpmZmbQP858gJyeHG2+8kYkTJ7J48WIeeeQRNm/ezKRJkwCYM2cOL7/8MgcddBCvv/46AC+++CIHHnhgta83fPhwzjnnnJDv2aFDh3Afc6e1a9dSUlJCu3aVOy/btWvHhx9+GPR5hx9+OFOnTqV79+6sXr2ae+65h379+vHNN9+wxx57RFQmkvdu2rQpRxxxBPfccw8HHHAA7du3Z9q0acydO5euXbtG/DmlcYl7AOC9f9k5twcwGsgBvgZO8d4vLitSNR9AMnbR3w+bBzAT6Oe9z4tPjYMbNAiuuy50mZKS8pTBIg3F0UcfzeTJk3fez8jIqPFrdu7cmRYtWvDggw8yefJkZs+evXNf+SOPPJLSKNJstmrVilatWtW4TjV18sknV7rft29f9t57b55//nlGjhwZcZlIvPjii1x22WV07NiR5ORkDjnkEM4//3zmzZtX8w8iDVJCMgF675/w3nf23qd573t772dXeGyA935Ahfvzvfe/8N5neu+be+/P8N5/l4h6V9WyJYwaZd/yq5ORAVdfbUsFRRqSzMxMunbtuvOI5tt0KL169eKJJ55g9OjRHHHEEbv9OrEeAmjdujXJycmsWlV5AdOqVavC9m5UlJ2dzf77788PP/wQcZlI33ufffbho48+Ij8/n59//pnPPvuM4uJi9t5774jrJ42L9gKooXHjYORI2xugaiBw9tlwxRXaH0Aar9TUVEpKSiIu771n//33Z/To0TV63+HDh/Pll1+GPPr06RPx66WmptK7d28++OCDSuc/+OCDqCYkbtu2jQULFpCTkxNxmWjfOysri5ycHDZs2MB7773H6aefHnH9pHFJxByABsU5uPtuCwJefRXmz4fZs2HePAsKvLc5AmU9mSKNSufOnfnss8/Iy8sjOzubVq1akZRU/feOSZMmMXv2bPbbbz+Swy2vCaM2hgBGjhzJ4MGDOeyww+jfvz9PPfUUy5cvZ/jw4TvLTJw4kYkTJ7JgwQIARo0axWmnnUanTp1YvXo1d999N1u3bmXIkPIpTJGUieS933vvPUpLS+nevTs//vgjN910E927d+fSSy+NaTtIw6EAIEZatoShQ21L4Pfft2//r7wCN9xg+wNkZ1uwINKYjBo1iiFDhtCzZ08KCwtZtGgRnTt33qXct99+y0033cTVV1/Nk08+SUFBAZnBxtYS5Nxzz2XdunXcc889rFixggMOOIB33nmHvfbaa2eZtWvX8t135SOUS5cu5fzzz2ft2rW0adOGvn378s9//rPScyIpE8l7b9q0iVtvvZWlS5fSqlUrzjrrLO69915SUhKeMFXqKOcbcK7aPn36+M8//zzi8rNmzWLAgAE1es/SUvjpJwsAvvoKJkyAk0+2bYNjME+q3olFmzZkffr0IZrf0S1btuycGNdQbN++ncMPP5yePXvyzDPP0LRpU+bMmUPfvn3j8v4NsU0TKRbtGe3/i4Yu2r+jzrl53vuwY1yaAxBjSUnWG3D++XZ/6lRITdXmQCLB3HLLLWzatIknn3ySzMxM9t13Xx599FGWLFmS6KqJNGgKAGpB06Zw4ok2+/+//4VvvrGhgaKiRNdMpG55//33mThxIn/84x9p3rw5ALfffjv/+Mc/Ko2Bi0jsKQCoBWlp0KoVnHee3X/mGdsxcPPmxNZLpK454YQTKC4upn///jvPDR48mFWrVjFz5swE1kyk4VMAUEv22APOPdf2Anj7bRsC2LDBEgOJiIgkmgKAWpKRAR072gTAkhJ44QWbIJifn+iaiYiIKACoNUlJNgwQ2LfkpZdsCGDSJEseNGWKJgaKiEjiKA9ALWraFA46CHr1siWB/fpZL0BpKWRl2T4Co0ZZQKAcASIiEk8KAGpRSooFAYGtvXfsKH8ssIPghAl2e/fd8a2biIg0bhoCqGXOWWrgYAoKYPx42LgxblUSERFRAFDb3nrLVgKEkpwMM2bEpz4iIiKgIYBat2oVbNsWukxBAaxcGZ/6SN2Sk5MT1a5027ZtIz09vRZr1PioTWMrFu0ZardEiR0FALWsfXvbJjgw5l+dzEwrJ43Pm2++GVV57a0Qe2rT2FJ71h8aAqhlgwaFT/5TUmKbB4mIiMSLAoBa1rKlLfULtrNpZqY93qJFXKslIiKNnIYA4mDcOLt96CHwvnxToNRUGDIExoxJXN1ERKRxUg9AHDhn6/wXLoTbby8f77/5Zrj+etiyJaHVExGRRkgBQBzl5Fhq4DvvtPsvvmg7B65dq02CREQkvhQAxJFz0Lo1DBgAe+0FeXnw3nv2mLYKFhGReFIAEGdZWTb2f/nldv+pp6wXYN062yNAREQkHhQAxFlgl8DTTrMVAl98AZ99Zhd/zQUQEZF4UQCQAM2aQUYGXHqp3Z840e6vXateABERiQ8FAAmQnGy9AOefb0MCH30E//ufTQTMz0907UREpDFQAJAgzZrZVsEXX2z3J06E9HTrBfA+sXUTEZGGTwFAgqSmlgcAaWnwt7/BTz9BcbF6AUREpPYpAEigVq0sBfB559n9wFyANWvUCyAiIrVLAUACpafbXgCXXw5NmsAbb0D//nD00RYMbNiQ6BqKiEhDpQAgwVq3hj32gH33tUmAS5fCsmWWJjg3F+64Q70BIiISe9oMKMEyMuCJJ2yfgIoKC+12wgS7vfvu+NZLREQaNvUAJNjGjfDMM7B9e/WPFxTA+PFWTkREJFYUACTYq69aXoBQkpNhxoz41EdERBoHBQAJtnKlfcsPpaDAyomIiMSKAoAEa9/eVgKEkpFh5URERGJFAUCCDRpks/9DKSmBs86KT31ERKRxUACQYC1bwqhRwXsB0tPhssvAufjWS0REGjYFAHXAuHEwcmR5YiDnyicGdusGt95qewSE6ykQERGJlAKAOsA5W+e/fLmt+7/uOjucgwULyicAaimgiIjEihIB1SEtW8IVV1gK4DVrbHOgv/4VHn0UHngA1q2D0lJ4/XULCtq3tzkELVsmuuYiIlLfqAegDmrWzL79jxxpQwHTp8OiRRYI7LknDB8Od94JN9ygdMEiIrJ7EhIAOOeucs4tcs5tc87Nc84dFab8Bc65L51zBc65lc65PzrnGuzCuORk2yMgJwfOOcfG/i+/HJ57zjIGlpZaua1bYds2Gza4887E1llEROqXuAcAzrlzgUeB+4BfAJ8Cf3POdQpSvj/wIvA8sD9wBtATeCke9U2UQC/A9ddDaip8/335/gBVKV2wiIhEKxE9ACOBqd77Kd77+d77a4EVwJVByh8BLPXe/957v8h7/0/gceDwONU3IQK9AC1awOERfFKlCxYRkWjENQBwzqUCvYH3qzz0PtAvyNPmADnOudOcaQ2cB7xTezWtGwK9AAccEL6s0gWLiEg04t0D0BpIBlZVOb8KqHZM33s/F7vgvwQUAWsABwypvWrWDcnJsMceNtGvSZj1GpmZShcsIiKRcz6O08edc7nAMuAY7/3sCufvBC703u9XzXN6Ah8AjwDvATnAQ8CX3vuLqyk/DBgG0K5du97Tp0+PuH75+flkZ2dH85HioqAAvvwymXvv7UtBQQq//e1/6dFjfaUyzkGvXuF3Foy3utqm9ZXaM/bUprGl9oy9aNv02GOPnee97xO2oPc+bgeQCuwAzq5yfhLwUZDnvAi8VuXckYAHOoZ6v969e/tozJw5M6ry8bJhg/dXXul9kybe24K/ykdmpvejRye6ltWrq21aX6k9Y09tGltqz9iLtk2Bz30E1+S4DgF474uAecDAKg8NxFYDVCcTqJoEN3C/UeQxaNbMcgIMG1b5fEYGpKVZPoBx4xJTNxERqZ8SkQlwAvCic+4zbILfcCAXeArAOfcCgC/v3n8TmOKcu5LyIYBHgP9475fEt+qJkZQEbdvCiBHQpQvcdBNkZdkeASedBHvtpc2CREQkOnH/Bu29fxm4HhgNfIl155/ivV9cVqRT2REoPxVbOngN8DXwKvA9cHq86lwXZGfbRMCzz4aDD7YkQBs32sS/deugqCjRNRQRkfokIV3o3vsnvPedvfdp3vvevsKEQO/9AO/9gCrlH/fe7++9z/Te53jvL/TeL417xRMoKQnatLFkQHfcYeeeeAJWr7bAYM2axNZPRETql0Yxht5QZGfblsG9e8OJJ9rqgIcftrkAW7bYfRERkUgoAKhHnLNegG3b4LbbbMnftGnw3XcWBKxcWb5PgIiISCgKAOqZrCxL+tOxI1x0kV3wx42zYYDiYti0KdE1FBGR+kABQD3Upo1N+rvxRlsiOGsWfPihBQdr1sCOHYmuoYiI1HUKAOqh9HRo3tx6AkaOtHNjx1oPQFKSrQoQEREJRQFAPdW6NZSUwJAh0LUr5OXBs8/aXIANG4JvHSwiIgIKAOqtlBRo1cq+9d91l5175BEbAkhPh1WrLFGwiIhIdRQA1GMtW9rtUUfBwIGQnw/33w/nnw/nnKMJgSIiEpwCgHosOdlSBBcUwJgx1ivwyiuwZAmsWAETJ1qiIBERkaoUANRzTZvahb9DBzjoIDu3fDksXQr33QedOlnmQA0HiIhIRYnYDEhiKCnJ9gO48Ub45pvKjwUmAk6YYLd33x3fuomISN2lHoAGYPt2eO45yxBYnYICGD/eNg8SEREBBQANwquv2nyAUJKSYMaM+NRHRETqPgUADcDKleHX/RcW2rwAERERUADQILRvb1kBQ0lPtyRBmgwoIiKgAKBBGDTIsgKGUloKxx2n3AAiImIUADQALVvCqFGhewEuuABycixDYHFx/OomIiJ1kwKABmLcONsYKD3dJvyBBQSBnxcvBudg61bYbz/o3BmmTLF9A0REpPFRANBAOGfr/Jcvh6eegptvhltvhZkzbefAf/wDLr8c+vWzYGDxYrjhBsjNVaIgEZHGSImAGpiWLWHoUJsTsGgRpKXZBX7UKHj33cplt261WyUKEhFpfNQD0EAlJ0O7dpYE6KSTrIcgGCUKEhFpfBQANGDZ2TYP4I03rCcglORkJQoSEWlMFAA0YM7ZboGrVlm64FAKCiyhkIiINA4KABq4tDTo0sVWB4SSmWkJhUREpHFQANAIDB5siYBCKSmBs8+OT31ERCTxFAA0AnvsYUv+MjKqfzwjAy69NPjjIiLS8CgAaCTuuw+GD7chgYorApKSYNgwWya4YkX4ngIREWkYFAA0Es7BQw/Bp5/C/ffDkCE287+0FPr2tTkCRUWwfn2iayoiIvEQVSIg51xf4CSgL5ALZABrge+Aj4DXvfdKLltHJSdbGuDMTJsXkJMDDzwAN94IH34IzZrB2rWQlaXhABGRhi6iHgDn3BDn3P+AT4EbgEzgB+BfwAbgcOAZYJlzbqpzrkst1VdqKCvLUgMXFMCVV8IvfmHpg2+/3XoJMjLsfrjdBUVEpH4LGwA45/4LPAC8A/QGWnjvj/ben+W9v8h7f4r3vgfQChgKtAW+dc6dW5sVl93Xpk35z48+ahf9116zIyXF9gVYvVr7A4iINGSR9AD8Aejivb/Ze/+F99VfFrz3m7z3L3nvT8GGCDbGsJ4SQ02a2Jr/ggLYZx+46y47f9ttsGyZDRFs2gSbNye2niIiUnvCBgDe+0e999uieVHv/Vfe+/d2v1pS27Kzy4cCLrgATjjBLvgjRlj3f3a2ZQYMl0FQRETqJ60CaMQCQwElJbYZUJs2MHeuDQscfTQcd5ztFLhuXWLrKSIisRdxAOCcO8M595xz7l/OuR/Kjn+VnTujFusotaRJE1sJUFBgyYLGj7fzDz8MeXk2HHD33dChg20prDkBIiINR9hlgM65lsCbQD9gCfAN8H3Zw62AAcAQ59xc4FdaBli/ZGVBy5bW/f+f/9hSwZKS8ot9YaHdTphgt3ffnZh6iohIbEXSA/Aw0Ak4xnvf2Xt/qvd+cNlxqve+C3A00AEYX5uVldrRujXk58NTTwVf/ldQYD0EGzfGtWoiIlJLIgkAfg2M8t5/HKyA9/4T4GbgjBjVS+IoOdkyBCaF+W1wDl5+2X4eMMAOERGpnyIJANKwZD/hbARSa1QbSZj162FbmLUe27bBTz9Z2RUrYPFimDIFNmjQR0Sk3okkAJgL3O6caxqsQNljt2KZAqUeat/e1v+Hkp4O//qXTQr88UebKHjDDZCbq0mCIiL1TSR7AVwPzAIWO+feBr6mvEegJbA/cCpQAhwb+ypKPAwaBNddF7rM9u3w2WeVewq2brVbTRIUEalfIkkE9C3QC3geOAK4D3iq7LgP6A+8ABzsvf+m9qoqtallS9sSOFQvgPfBhwk0SVBEpH6JKA+A936F9/4G731XIAub8d8ByPbe71P22PLarKjUvnHjYORI6+oPTAjMzLR8ARC+iz85GWbMqN06iohIbESdCdB7v60sIFjhvS/cnTd1zl3lnFvknNvmnJvnnDsqRNmpzjlfzbF1d95bgnPOuvCXL4cnnrDx/dGj4b//tV0DwykosPTBIiJS90WSCOhM7/1fonlR51wOsJf3/p/VPHYu8ChwFfBJ2e3fnHM9vfdLqnm5EcAtVc7NAWZHUyeJXMuWcMUVNuafl2fJgs4+G776CkpLgz8vM9MmE4qISN0XSQ/A4865L51zw51zrUIVdM4d5ZybDPwIHBSk2Ehgqvd+ivd+vvf+WmAFcGV1hct2GVwZOIB9gL2BKRHUXWogLc1SBefnw+mnlw8FBFNSYoGCiIjUfZGsAtgXGAWMw4KB+cBXwBpgO7YSYG+gD9Ac+2Y+0Hu/y5JA51wq0JtdMwa+j6UajsRQ4JvqXl9ir1kzm+mfnw9XXglPPglFRbuWy8iA66+HFi3iXUMREdkdzke4eLvs4v0b4CTgcCAXSAfWAQuwC//L3vsFIV4jF1iGpRWeXeH8ncCF3vv9wtShOdZbcKv3/tEgZYYBwwDatWvXe/r06RF9PoD8/Hyys7MjLt+YBC76q1bBX/7Sgddf35eUlBKuvfYLOnTIp00baNvWeg0qUpvGltoz9tSmsaX2jL1o2/TYY4+d573vE65cJD0AAHjvi5xzfwfe8N6HyRlXay7Chi1eDFbAez8ZmAzQp08fPyCKfLWzZs0imvKNSVGRzQfo0QMOOcR2Cvz3v5N59tk+/PWvsM8+tnFQaqolCgqsIlCbxpbaM/bUprGl9oy92mrTsHMAnHPJzrmxzrkNwCpgs3Puz865FrvxfmuxhEHtqpxvB0Qyf3wo8Gfv/frdeG+pgdRUy/i3dat187/8MvTubev+R42yACEjw4KANWuUFVBEpK6LZBLgcOBO4Ats7P4N4HTg99G+mfe+CJgHDKzy0EDCpBF2zh2GJSTS5L8Eyc4u3zkwLc32AWjf3rID3nKLXfSzs21vAO0PICJSt0USAAwFpnjvf+m9v9l7fzZwNXBR2byAaE0ALnHOXe6c6+GcexSbT/AUgHPuBefcC9U8bxjwg/d+1m68p8TIHnvYcr+CAmjXDp591hIHvfwyTJpkZZo2hdWrYfPmxNZVRESCiyQA2Buomt/tZSAZ2CvaN/Tev4ztLzAa+BI4EjjFe7+4rEinsmOnss2GzgOeifb9JLacs6WBSUnW7d+rF0ycaOfvvx/eest+zs62hEKh8gaIiEjiRBIAZANVv8ttKbsNukNgKN77J7z3nb33ad773hVXBHjvB3jvB1Qpv8V7n+29f3B33k9iq0kTm+i3fbut/T/5ZLj9dntsxAj44gsLEDIzobg4/DbDIiISf5GmAu7gnNs7cGC9ArucL3tMGoG0NAsCtm61sf/hw+HCC+1if+ml8PPPFig4B0uXVp87QEREEifSZYCvBjn/ejXnknevKlLfZGfb2v/Vqy1h0L33wpIl8PHHcNFF8NprFgAkJVkQ0KlT+GyCIiISH5H8Ob601msh9VbLljYUkJ9vewZMngxnngnz58Mll8CYMUmkp9vywKVLYc89bddAERFJrLABgPf++XhUROon52w1QFGRXeSbNYMXX7S9A+bNg/vv78n06eU5ApYvr5woSEREEkN/hqXGkpLsou6c9Qbk5MBLL1nCoLlzW3PbbTZPICPD5gisWKHVASIiiaYAQGIisDKguBh27IB994WpUyE1tYSXXoLxZds/ZWXZxMFVq5QtUEQkkRQASMykpUHHjpYkqLQUDj0Ubr11PklJ8MgjNj8AbPLg5s02eVBBgIhIYigAkJjKzLQ9A/LzbTJg+/Zrefhhe+yuu2DaNPu5aVNLF6wgQEQkMRQASMw1a2abB61caUMCxcVw66322E03wV//Wl5u40ZtHiQikggKACSmvIc77oD997dkQEVFMHYsTJgAffva49ddB3//u5XPzob162HtWgUBIiLxpABAYurOO+1iv21b+Uz/ggJbHfDll3DIIdYjMHQoHHccDBpkwwHr1ikIEBGJJwUAEjMbNths/4KC6h/ftg2+/hrOO88Cgu++gx9/hD/9yfYUWL9ecwJEROJFAYDEzKuvhs/yl5xsF/mkJLvQr11rQwa9e8OTT2pioIhIvCgzu8TMypXBv/0HFBbC7NmVEwFt3263gWWCV15pAUC7dpZcSEREYk89ABIz7dvbMsBwduyo/nxhITz1lAUHmzcrY6CISG1SACAxM2iQjeXX1Ftv2eqA/HzbOyAWrykiIpUpAJCYadkSRo0K3guQkhL+NbZvhzlz7OfsbJs4uHRp8F4DERHZPQoAJKbGjYORIyE9vXzHv6wsuz9woP0czptvwhtv2M+DB8PZZ1sQUFxce/UWEWlsFABITDkHd99tXfdPPWVpgX//exvP/+Mfw3fnJyfbuP/VV8PTT9umQStW2O6C//tf+YRBERGpGQUAUitatrRkPzk5dtuiRfghgowMu/DffLOtAhg3DvLy7Nv/ffdBv35www3hVxqIiEh4CgAkrqobIsjMtJ0EL7sM/u//bNw/MF8gkA8gkE1w6lQLIrZsSUj1RUQaDOUBkLgKDBGMHGmJg1autOWDZ5xhF/VVq2zoINh4f2EhPPssDBkC3brBb35j52fNitcnEBFpGBQASEIEhgiqnnv55fKegWCSk+Gjj+znn3+2eQVTptgyxJYta6e+IiINjYYApM5o0sR2D9y2LXS5ggJ47TU45hibI7B4sc0NyM21tMJKIywiEp56AKROyc21OQFbt4Yu9/nnlXMDBMpPmGC3d99dO/UTEWko1AMgdUqk2QSDJQYqKLAdCTdujGm1REQaHAUAUqeEWyrYpEn4DYKSkuCVV2JfNxGRhkQBgNQ51S0VzMiwpYJ9+oQf4y8shJ9+0kZCIiKhaA6A1DnVLRVs3hz69oWZM+G//w2dDCgjA5o2tRUCOTmQmhq/uouI1BcKAKTOqrpUMDDRb8yY0M8rKYGzzrLbxYstwVBysnIFiIhUpCEAqTeysqBXL7j8cvuWH8wxx1iPQXq6LSn8+WdYuBAmT4YNG3YtP2CAHSIijYkCAKlX0tJsqd9vf2s/V5wjEPj5/fctH8D998Ohh1oA8PPPcP31u+YK2LDBNhtavNiSCVUXIIiINEQKAKTeSU2FRx+FL76APfeEjh3hrrvg66/hd7+zfQSefRaeeML2DwhMBiwstB6BCRMsCLjjDgsIfvzREgopmZCINCaaAyD1UlISdO8O//kPrF5twwPJyXDRRRYQXHhh8FUABQXwwAMWSFTMOqhkQiLSmKgHQOot56BVK7vgB3YLBFi2LPQcAbAJgoWF1T+mZEIi0hgoAJB6LzsbOne2bvuCAusRCLefQDjJyTBjRkyqJyJSJykAkAYhLQ322ssyCDZtGr4HIJyCAss/ICLSUCkAkAYjOdkS/wweHNl+AqFkZkL79rGpl4hIXaQAQBoU56BLF1vyV5NegJISOPvsmFVLRKTOUQAgDdL999uyvoq5AjIz7f4FF8AeewR/bkaG5RlISYlPXUVEEkEBgDRIzsG998Ly5dYjEFjf/8UX8NBD8K9/wSGHVH5OeroFCMOGwU03WfKgFSuCbz0sIlKfKQCQBq1VK0v08+OP1qUf+FafkQFvvglTp9oWwwDFxXDFFdZzkJICzZrZZMBFi2DLFiUHEpGGJSEBgHPuKufcIufcNufcPOfcUWHKpzrnxpU9Z7tzbolz7rp41Vfqv4wMWyqYlQWbN5dPEhw40DIIXnihnXvsMTj1VDsXeF56uuUWWL4ciooS9hFERGIq7gGAc+5c4FHgPuAXwKfA35xznUI8bTpwEjAM2A84G/hvLVdVGpjAKoEOHSwJUCARUNOm8OCDMH26pRb+5hs45RSbR1BYaM9r1sxyC+Tlwfr1wbMMiojUF4noARgJTPXeT/Hez/feXwusAK6srrBz7gTgOOAU7/0H3vs87/2/vPez4ldlaUiaNrV5Aamp1hsQuJgfdRT8/e82AbC0FCZOhOOOg9mzbXvhiy6yiYRr11ogUFAQ+Xtqx0ERqWviGgA451KB3sD7VR56H+gX5GlnAP8GRjrnljrnfnDOPeacy669mkpDl5JiKYRzcmwPgEBvQFYWjBsHr79uew0sXgznnw//+5/9PG2aTQpMTi6fJFhcHPq9tOOgiNRF8e4BaA0kA6uqnF8FBEu7sjdwJNALOAu4BhsOmFo7VZTGwjlo3ry8N2DLlvLegD594G9/g35lYenWrXYRv/12+MUv4Pe/txTEBQWwcCH07w/HHFP59b3XjoMiUnc5H8e/Qs65XGAZcIz3fnaF83cCF3rv96vmOe8DRwHtvfebys6dALxXdm5VlfLDsLkCtGvXrvf06dMjrl9+fj7Z2epYiKX61KYlJeVL/pKSLBXw2rWwZk06f/nLvnz3nSUPyM3N58wzf+CwwzbRvr0974cf7ILevr3lGEhOtkmDq1ZVP18gKQnatbNgIBr1qT3rC7VpbKk9Yy/aNj322GPnee/7hC3ovY/bAaQCO4Czq5yfBHwU5DnPAz9WObcn4IFDQ71f7969fTRmzpwZVXkJr761aVGR90uXev/Pf3qflua9XdarP5KSvL/oIiuXlGTnMjLs/vXXe5+eHvr56eneb9gQXf3qW3vWB2rT2FJ7xl60bQp87iO4Jsd1CMB7XwTMAwZWeWggthqgOnOA3Cpj/t3KbhfHtobS2KWk2CqBzz4rzyAYTGkp/PGPtg1x4Ft+YaHdnzQpfAIh7TgoIomUiFUAE4BLnHOXO+d6OOceBXKBpwCccy84516oUP5PwDrgOefc/s65/tgywle996vjXXlpHDZurNmWwsXF4QOAqjsOaqWAiMRT3AMA7/3LwPXAaOBLbILfKd77wLf5TmVHoHw+cDzQHFsN8ArwEXBZ3CotjU779rbkrzZV3HFQKwVEJN4SkgnQe/+E976z9z7Ne9/bV5gQ6L0f4L0fUKX8d977E7z3md77Dt77q733W+JecWk0Bg2q+ZbC4ZSU2PtopYCIJIL2AhCpRsuWMGpU8F6AlJTyPQRCca768xkZcPXV8PDDMGGCDTcE5hFs3Wr3J0yAO+/cvfqLiISjAEAkiHHjYORI2wsgMCEwK8vuX321TeILp+o3+MCWxEOHwjnnwPjxwTMKFhTY4xs31uhjiIhUSwGASBDOwd1323r+rl1tM6Hf/97G6n//e9syOFgPQUaGpRS+5JLKPQU9esBrr8HNN8NHH4UPIrRSQERqSwSdmCKNW8uW8N13u54fN85ux4+3XQJLS+3CX1oKw4ZZgOAcXHmlBQwzZsC8efCrX8Hpp1sWwkAK4mCqrhQQEYkV9QCI7KaqPQR77QUPPACffgpXXVXe/d+xo431f/KJbTucnGy9AM8/Hz7XQEZG+UoBEZFYUg+ASA1V7SEoLYVNmyyNsPc2TJCUBJ062bbDI0bYToPTp1vPQSglJXD44ZCfb/MPRERiRT0AIjGWlGRBwd57Q5s21s2fn18+y79DB7j/fvjnP6F37+Cvk5EBw4dDs2awbBn07WuBRrgEQyIikVAAIFJLkpOrDwQC+QXatYM33rCLfHWTAQ8+2CYSpqbac9assTTD998PCxbULFOhiIgCAJFaVjUQ2L69PBBwzhL+/Pe/tsqgZcvyMf+5c+HQQ+H44y0YWLzYUgw/8IDdv+EGWLSo8jbGIiKR0hwAkTgJBALNm1sAsHatzfJPT4cWLWDOHCvnPXz8saUE/sc/YP788tf4z3/a7swb8PzzlpDo6qtt2KFFCxsuSEuL9ycTkfpIPQAicZaUZBfqLl1sPoD3sHlzeZe+c3D00fD449b9X9Gf/tRz58+FhTB5sn37z8y0iYd5eXZs3lz7qYxFpH5TACCSIM5BdrYtH9xrL7vYb9livQLew9tv75puODc3v9L94mJbirh9uwUBTZva665cCT/9ZLeFhdpTQER2pQBAJMGcsxn/HTtar0CLFhYELF26a6KgG274vNL90lKYNs1WE4weDSedBOeea4FFVpbtK7BkCSxcaEMO27fH73OJSN2mAECkDklNhdatbcLgPvvY/ICKqm4ulJJiwwgbN8Jzz8H//mfZBocOhR9+sMCiaVObF7B4MXTvboHGI4/AqlXx+lQiUhcpABCpg5KTYfDg8F33SUnw/vtwwQXlSwl37IB33oFjj4UjjrCEQ/feC/37W2/AsmVw22027HDttRY8FBfX+kcSkTpGqwBE6qjAlsQTJlS/Y2BGhu05MHmypRaubtLfkiVw4427ng8MLfzhD/a8ESPKVyOcdpoFFrNmxfLTiEhdox4AkTos1JbEV1xh3/yfeir8pkLBFBbCs8+W5xH4/nsLGn780YYJtBGRSMOlAECkDqu64VBqauUtib/4IvyWwuHs2GHv8cADcNRR8PPP5cMEnTvDNddYFsJYriYYMMAOEUkcDQGI1AOBDYdmzap84Vy9eve//QeUlNhKgqoCr/vss3bhv+4664Vo2rR8YmHVZYqR2LDBApiiIkt2NGiQfT4RiS/1AIjUY+3b2/r/mkhJCf14YSE880x5wqHTT7cJhgsX2sqCDRssiVG43gHvLe1xbq4NMeTlWTrj3Fw7r1wFIvGlAECkHhs0qOYZ/7y3CYWhFBXZqoSXX7Z5AStW2EZGgW2PA/MGVqywNMdHH71rF/+dd9qExm3byuccbN1q9ydMsMdFJH4UAIjUY4GVAsF6ATIz4cgjgz+ekQF9+kS2s+C8ebaiYMkSS1J022323Mces4mJmZnWWzB/fnlA8PDDVnbNGhg/vvrVDGDnx4+3JYkiEh8KAETquVArBUaOhI8+Cv74lVfCr361a8KhqpKTy58bsGOH5Q+YONEmDz7zjH2LP/LI8omEd9xhkxfPPnvXJEbVvceMGbvXBiISPQUAIvVc1ZUCnTuXrxS4+267cAd7/OGH4aqrwo+/l5SE3nJ40SIYOxZeesmGCwJlCwst/fCcOeEnKxYUaNmhSDxpFYBIAxFYKRDt4+ESDqWkWICwY0fw105JsceDBRKhnhuQnm4rCzZtstuUlJovcRSR4NQDICIhhxH69g0/0bC4ePeWBFZUWgonnmh7FCxebLsZ5uXZ/IH8fOtZaKgrBepCXoQNG2C//WxDqilT7L40bAoARCTkMMLgweGXGjZpUrP9BJyzC8/bb9uFPy3NdjRMSoLNm61eeXk2sXDJEli3zlYQNISgIJAXYfHixFx4tTyz8dIQgIjsVN0wwaBBlgQonIyM0OP8GRnQrx98/LEFC97bBb601H5esABuv93KpqZCz57Qq5cdBx4I3brZkMCOHXaRLCmxwME5CxgyM63HIiXFjqqTFmtL4Jt7tHsneG+TJsePL583ccMN1tajRlmvTLiJk7FQcXlmwNatdjthgt3efXft10PiTwGAiIQUbo5AZqatJpg0KfTrlJbakkGwlQfFxXaxO/FE+4b/n//Y8dVX9k30yy/tCEhPhx494IADYP/97ejRw87v2GFzB9avt7LeWxARCApSU62XokmT2F5Ua5LVsC5ceDdssAAk2DLQwPLMG2+0jaKkYVEAICJhjRtntxW/rWZl2bfwkSPt8YyM0EHC0KH2zby0FN59177Np6TYRbl1azjoILjkEiu/ZQv85jd2QezVC/73P+ua/uILOyraZx/rLeje3W579IAOHSwI2LrVAoOAQG9BRoYFBoF5C95HFxjU9Nt7Xbnwvvpq+ImWgeWZQ4fG5j13t8dEYk8BgIiEFZgjMHKkTQosKrJEQGefXX6BiiRIAPvmX1RkwwX5+ZUvgk2aWFBQUlKeMfCoo2yjIufg668tGPjqK/jb3+y1fvrJjjffLH+drCy7DfROdOtmR06OvfbmzeVj7du3W49DaqoFBYFhhECPQXVDCTX99l5XLrwrVwZPzhQQy+WZ2geiblEAICIRC7XUMJIgAexCm5pqk/zatLGLfFGRHQUFcM89llSouNgeGzPGJqMNH27frufMgffeK1+ZEBgCOPBAe80FC2zlANhFORB4gAUE++5rvQZdu8Jrr8G552bRtavVPz/fegwqTnxLSbFeg8BQwpYtNf/2XlcuvIG9JALBS3UyM61cTcR7vkNd6WWoK/UIRgGAiMRUuHwEVSUllX/zfugheO45+1YeEJhYOHkyfPIJfPtt5ccDF+IFC+zb8iGHwJNPlk80bNLEgoX0dLuofvWVHQH33HMo995rvQNdu8Lee9uKhM6d7ejQwV5r2zZ7nZdfjjyrYbBv73Xlwhv4OZSSEgviaiKe8x3qSi9DXalHSN77Bnv07t3bR2PmzJlRlZfw1Kax1ZDbc/1679PTvbfL1u4dycnBXyMjw/thw7z/85+9P+44Kwvet269Nezrtmzp/WGHeX/++d4feWT4ejjn/YgR3i9c6P2yZd6vXev95s3eb93q/bZt3q9eHf6zpqd7v2FD5O13zDF2VDR6tPeZmdW/fmamPR5JmVDvUVXV39FI/l2j/azVKS21uqane5+UZK+blWX3R4+2x+OhNuoR7f974HMfwTVSeQBEpE6IZFw8nMDcgeoUFsLzz8MHH8Cnn5YPIdxyy2c7y6Sm2l4GBx5Y+Vv+hg3w2WcwbZr1QoSTkmLf/FatgjPOgJNPtm+DS5faev/16+HSS4PvwpiZaUMpzZvb/XCJgqrLJRCYaBhuA6aRI0PvJREYQtndfAXRzHeoiUTsNlndv0u09UhkEigFACJSJ0QyLl5Tztn8gmD5CoqKYO5c+OGH6hPgpKTYaoVwOQaKimwY4ogj4N//tuWNJ5xg3e3jx8Of/mRBxqmnWtARCDYyMmy+wZAhloDphx9s1UMgM+Lvf28/b91avs/C7bdXn8Tnoosiu/C++mrovSSgZomC4jHfIdJgJ5a7TdYk6ArUI9FJoDQHQETqhEjGxWtq2za7iIdSUhI89XFxsc1vGDbMehOqCySaNLGLaH5++UWtpMT+yC9eHPx9k5LsG/8++1imw4kTbdXDnDn2fO9tUuUtt8BvfwsjRsCjj8LUqdWPrb//fvg9GCpeeIPN3ajp+H085jvEc1WFDzGv4pe/jKwer7xiO2YmOgmUAgARqRMizThYEykpNUtZDHahzs21nAXPPlu+WiEz0y7Uw4dbucmTq39+kyaWxKhDB7v4rlwJq1fbxTpwP5jARfipp+DPf4a1a4Pv0hjNBkyrVpUvewxs/ZyUZN9Uo1nxMGAAXHBB5S7tSP5dazrRMJ6rKkIFRB98EP73q6DAeoD+/e/EZ1/UEICI1AmBjIPB9h3IzLTx+WCPZ2SE35DI+/D7GoSzbZv9kX/wQZg/H/baCzp2tK74Tz6BCy+0C3SwYYYdO2zFwvjxlrvg3/+27ZS/+sqWN77wAtx1V+hvkt5b0BBqi+ZIFBfbxfedd+Cjj6wu33xjPRVLlthwSbhvoklJ1guxZAksW2bDEk88YRfb4mJo1swChFD/rqNG1SzhUaCXIZRoexmqG5sP18UfSXCZkWFzUOI5XBGMAgARqTNC7Uo4cqRdpII9fuON1j0e6kIzYkTNL5qZmbDnnpZzoEsXWLjQunNvuw1694Z588J3Aycl2Tf4/Hw7Cgrsdbt2tcRHaWl2hBKLrZJ37LB6X3YZnHUWDBxoORx69oRDD7VlmaH2dwCr+9NPW91/+gk2bEjhxhttGGTECJvHMHiwzWtISyv/d8vMtPvDhlm5DRssx8JRR9lRWGjB1vbt1k2+Y4cFK4G9IyoaNCj8jpXR9DIEG5uPxUTVoqLww1CxmBQZCQ0BiEidEUkyoVCPBy4Mu5uyOCPDvsWF6j4PdSFxzpIQhbtobttm79GpU/lFbceO8gvdqlXhX6OkxHo8QtU1PR0OP9wmNgbyIqSm2nN797a0yRs32gVu40ZLgrRxo2VK3Lw59PtXtGBB+c9jxvTf+fOTT1p3dyBgOvxwC5AAfvELGwrJyLAek8xMq9/339vtzTfD8cdDu3blkyMDvRHelw9VOGe3w4bZxbq6dsvMhGuusQtvQUH5JlJVD4CxY+Hhh6sfmw88P5xgQ02ZmRZYzZ4d+vmxTAIVigIAEalzwiUTCvb47qQshvIg4cYb7X6oPQ1GjgzdXR3ppLcOHewCXZ0ePcK/RiBYCcV7+xYPcM45Vv6qq2wzpubNy8f6q3bzl5ZaAPDzz3DaadHNm8jMLKagoPwr7qZNlfdjCPjkk9BLKh9/3I6ApCRrk8CRkVF+G9jboXNn+70I9PIEhoQOO8yyTk6dWp7VMZDZseLPf/gD/PGP1Y/NP/yw/U6lp4ff9fKoo+wiHwgiMjPt9rrrbLjo3/8OHUjEIglUJBQAiEiDE03K4tRUW/YWTS9CKLGY9BbJa3gP119v4+3BgpXrrrPu/NJS21lxx47yo6Sk/DawtXLgdZ2zi2eXLnD55XbhrO6il5Ji5Sv2QowbN4dRowbsvJ+ebt/OjzzSLqaBYY/8fLu/dauNiS9YELwbPznZHgs8L1KBes2aVfN0vIWFkb1GUZEFdxdfbLP9vbeA4OCDoWlTC4YCgWcwsci+GImEBADOuauAm4Ac4Bvgeu/9x0HKDgBmVvNQD+/9gmrOi4iEFQgSZs2qPNkr0j0NQr1uuO2Tw/UiRPoa48bZBTZUsBLJcrJATr7S0vIx9sDt735n3fePP175G21JiV3UPvss9GsH0jb36lV+LtBtD9bT8MILocfwmzSxXAqBLvjCwvLbikdBgX17r3hu27bK5wLzCr7+2j5Lu3Z2bvPm8N374XIegH2O556rfO6tt+yIRCS/H7ES9wDAOXcu8ChwFfBJ2e3fnHM9vfdLQjx1f2B9hftraq+WItLYRbunQUWR7oxY09eoabASEBgDD5bg6MEH4dZbd32PGTPsQhpuqKJbt/Itmr0v73UoLYXXX49s0uRf/2rDGE2b2hFMoAej6th+xZ83b7YMjTt22LLNU06xvA4TJoS/yJ97rgUQb71Vnp8hNdU+S79+1tNRVFS+62XgCNzfvr18JciSJeXvF+3vRywkogdgJDDVez+l7P61zrmTgCuBW0M8b7X3fm2t105EpIZicWGO5jVqEqxEqrr3iGSoorTUlkYGtmiuKvBNPpTARkx7721plPv3twvq//2fXcgDQzdVj0CPRuAoKbFg5qmnyvM33HWX7Th5xBGRje8fcIAFIrfdZrdFRbbl9Ekn2ZLH6lQMQirebtoEZ55p9bjjjugDt5qKawDgnEsFegPjqzz0PtAvzNM/d86lAd8C93jvqxsWEBGpM2JxYY7HxX13xWK4I9JJkzk5drGu2CPyf/9nEzcjzZ53xx22UqC63Sb/9a/IJlUOG1YecCxYUB5sBB6v+HPgNjApseKt9xYwfPaZ3QabEFqbnI9kUCNWb+ZcLrAMOMZ7P7vC+TuBC733+1XznP2AY4F/A6nAYGB42WvsMm/AOTcMGAbQrl273tOnT4+4fvn5+WRnZ0f1mSQ0tWlsqT1jT21ac8uX27I176Fjx3yWL8/Ge7u45+aGfm5JiSVBCnUpcg7atrUlltXlcUhKsrH8UO8VyfsEXmt336O2RPs7euyxx87z3vcJWzCSLQNjdQC5gAeOrnL+TuC7KF7nHeCv4cppO+DEU5vGltoz9tSmsbF+vffdunn/2GMz/eTJ0W3vG25L4htvrPmWwpMn27a8oV4jM9P7U09N/JbCVTWU7YDXAiVAuyrn2wHRpD34F7BvrColIiI1ExiqOPBA23AnmrHscBkgu3Wr+ZbCkewXUFhoyYqC7YwYjw164imuAYD3vgiYBwys8tBA4NMoXupgYEWMqiUiIgkUmPAY7MK7alXNN/uJZr+AQDCzaFH0wUx9kohVABOAF51znwFzsPH8XOApAOfcCwDe+4vL7l8P5GH5AlKBi4AzgLPiW20REalNwSY8xmJL4XjsSljfxH0zIO/9y8D1wGjgS+BI4BTvfWCn7E5lR0Aq8BDwX+DjsvKneu//Eqcqi4hIAsVis59Idpus6a6E9U1CMgF6758Angjy2IAq9x8EHoxDtUREpA6KxXJDiE2CpoZE2wGLiEidF26iYCQX73BzDRraJL9wtBmQiIjUebFKewx1O7lSPCkAEBGRekMX79jREICIiEgjpABARESkEVIAICIi0ggpABAREWmEFACIiIg0QgoAREREGiEFACIiIo2QAgAREZFGSAGAiIhII6QAQEREpBFSACAiItIIKQAQERFphJz3PtF1qDXOuTXA4iie0hpYW0vVaazUprGl9ow9tWlsqT1jL9o23ct73yZcoQYdAETLOfe5975PouvRkKhNY0vtGXtq09hSe8ZebbWphgBEREQaIQUAIiIijZACgMomJ7oCDZDaNLbUnrGnNo0ttWfs1Uqbag6AiIhII6QeABERkUZIAYCIiEgjpACgAudcJ+fcm865rc65tc65x5xzqYmuV33gnOvlnJvmnPvZOVfonPvOOfd/zrmkKuUOdM59VFZmmXPuTuecS1S96wPnXOuytvLOudZVHlN7Rsk5d5Fz7kvn3Lay/+cvVHlcbRoh59yhzrkPnXMby46/O+cOq1JG7RmCc+5R59znZb+PeUHKhG1D59xZzrlvnXPby25/E+69m8ToM9R7zrlk4G1gHXAUsAfwPOCAaxNYtfqiN7AGGAwsAQ4DpmC/Y/cBOOeaAR8As4FDge7Ac8BW4OH4V7neeA74EsiteFLtGT3n3HXArcBNwD+BDKBbhcfVphFyzmUD72J/N/tifytvB95zznXy3m9Re0YkCbvWHAicUPXBSNrQOXcE8DIwBvgLcCYwwznX33v/r6Dv7L3XYRMhTwZKgT0rnLsI2AY0S3T96uMBPAjMq3D/SmAzkFHh3GhgGWUTUnXs0oYjgL8DvwQ80Frtudtt2aLsj+bAEGXUppG3Z5+y38kuFc51KTvXR+0ZdXuOAvKqOR+2Dcsu/h9Ued6HwLRQ76khgHJHAPO99z9XOPcekIZ9u5XoNQM2VLh/BPCx976wwrn3sG+2neNYr3rBOfcL4GbgYiw4rUrtGZ0TgGSgXVkX6TLn3GvOub0rlFGbRu47rNfvt865NOdcGjAU6wH8pqyM2rPmImnDI4D3qzzvPaBfqBdWAFCuPbCqyrm1QEnZYxIF59whwCXAkxVOV9fGqyo8JmWcc1nAdOBa7/2yIMXUntHZG/ubNxoYCfwGSAFmOucyy8qoTSPkvd8CDADOAQrKjnOxHpbAxUrtWXORtGGwMiHbWAGAxJxzbj9sXPAR7/2fE12feuox4BO1X0wlYRf867z373rvPwMuBNoCpyW0ZvWQcy4DeBabS9EX6A98AbxRFsBKHacAoNxKoF2Vc62xLsOV8a9O/eSc6w7MAqZ772+p8nB1bdyuwmNS7jjgEufcDufcDmweAMBK59y9gZ9Re0ZjRdntt4ET3vtNwHKgU9kptWnkLgD2AS713v/be//PsnOdsN4VUHvGQiRtGKxMyDZWAFBuLtDDOdexwrmBwHZgXmKqVL8453piF/8Z3vsbqikyFzjKOZde4dxA7A9wXq1XsH45AegFHFx2XF52fgDWOwBqz2jNKbvdL3CibCZ7DuXbhqtNI5eJTfirOD+ltOxc4Nqi9qy5SNpwbtk5qpT5NOQrJ3rmY105sG/6/wP+AfwCOB6bZfl4outWHw5gf2zMaTo27rTzqFCmORaRTgcOwJaqbAZuTHT96/qBXfirrgJQe0bfjq8DX2Pd1T2BGWV/RDPVplG3ZXdsldSTQI+yvwEvApuAjmrPiNuxKxbkTyi7qB9cdqRG2obYZL8dwC1l/y63AsXA4SHfO9Efvi4dWNfVW9hklnXYN620RNerPhzA2LIL1C5HlXIHYutZt2FdsmPQcqBI2neXAEDtuVvt2BTLT7EeW6HyJrCP2nS323Mg8Amwsaw9ZwL91J5RteGsIH87O0fThsAgYAFQBMwHzgz33toMSEREpBHSHAAREZFGSAGAiIhII6QAQEREpBFSACAiItIIKQAQERFphBQAiIiINEIKAETqKefcJc45X+HY6pzLK9vh7hznnNvN1x1Q9noDYlvjkO9Z6bPU0nuMrvAeS2vjPUTqEwUAIvXf2dh2oKcAd2Dpq6cBH5Rt2FKfnIl9ltrwXNlrv1NLry9SrzRJdAVEpMa+9N7/WOH+i865GVia2weBaxNTrd3yhfc+rzZe2Nu2ysucc2tq4/VF6hv1AIg0QN62EX4DGFphr3ucc5nOud855xY554rKbm93zoX8W+CcO8E5945zboVzrsA597Vz7kbnXHKFMm86576o5rldnHOlzrnh0X4O51znsi77S6qc32WYwjl3onPuU+fcJudcvnPuO+fcndG+p0hjoQBApOF6B0gD+gA455oA72E7Cz4KnAw8gw0bPBTmtfbGtiS+DDgVeB7b/+HeCmWeBA52zh1W5bnDgK3AS7v/UUJzzu0N/BVYBJwL/BrbXEX70osEoSEAkYZrSdltTtnt+cCRwDHe+9ll5/5eNldwjHPud9771dW9kPf+qcDPZZMLPwZSgVHOudu896XAu8BC4Args7KyKcClwEve+y2x/HBVHFJWnyu995vLzv2jFt9PpN5TD4BIwxVYBRCYVX8Stu/9p865JoEDeB9IAfoGfSHncpxzTzvnFmO7jRUD9wAtgLYAZUHA08B5zrnmZU89A2hXdr42fVlWp+nOuUHOuba1/H4i9Z4CAJGGa8+y2xVlt22BvbALZcXjs7LH96juRcrmB/wV+BV20f8lcCjl3f/pFYr/AUgGBpfdHw585r3fZW5ALJVNgjwR+5v2IrDSOfdP59wxtfm+IvWZhgBEGq5Tsf3D55XdX4eNkZ8TpHxekPP7YPMIBnvv/xg46Zw7rWpB7/0659wrwBXOufeAY7E5BzVV9W9VdjXvPROY6ZxLA/oD44C3nXOdvfdrY1AHkQZFAYBIA+ScOwubCPeo976g7PS7wFlAvvd+QRQvF1hFUFzh9VOAC4OUfwKYi00w3ARMj+K9gjmgyv2gwxXe++3AP5xz2dhKiC6AAgCRKhQAiNR/BzvnWmOT4DphXfVnAx8At1Yo9xI2Ie/vzrmHga/KnrMPFiycUSFYqGg+NnfgXudcCRYI3BCsMt77f5YtBzwaeDzIa0brcufcz8AXWG/ENWXnT3TOLQFOKHu/d4CfgdbYZ18OfB2D9xdpcBQAiNR/M8putwGrgf8A5wGveu93ptX13hc7504EbsGW5nXBluf9BLyNTe7bhfe+yDl3BjAReAFYDzyLrTKYEqJOvyB2k/8eAQYB9wE/YpML7wOuBD7EgpmTgfuxuQ7rgU+AC733hTGqg0iD4ir8fRARiQnn3Byg1Ht/VITlL8FS9XYFFnvvd5Sd74zNW7jUez+1hnVy2ATFPwDHee871uT1ROo79QCISEyUTb47BDge6AecvhsvE0hpvFsbGYVxO3B32c/LauH1ReoVBQAiEis5wKfARuA+7/1fo3jum9jSwtr0B2wiJAQZ7hBpTDQEICIi0ggpEZCIiEgjpABARESkEVIAICIi0ggpABAREWmEFACIiIg0QgoAREREGqH/B0LOrZNADQ/2AAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -220,7 +222,9 @@
{
"cell_type": "code",
"execution_count": 7,
- "metadata": {},
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"name": "stdout",
@@ -228,16 +232,16 @@
"text": [
"DbAnalysisResultV1\n",
"- name: @Parameters_T2HahnAnalysis\n",
- "- value: [4.78978892e-01 5.02410014e-01 2.01190669e-05] ± [5.08967760e-03 3.07896251e-03 5.78613251e-07]\n",
- "- χ²: 0.5509343873343946\n",
+ "- value: [4.79645080e-01 5.01025155e-01 2.01217480e-05] ± [3.58419367e-03 2.92444090e-03 4.64978519e-07]\n",
+ "- χ²: 0.6109365214541765\n",
"- quality: good\n",
"- extra: <4 items>\n",
"- device_components: ['Q0']\n",
"- verified: False\n",
"DbAnalysisResultV1\n",
"- name: T2\n",
- "- value: 2.0119066897403302e-05 ± 5.786132511852634e-07 s\n",
- "- χ²: 0.5509343873343946\n",
+ "- value: 2.0121748034379643e-05 ± 4.6497851904598897e-07 s\n",
+ "- χ²: 0.6109365214541765\n",
"- quality: good\n",
"- device_components: ['Q0']\n",
"- verified: False\n"
@@ -255,7 +259,7 @@
"metadata": {},
"source": [
"### 3. Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `4` echoes. In addition, we will add frequency to the qubit and see how the result changes due to that (We can see Rabi Oscillations in the `0` echoes case).\n",
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `1` echoes. The analysis should fail for the circuit with `0` echoes. In order to see it, we will add frequency to the qubit and see how it affect the estimated $T_2$. <br>\n",
"Note, that the provided delay time is the for each delay in the circuit and not the total time."
]
},
@@ -274,22 +278,17 @@
" └─────────┘└───────────────┘└──────────┘└╥┘\n",
"c: 1/═════════════════════════════════════════╩═\n",
" 0 \n",
- "The first circuit of hahn echo experiment with 4 echoes:\n",
+ "The first circuit of hahn echo experiment with 1 echoes:\n",
" ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌───────────────┐»\n",
" q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Delay(0.0[s]) ├»\n",
" └─────────┘└───────────────┘└───────┘└───────────────┘└───────────────┘»\n",
"c: 1/═══════════════════════════════════════════════════════════════════════»\n",
" »\n",
- "« ┌───────┐┌───────────────┐┌───────────────┐┌───────┐┌───────────────┐»\n",
- "« q: ┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├»\n",
- "« └───────┘└───────────────┘└───────────────┘└───────┘└───────────────┘»\n",
- "«c: 1/═════════════════════════════════════════════════════════════════════»\n",
- "« »\n",
- "« ┌───────────────┐┌───────┐┌───────────────┐┌──────────┐┌─┐\n",
- "« q: ┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └───────────────┘└───────┘└───────────────┘└──────────┘└╥┘\n",
- "«c: 1/════════════════════════════════════════════════════════╩═\n",
- "« 0 \n"
+ "« ┌───────┐┌───────────────┐┌──────────┐┌─┐\n",
+ "« q: ┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(-π/2) ├┤M├\n",
+ "« └───────┘└───────────────┘└──────────┘└╥┘\n",
+ "«c: 1/═══════════════════════════════════════╩═\n",
+ "« 0 \n"
]
}
],
@@ -314,12 +313,16 @@
"\n",
"# Delays for Hahn Echo Experiment with 4 echoes\n",
"delays3 = np.append(\n",
- " (np.linspace(0.0, 6.375, num=26)).astype(float),\n",
- " (np.linspace(6.625, 12.5, num=25)).astype(float),\n",
+ " (np.linspace(0.0, 25.5, num=26)).astype(float),\n",
+ " (np.linspace(26.5, 50, num=25)).astype(float),\n",
" )\n",
+ "# delays3 = np.append(\n",
+ "# (np.linspace(0.0, 6.375, num=26)).astype(float),\n",
+ "# (np.linspace(6.625, 12.5, num=25)).astype(float),\n",
+ "# )\n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
- "num_echoes = 4\n",
+ "num_echoes = 2\n",
"estimated_t2hahn2 = 20 * conversion_factor\n",
"\n",
"# Create a T2Hahn experiment with 0 echoes\n",
@@ -328,11 +331,11 @@
"print(\"The first circuit of hahn echo experiment with 0 echoes:\")\n",
"print(exp2_0echoes.circuits()[0])\n",
"\n",
- "# Create a T2Hahn experiment with 4 echoes. Print the first circuit as an example\n",
- "exp2_4echoes = T2Hahn(qubit2, delays3, num_echoes=4)\n",
- "exp2_4echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
- "print(\"The first circuit of hahn echo experiment with 4 echoes:\")\n",
- "print(exp2_4echoes.circuits()[0])\n"
+ "# Create a T2Hahn experiment with 1 echoes. Print the first circuit as an example\n",
+ "exp2_1echoes = T2Hahn(qubit2, delays3, num_echoes=num_echoes)\n",
+ "exp2_1echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
+ "print(\"The first circuit of hahn echo experiment with 1 echoes:\")\n",
+ "print(exp2_1echoes.circuits()[0])\n"
]
},
{
@@ -363,12 +366,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Hahn Echo with 4 echoes:\n"
+ "Hahn Echo with 1 echoe:\n"
]
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -395,22 +398,22 @@
"expdata2_0echoes = exp2_0echoes.run(backend=backend2, shots=2000)\n",
"expdata2_0echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
- "# Analysis for Hahn Echo experiemnt with 4 echoes\n",
- "expdata2_4echoes = exp2_4echoes.run(backend=backend2, shots=2000)\n",
- "expdata2_4echoes.block_for_results() # Wait for job/analysis to finish.\n",
+ "# Analysis for Hahn Echo experiemnt with 1 echo\n",
+ "expdata2_1echoes = exp2_1echoes.run(backend=backend2, shots=2000)\n",
+ "expdata2_1echoes.block_for_results() # Wait for job/analysis to finish.\n",
"\n",
"# Display the figure\n",
"print(\"Hahn Echo with 0 echoes:\")\n",
"display(expdata2_0echoes.figure(0))\n",
- "print(\"Hahn Echo with 4 echoes:\")\n",
- "display(expdata2_4echoes.figure(0))"
+ "print(\"Hahn Echo with 1 echoe:\")\n",
+ "display(expdata2_1echoes.figure(0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 30[\\mu s]$, which is close to the estimate of the 4 echoes experiment"
+ "We see that the estimate $T_2$ is different in the two plots. The mock backend for this experiment used $T_{2} = 30[\\mu s]$, which is close to the estimate of the 1 echo experiment."
]
},
{
From 3e9fd4122eaad79e7e129eafbb655bcf1c2b8bc9 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 30 Jan 2022 16:16:19 +0200
Subject: [PATCH 87/93] updated feature text.
---
.../notes/t2-hahn-experiment-84fb05d71b5ef250.yaml | 14 ++++++++++----
1 file changed, 10 insertions(+), 4 deletions(-)
diff --git a/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml b/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
index e3f9cf519d..4cd2545541 100644
--- a/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
+++ b/releasenotes/notes/t2-hahn-experiment-84fb05d71b5ef250.yaml
@@ -1,7 +1,13 @@
---
features:
- |
- Hahn Echo experiment is added to the library.
- This experiment estimates dephasing noise T2.
-
- See experiment class documentation for details.
+ Adds a :class:`~qiskit.qiskit_experiments.library.characterization.T2Hahn`
+ class for composing and running Hahn Echo experiment to estimate T2.
+ - |
+ Adds a :class:`~qiskit.qiskit_experiments.library.characterization.analysis.T2HahnAnalysis`
+ class for analyzing experiment data from :class:`~qiskit.qiskit_experiments.library.characterization.T2Hahn`.
+ - |
+ Adds a :class:`~qiskit.qiskit_experiments.test.T2HahnBackend` class for testing
+ which simulates T2 noise statistics.
+
+
From 968a2f15b32776ba3e16ae67fd6be2276303c3d5 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 30 Jan 2022 16:25:49 +0200
Subject: [PATCH 88/93] black version 22.1.0 pass
---
docs/_ext/autodoc_analysis.py | 6 +-
docs/_ext/custom_styles/utils.py | 3 +-
docs/conf.py | 95 ++++++++++---------
.../curve_analysis/curve_fit.py | 2 +-
.../curve_analysis/data_processing.py | 2 +-
.../curve_analysis/fit_function.py | 14 +--
.../database_service/db_analysis_result.py | 2 +-
.../library/calibration/fine_drag_cal.py | 2 +-
.../analysis/cr_hamiltonian_analysis.py | 2 +-
.../mitigation/mitigation_experiment.py | 2 +-
.../library/quantum_volume/qv_analysis.py | 6 +-
.../interleaved_rb_analysis.py | 2 +-
.../randomized_benchmarking/rb_analysis.py | 4 +-
.../tomography/basis/tomography_basis.py | 6 +-
qiskit_experiments/test/t2hahn_backend.py | 2 +-
.../calibration/experiments/test_fine_drag.py | 2 +-
test/curve_analysis/test_guess.py | 4 +-
test/randomized_benchmarking/test_rb.py | 2 +-
test/test_qubit_spectroscopy.py | 2 +-
test/test_tomography.py | 8 +-
20 files changed, 84 insertions(+), 84 deletions(-)
diff --git a/docs/_ext/autodoc_analysis.py b/docs/_ext/autodoc_analysis.py
index 73c0cac5a9..ded49695c6 100644
--- a/docs/_ext/autodoc_analysis.py
+++ b/docs/_ext/autodoc_analysis.py
@@ -26,14 +26,12 @@ class AnalysisDocumenter(ClassDocumenter):
"""Sphinx extension for the custom documentation of the standard analysis class."""
objtype = "analysis"
- directivetype = 'class'
+ directivetype = "class"
priority = 10 + ClassDocumenter.priority
option_spec = dict(ClassDocumenter.option_spec)
@classmethod
- def can_document_member(
- cls, member: Any, membername: str, isattr: bool, parent: Any
- ) -> bool:
+ def can_document_member(cls, member: Any, membername: str, isattr: bool, parent: Any) -> bool:
return isinstance(member, BaseAnalysis)
def add_content(self, more_content: Any, no_docstring: bool = False) -> None:
diff --git a/docs/_ext/custom_styles/utils.py b/docs/_ext/custom_styles/utils.py
index b25cb3ef3c..f86cd250d7 100644
--- a/docs/_ext/custom_styles/utils.py
+++ b/docs/_ext/custom_styles/utils.py
@@ -161,7 +161,7 @@ def _generate_analysis_ref(
raise Exception(f"Option docstring for analysis_ref is missing.")
analysis_ref_lines = []
- for line in lines[analysis_ref_start + 1:]:
+ for line in lines[analysis_ref_start + 1 :]:
# add lines until hitting to next section
if line.startswith("# section:"):
break
@@ -202,6 +202,7 @@ def _format_default_options(defaults: Dict[str, Any], indent: str = "") -> List[
def _check_no_indent(method: Callable) -> Callable:
"""Check indent of lines and return if this block is correctly indented."""
+
def wraps(self, lines: List[str], *args, **kwargs):
if all(l.startswith(" ") for l in lines):
text_block = "\n".join(lines)
diff --git a/docs/conf.py b/docs/conf.py
index 44ad860300..fe2987e984 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -25,7 +25,8 @@
#
import os
import sys
-sys.path.insert(0, os.path.abspath('.'))
+
+sys.path.insert(0, os.path.abspath("."))
sys.path.append(os.path.abspath("./_ext"))
"""
@@ -33,19 +34,20 @@
"""
import os
+
# Set env flag so that we can doc functions that may otherwise not be loaded
# see for example interactive visualizations in qiskit.visualization.
-os.environ['QISKIT_DOCS'] = 'TRUE'
+os.environ["QISKIT_DOCS"] = "TRUE"
# -- Project information -----------------------------------------------------
-project = 'Qiskit Experiments'
-copyright = '2021, Qiskit Development Team' # pylint: disable=redefined-builtin
-author = 'Qiskit Development Team'
+project = "Qiskit Experiments"
+copyright = "2021, Qiskit Development Team" # pylint: disable=redefined-builtin
+author = "Qiskit Development Team"
# The short X.Y version
-version = '0.3'
+version = "0.3"
# The full version, including alpha/beta/rc tags
-release = '0.3.0'
+release = "0.3.0"
rst_prolog = """
.. raw:: html
@@ -53,7 +55,9 @@
<br><br><br>
.. |version| replace:: {0}
-""".format(release)
+""".format(
+ release
+)
nbsphinx_prolog = """
{% set docname = env.doc2path(env.docname, base=None) %}
@@ -81,32 +85,31 @@
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
- 'sphinx.ext.napoleon',
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.mathjax',
- 'sphinx.ext.viewcode',
- 'sphinx.ext.extlinks',
- 'jupyter_sphinx',
- 'sphinx_autodoc_typehints',
- 'reno.sphinxext',
- 'sphinx_panels',
- 'sphinx.ext.intersphinx',
- 'nbsphinx',
- 'autoref',
- 'autodoc_experiment',
- 'autodoc_analysis',
+ "sphinx.ext.napoleon",
+ "sphinx.ext.autodoc",
+ "sphinx.ext.autosummary",
+ "sphinx.ext.mathjax",
+ "sphinx.ext.viewcode",
+ "sphinx.ext.extlinks",
+ "jupyter_sphinx",
+ "sphinx_autodoc_typehints",
+ "reno.sphinxext",
+ "sphinx_panels",
+ "sphinx.ext.intersphinx",
+ "nbsphinx",
+ "autoref",
+ "autodoc_experiment",
+ "autodoc_analysis",
]
-html_static_path = ['_static']
-templates_path = ['_templates']
-html_css_files = ['style.css', 'custom.css', 'gallery.css']
+html_static_path = ["_static"]
+templates_path = ["_templates"]
+html_css_files = ["style.css", "custom.css", "gallery.css"]
nbsphinx_timeout = 360
-nbsphinx_execute = os.getenv('QISKIT_DOCS_BUILD_TUTORIALS', 'never')
-nbsphinx_widgets_path = ''
-exclude_patterns = ['_build', '**.ipynb_checkpoints']
-nbsphinx_thumbnails = {
-}
+nbsphinx_execute = os.getenv("QISKIT_DOCS_BUILD_TUTORIALS", "never")
+nbsphinx_widgets_path = ""
+exclude_patterns = ["_build", "**.ipynb_checkpoints"]
+nbsphinx_thumbnails = {}
# -----------------------------------------------------------------------------
@@ -120,7 +123,7 @@
# -----------------------------------------------------------------------------
autodoc_default_options = {
- 'inherited-members': None,
+ "inherited-members": None,
}
@@ -131,9 +134,7 @@
# A dictionary mapping 'figure', 'table', 'code-block' and 'section' to
# strings that are used for format of figure numbers. As a special character,
# %s will be replaced to figure number.
-numfig_format = {
- 'table': 'Table %s'
-}
+numfig_format = {"table": "Table %s"}
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
@@ -144,10 +145,10 @@
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', '**.ipynb_checkpoints']
+exclude_patterns = ["_build", "**.ipynb_checkpoints"]
# The name of the Pygments (syntax highlighting) style to use.
-pygments_style = 'colorful'
+pygments_style = "colorful"
# A boolean that decides whether module names are prepended to all object names
# (for object types where a “module” of some kind is defined), e.g. for
@@ -158,7 +159,7 @@
# (e.g., if this is set to ['foo.'], then foo.bar is shown under B, not F).
# This can be handy if you document a project that consists of a single
# package. Works only for the HTML builder currently.
-modindex_common_prefix = ['qiskit_experiments.']
+modindex_common_prefix = ["qiskit_experiments."]
# -- Configuration for extlinks extension ------------------------------------
# Refer to https://www.sphinx-doc.org/en/master/usage/extensions/extlinks.html
@@ -169,20 +170,20 @@
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
-html_theme = 'qiskit_sphinx_theme' # use the theme in subdir 'theme'
+html_theme = "qiskit_sphinx_theme" # use the theme in subdir 'theme'
-#html_sidebars = {'**': ['globaltoc.html']}
-html_last_updated_fmt = '%Y/%m/%d'
+# html_sidebars = {'**': ['globaltoc.html']}
+html_last_updated_fmt = "%Y/%m/%d"
html_theme_options = {
- 'logo_only': True,
- 'display_version': True,
- 'prev_next_buttons_location': 'bottom',
- 'style_external_links': True,
+ "logo_only": True,
+ "display_version": True,
+ "prev_next_buttons_location": "bottom",
+ "style_external_links": True,
}
-autoclass_content = 'both'
-intersphinx_mapping = {'matplotlib': ('https://matplotlib.org/stable/', None)}
+autoclass_content = "both"
+intersphinx_mapping = {"matplotlib": ("https://matplotlib.org/stable/", None)}
# Current scipy hosted docs are missing the object.inv file so leaving this
# commented out until the missing file is added back.
# 'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None)}
diff --git a/qiskit_experiments/curve_analysis/curve_fit.py b/qiskit_experiments/curve_analysis/curve_fit.py
index 26f99476f3..dc597d1028 100644
--- a/qiskit_experiments/curve_analysis/curve_fit.py
+++ b/qiskit_experiments/curve_analysis/curve_fit.py
@@ -140,7 +140,7 @@ def fit_func(x, *params):
yfits = fit_func(xdata, *popt)
residues = (yfits - ydata) ** 2
if sigma is not None:
- residues = residues / (sigma ** 2)
+ residues = residues / (sigma**2)
reduced_chisq = np.sum(residues) / dof
# Compute data range for fit
diff --git a/qiskit_experiments/curve_analysis/data_processing.py b/qiskit_experiments/curve_analysis/data_processing.py
index 33322aadb5..f07e7f059e 100644
--- a/qiskit_experiments/curve_analysis/data_processing.py
+++ b/qiskit_experiments/curve_analysis/data_processing.py
@@ -151,7 +151,7 @@ def mean_xy_data(
# Compute sample mean and sum of variance with weights based on shots
y_means[i] = np.sum(weights * ys)
- y_sigmas[i] = np.sqrt(np.sum(weights ** 2 * ss ** 2))
+ y_sigmas[i] = np.sqrt(np.sum(weights**2 * ss**2))
y_shots[i] = np.sum(ns)
return x_means, y_means, y_sigmas, y_shots
diff --git a/qiskit_experiments/curve_analysis/fit_function.py b/qiskit_experiments/curve_analysis/fit_function.py
index 0a5cff4b78..691ee1e54b 100644
--- a/qiskit_experiments/curve_analysis/fit_function.py
+++ b/qiskit_experiments/curve_analysis/fit_function.py
@@ -74,7 +74,7 @@ def gaussian(
.. math::
y = {\rm amp} \cdot \exp \left( - (x - x0)^2 / 2 \sigma^2 \right) + {\rm baseline}
"""
- return amp * np.exp(-((x - x0) ** 2) / (2 * sigma ** 2)) + baseline
+ return amp * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) + baseline
def cos_decay(
@@ -123,9 +123,9 @@ def bloch_oscillation_x(
where :math:`\omega = \sqrt{p_x^2 + p_y^2 + p_z^2}`. The `p_i` stands for the
measured probability in :math:`i \in \left\{ X, Y, Z \right\}` basis.
"""
- w = np.sqrt(px ** 2 + py ** 2 + pz ** 2)
+ w = np.sqrt(px**2 + py**2 + pz**2)
- return (-pz * px + pz * px * np.cos(w * x) + w * py * np.sin(w * x)) / (w ** 2) + baseline
+ return (-pz * px + pz * px * np.cos(w * x) + w * py * np.sin(w * x)) / (w**2) + baseline
def bloch_oscillation_y(
@@ -140,9 +140,9 @@ def bloch_oscillation_y(
where :math:`\omega = \sqrt{p_x^2 + p_y^2 + p_z^2}`. The `p_i` stands for the
measured probability in :math:`i \in \left\{ X, Y, Z \right\}` basis.
"""
- w = np.sqrt(px ** 2 + py ** 2 + pz ** 2)
+ w = np.sqrt(px**2 + py**2 + pz**2)
- return (pz * py - pz * py * np.cos(w * x) - w * px * np.sin(w * x)) / (w ** 2) + baseline
+ return (pz * py - pz * py * np.cos(w * x) - w * px * np.sin(w * x)) / (w**2) + baseline
def bloch_oscillation_z(
@@ -157,6 +157,6 @@ def bloch_oscillation_z(
where :math:`\omega = \sqrt{p_x^2 + p_y^2 + p_z^2}`. The `p_i` stands for the
measured probability in :math:`i \in \left\{ X, Y, Z \right\}` basis.
"""
- w = np.sqrt(px ** 2 + py ** 2 + pz ** 2)
+ w = np.sqrt(px**2 + py**2 + pz**2)
- return (pz ** 2 + (px ** 2 + py ** 2) * np.cos(w * x)) / (w ** 2) + baseline
+ return (pz**2 + (px**2 + py**2) * np.cos(w * x)) / (w**2) + baseline
diff --git a/qiskit_experiments/database_service/db_analysis_result.py b/qiskit_experiments/database_service/db_analysis_result.py
index 6a5f8286ba..abeae5e983 100644
--- a/qiskit_experiments/database_service/db_analysis_result.py
+++ b/qiskit_experiments/database_service/db_analysis_result.py
@@ -169,7 +169,7 @@ def save(self) -> None:
if db_value is not None:
result_data["value"] = db_value
if isinstance(value.stderr, (int, float)):
- result_data["variance"] = self._display_format(value.stderr ** 2)
+ result_data["variance"] = self._display_format(value.stderr**2)
if isinstance(value.unit, str):
result_data["unit"] = value.unit
else:
diff --git a/qiskit_experiments/library/calibration/fine_drag_cal.py b/qiskit_experiments/library/calibration/fine_drag_cal.py
index 1eefdc2a88..5ce35264a9 100644
--- a/qiskit_experiments/library/calibration/fine_drag_cal.py
+++ b/qiskit_experiments/library/calibration/fine_drag_cal.py
@@ -138,7 +138,7 @@ def update_calibrations(self, experiment_data: ExperimentData):
d_theta = BaseUpdater.get_value(experiment_data, "d_theta", result_index)
# See the documentation in fine_drag.py for the derivation of this rule.
- d_beta = -np.sqrt(np.pi) * d_theta * sigmas[0] / target_angle ** 2
+ d_beta = -np.sqrt(np.pi) * d_theta * sigmas[0] / target_angle**2
old_beta = experiment_data.metadata["cal_param_value"]
new_beta = old_beta + d_beta
diff --git a/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py b/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py
index 919c6a681c..6d4fd713e8 100644
--- a/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/cr_hamiltonian_analysis.py
@@ -336,7 +336,7 @@ def _extra_database_entry(self, fit_data: curve.FitData) -> List[AnalysisResultD
else:
coef_val = 0.5 * (p0_val.value + p1_val.value) / (2 * np.pi)
- coef_err = 0.5 * np.sqrt(p0_val.stderr ** 2 + p1_val.stderr ** 2) / (2 * np.pi)
+ coef_err = 0.5 * np.sqrt(p0_val.stderr**2 + p1_val.stderr**2) / (2 * np.pi)
extra_entries.append(
AnalysisResultData(
diff --git a/qiskit_experiments/library/mitigation/mitigation_experiment.py b/qiskit_experiments/library/mitigation/mitigation_experiment.py
index a5febc3372..3bafa7fc9d 100644
--- a/qiskit_experiments/library/mitigation/mitigation_experiment.py
+++ b/qiskit_experiments/library/mitigation/mitigation_experiment.py
@@ -92,7 +92,7 @@ def analysis(self):
def labels(self) -> List[str]:
"""Returns the labels dictating the generation of the mitigation circuits"""
- return [bin(j)[2:].zfill(self.num_qubits) for j in range(2 ** self.num_qubits)]
+ return [bin(j)[2:].zfill(self.num_qubits) for j in range(2**self.num_qubits)]
class LocalMitigationHelper:
diff --git a/qiskit_experiments/library/quantum_volume/qv_analysis.py b/qiskit_experiments/library/quantum_volume/qv_analysis.py
index f257937a5a..a536c4d8ae 100644
--- a/qiskit_experiments/library/quantum_volume/qv_analysis.py
+++ b/qiskit_experiments/library/quantum_volume/qv_analysis.py
@@ -89,7 +89,7 @@ def _calc_ideal_heavy_output(probabilities_vector, depth):
# Keys are bit strings and values are probabilities of observing those strings
all_output_prob_ideal = {
format_spec.format(b): float(np.real(probabilities_vector[b]))
- for b in range(2 ** depth)
+ for b in range(2**depth)
}
median_probabilities = float(np.real(np.median(probabilities_vector)))
@@ -157,7 +157,7 @@ def _calc_confidence_level(z_value):
float: confidence level in decimal (not percentage).
"""
- confidence_level = 0.5 * (1 + math.erf(z_value / 2 ** 0.5))
+ confidence_level = 0.5 * (1 + math.erf(z_value / 2**0.5))
return confidence_level
@@ -201,7 +201,7 @@ def _calc_quantum_volume(self, heavy_output_prob_exp, depth, trials):
warnings.warn("Must use at least 100 trials to consider Quantum Volume as successful.")
if mean_hop > threshold and trials >= 100:
- quantum_volume = 2 ** depth
+ quantum_volume = 2**depth
success = True
hop_result = AnalysisResultData(
diff --git a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
index 0bdfc48d2e..25170b25ce 100644
--- a/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
+++ b/qiskit_experiments/library/randomized_benchmarking/interleaved_rb_analysis.py
@@ -165,7 +165,7 @@ def _generate_fit_guesses(
def _extra_database_entry(self, fit_data: curve.FitData) -> List[AnalysisResultData]:
"""Calculate EPC."""
- nrb = 2 ** self._num_qubits
+ nrb = 2**self._num_qubits
scale = (nrb - 1) / nrb
alpha = fit_data.fitval("alpha")
diff --git a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
index 4d424e2c6b..9473fed7f0 100644
--- a/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
+++ b/qiskit_experiments/library/randomized_benchmarking/rb_analysis.py
@@ -117,7 +117,7 @@ def _initial_guess(
opt: curve.FitOptions, x_values: np.ndarray, y_values: np.ndarray, num_qubits: int
) -> curve.FitOptions:
"""Create initial guess with experiment data."""
- opt.p0.set_if_empty(b=1 / 2 ** num_qubits)
+ opt.p0.set_if_empty(b=1 / 2**num_qubits)
# Use the first two points to guess the decay param
dcliff = x_values[1] - x_values[0]
@@ -169,7 +169,7 @@ def _extra_database_entry(self, fit_data: curve.FitData) -> List[AnalysisResultD
# Calculate EPC
alpha = fit_data.fitval("alpha")
- scale = (2 ** self._num_qubits - 1) / (2 ** self._num_qubits)
+ scale = (2**self._num_qubits - 1) / (2**self._num_qubits)
epc = FitVal(value=scale * (1 - alpha.value), stderr=scale * alpha.stderr)
extra_entries.append(
AnalysisResultData(
diff --git a/qiskit_experiments/library/tomography/basis/tomography_basis.py b/qiskit_experiments/library/tomography/basis/tomography_basis.py
index f346999393..32bbfca5bf 100644
--- a/qiskit_experiments/library/tomography/basis/tomography_basis.py
+++ b/qiskit_experiments/library/tomography/basis/tomography_basis.py
@@ -79,8 +79,8 @@ def _instruction_povms(instructions: List[Instruction]) -> List[Dict[int, np.nda
for inst in instructions:
inst_inv = inst.inverse()
basis_dict = {
- i: DensityMatrix.from_int(i, 2 ** inst.num_qubits).evolve(inst_inv).data
- for i in range(2 ** inst.num_qubits)
+ i: DensityMatrix.from_int(i, 2**inst.num_qubits).evolve(inst_inv).data
+ for i in range(2**inst.num_qubits)
}
basis.append(basis_dict)
return basis
@@ -90,7 +90,7 @@ def _instruction_states(instructions: List[Instruction]) -> List[np.ndarray]:
"""Construct preparation density matrices from instructions"""
states = []
num_qubits = instructions[0].num_qubits
- init = DensityMatrix.from_int(0, 2 ** num_qubits)
+ init = DensityMatrix.from_int(0, 2**num_qubits)
for inst in instructions:
states.append(init.evolve(inst).data)
return states
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index ba2d47dd03..400ba5f94e 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -263,7 +263,7 @@ def _measurement_gate(self, qubit_state: dict) -> int:
# measuring output. First, we calculate the probability and later we are
# tossing to see if the event did happen.
z_projection = np.cos(qubit_state["Theta"])
- probability = z_projection ** 2
+ probability = z_projection**2
if self._rng.random() > probability:
meas_res = self._rng.random() < 0.5
else:
diff --git a/test/calibration/experiments/test_fine_drag.py b/test/calibration/experiments/test_fine_drag.py
index b552c022dc..33e21ef6b5 100644
--- a/test/calibration/experiments/test_fine_drag.py
+++ b/test/calibration/experiments/test_fine_drag.py
@@ -133,7 +133,7 @@ def test_update_cals(self):
d_theta = exp_data.analysis_results(1).value.value
sigma = 40
target_angle = np.pi
- new_beta = -np.sqrt(np.pi) * d_theta * sigma / target_angle ** 2
+ new_beta = -np.sqrt(np.pi) * d_theta * sigma / target_angle**2
transpile_opts = copy.copy(drag_cal.transpile_options.__dict__)
transpile_opts["initial_layout"] = list(drag_cal.physical_qubits)
diff --git a/test/curve_analysis/test_guess.py b/test/curve_analysis/test_guess.py
index 1692dde7cd..61e44067ef 100644
--- a/test/curve_analysis/test_guess.py
+++ b/test/curve_analysis/test_guess.py
@@ -134,7 +134,7 @@ def test_linewidth_spect(self, idx, a, fwhm):
"""Test of linewidth of peaks."""
x = np.linspace(-1, 1, 100)
sigma = fwhm / np.sqrt(8 * np.log(2))
- y = a * np.exp(-((x - x[idx]) ** 2) / (2 * sigma ** 2))
+ y = a * np.exp(-((x - x[idx]) ** 2) / (2 * sigma**2))
lw_guess = guess.full_width_half_max(x, y, idx)
@@ -153,7 +153,7 @@ def test_baseline_spect(self, b0, x0, a, fwhm):
"""Test of baseline of peaks."""
x = np.linspace(-1, 1, 100)
sigma = fwhm / np.sqrt(8 * np.log(2))
- y = a * np.exp(-((x - x0) ** 2) / (2 * sigma ** 2)) + b0
+ y = a * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) + b0
b0_guess = guess.constant_spectral_offset(y)
diff --git a/test/randomized_benchmarking/test_rb.py b/test/randomized_benchmarking/test_rb.py
index 6f7f15374f..d46718118e 100644
--- a/test/randomized_benchmarking/test_rb.py
+++ b/test/randomized_benchmarking/test_rb.py
@@ -76,7 +76,7 @@ def is_identity(self, circuits: list):
circ.remove_final_measurements()
# Checking if the matrix representation is the identity matrix
self.assertTrue(
- matrix_equal(Clifford(circ).to_matrix(), np.identity(2 ** num_qubits)),
+ matrix_equal(Clifford(circ).to_matrix(), np.identity(2**num_qubits)),
"Clifford sequence doesn't result in the identity matrix.",
)
diff --git a/test/test_qubit_spectroscopy.py b/test/test_qubit_spectroscopy.py
index 514f47258e..61a84a3ae4 100644
--- a/test/test_qubit_spectroscopy.py
+++ b/test/test_qubit_spectroscopy.py
@@ -47,7 +47,7 @@ def _compute_probability(self, circuit: QuantumCircuit) -> float:
"""Returns the probability based on the frequency."""
freq_shift = next(iter(circuit.calibrations["Spec"]))[1][0]
delta_freq = freq_shift - self._freq_offset
- return np.exp(-(delta_freq ** 2) / (2 * self._linewidth ** 2))
+ return np.exp(-(delta_freq**2) / (2 * self._linewidth**2))
class TestQubitSpectroscopy(QiskitExperimentsTestCase):
diff --git a/test/test_tomography.py b/test/test_tomography.py
index 6e7929d94e..11d2d05ebb 100644
--- a/test/test_tomography.py
+++ b/test/test_tomography.py
@@ -47,7 +47,7 @@ def test_full_qst(self, num_qubits, fitter):
backend = AerSimulator(seed_simulator=9000)
seed = 1234
f_threshold = 0.95
- target = qi.random_statevector(2 ** num_qubits, seed=seed)
+ target = qi.random_statevector(2**num_qubits, seed=seed)
qstexp = StateTomography(target)
if fitter:
qstexp.analysis.set_options(fitter=fitter)
@@ -128,7 +128,7 @@ def test_exp_circuits_measurement_qubits(self, meas_qubits):
tomo_circuits = exp.circuits()
# Check correct number of circuits are generated
- self.assertEqual(len(tomo_circuits), 3 ** num_meas)
+ self.assertEqual(len(tomo_circuits), 3**num_meas)
# Check circuit metadata is correct
for circ in tomo_circuits:
@@ -298,7 +298,7 @@ def test_full_qpt(self, num_qubits, fitter):
backend = AerSimulator(seed_simulator=9000)
seed = 1234
f_threshold = 0.94
- target = qi.random_unitary(2 ** num_qubits, seed=seed)
+ target = qi.random_unitary(2**num_qubits, seed=seed)
qstexp = ProcessTomography(target)
if fitter:
qstexp.analysis.set_options(fitter=fitter)
@@ -335,7 +335,7 @@ def test_exp_measurement_preparation_qubits(self, qubits):
tomo_circuits = exp.circuits()
# Check correct number of circuits are generated
- size = 3 ** num_meas * 4 ** num_meas
+ size = 3**num_meas * 4**num_meas
self.assertEqual(len(tomo_circuits), size)
# Check circuit metadata is correct
From 29c67e6c21f3ffd3b81f0a1b654f47da5a6a2b18 Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Sun, 30 Jan 2022 16:43:10 +0200
Subject: [PATCH 89/93] fixed angles to be of absolute value and not previous
ones.
---
qiskit_experiments/test/t2hahn_backend.py | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 400ba5f94e..925270b54d 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -193,7 +193,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
elif isclose(angle, np.pi / 2):
- new_theta = angle - qubit_state["Theta"]
+ new_theta = (np.pi / 2) - qubit_state["Theta"]
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plane": False,
@@ -201,7 +201,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
"Theta": new_theta,
}
elif isclose(angle, -np.pi / 2):
- new_theta = np.abs(angle - qubit_state["Theta"])
+ new_theta = np.abs((-np.pi / 2) - qubit_state["Theta"])
new_theta = new_theta % (2 * np.pi)
new_qubit_state = {
"XY plane": False,
@@ -210,7 +210,7 @@ def _rx_gate(self, qubit_state: dict, angle: float) -> dict:
}
else:
raise QiskitError(
- f"Error - the angle {angle} isn't supported. We only support multiplication of pi/2"
+ f"Error - the angle {angle} isn't supported. We only support multiplications of pi/2"
)
else:
if isclose(angle, np.pi):
From 1597f1e2d77ea7a02dc722b916a814143866effd Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 31 Jan 2022 10:00:05 +0200
Subject: [PATCH 90/93] updated text in the tutorial.
---
docs/tutorials/t2hahn_characterization.ipynb | 25 ++++++++------------
1 file changed, 10 insertions(+), 15 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 11df19de17..5dcf62c61a 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -18,7 +18,7 @@
"<br>Hahn Echo experiment and CPMG sequence are experiments to estimate $T_2$ which are robust to the <I>detuning frequency</I>.\n",
"The decay in amplitude causes the probability function to take the following form:<br>\n",
"$$f(t) = A \\cdot e^{-\\frac{t}{T_2}}+ B$$\n",
- "The difference between Hahn Echo and CPMG sequence is that in Hahn Echo experiment, there is only one echo sequences while in CPMG there are multiple echo sequences."
+ "The difference between Hahn Echo and CPMG sequence is that in Hahn Echo experiment, there is only one echo sequence while in CPMG there are multiple echo sequences."
]
},
{
@@ -28,7 +28,7 @@
"## 1. Decoherence Time\n",
"Decoherence time is the time taken for off-diagonal components of the density matrix to fall to approximately 37% ($\\frac{1}{e}$). For $t\\gg T_2$, the qubit statistics behave like a random bit. It gets the value of `0` with probability of $p$ and the value of `1` with probability of $1-p$.\n",
"\n",
- "Since the qubit is exposed to other types of noise (like <a href=\"./t1.ipynb\"> T<sub>1</sub></a>), we are using a $Rx(\\pi)$ pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation."
+ "Since the qubit is exposed to other types of noise (like <a href=\"./t1.ipynb\"> T<sub>1</sub></a>), we are using $Rx(\\pi)$ pulses for decoupling and to solve our inaccuracy for the qubit frequency estimation."
]
},
{
@@ -189,7 +189,7 @@
"metadata": {},
"source": [
"### 2. Providing initial user estimates\n",
- "The user can provide initial estimates for the parameters to help the analysis process. In the initial guess, the keys `{amp, tau, base}` corresponding to the parameters `{A, T_2, B}` respectively.<br>\n",
+ "The user can provide initial estimates for the parameters to help the analysis process. In the initial guess, the keys `{amp, tau, base}` correspond to the parameters `{A, T_2, B}` respectively.<br>\n",
"Because the curve is expected to decay toward $0.5$, the natural choice for parameter $B$ is $0.5$. When there is no $T_2$ error, we would expect that the probability to measure `1` is $100\\%$, therefore we will guess that A is $0.5$. In this experiment, `t2hahn` is the parameter of interest. Good estimate for it is the value computed in previous experiments on this qubit or a similar value computed for other qubits."
]
},
@@ -279,16 +279,11 @@
"c: 1/═════════════════════════════════════════╩═\n",
" 0 \n",
"The first circuit of hahn echo experiment with 1 echoes:\n",
- " ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌───────────────┐»\n",
- " q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Delay(0.0[s]) ├»\n",
- " └─────────┘└───────────────┘└───────┘└───────────────┘└───────────────┘»\n",
- "c: 1/═══════════════════════════════════════════════════════════════════════»\n",
- " »\n",
- "« ┌───────┐┌───────────────┐┌──────────┐┌─┐\n",
- "« q: ┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(-π/2) ├┤M├\n",
- "« └───────┘└───────────────┘└──────────┘└╥┘\n",
- "«c: 1/═══════════════════════════════════════╩═\n",
- "« 0 \n"
+ " ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌─────────┐┌─┐\n",
+ " q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(π/2) ├┤M├\n",
+ " └─────────┘└───────────────┘└───────┘└───────────────┘└─────────┘└╥┘\n",
+ "c: 1/══════════════════════════════════════════════════════════════════╩═\n",
+ " 0 \n"
]
}
],
@@ -322,7 +317,7 @@
"# )\n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
- "num_echoes = 2\n",
+ "num_echoes = 1\n",
"estimated_t2hahn2 = 20 * conversion_factor\n",
"\n",
"# Create a T2Hahn experiment with 0 echoes\n",
@@ -371,7 +366,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
From b12831bd7e33af1fa0c22664a3d17af5b6443d9b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 31 Jan 2022 10:56:05 +0200
Subject: [PATCH 91/93] changed doc string year to 2022
---
.../library/characterization/analysis/t2hahn_analysis.py | 2 +-
qiskit_experiments/library/characterization/t2hahn.py | 2 +-
qiskit_experiments/test/t2hahn_backend.py | 2 +-
3 files changed, 3 insertions(+), 3 deletions(-)
diff --git a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
index 057c9b84f3..771f9ba6bc 100644
--- a/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
+++ b/qiskit_experiments/library/characterization/analysis/t2hahn_analysis.py
@@ -1,6 +1,6 @@
# This code is part of Qiskit.
#
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
diff --git a/qiskit_experiments/library/characterization/t2hahn.py b/qiskit_experiments/library/characterization/t2hahn.py
index 05adaeb5fc..84e6c28243 100644
--- a/qiskit_experiments/library/characterization/t2hahn.py
+++ b/qiskit_experiments/library/characterization/t2hahn.py
@@ -1,6 +1,6 @@
# This code is part of Qiskit.
#
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
diff --git a/qiskit_experiments/test/t2hahn_backend.py b/qiskit_experiments/test/t2hahn_backend.py
index 925270b54d..8cbe72ecfe 100644
--- a/qiskit_experiments/test/t2hahn_backend.py
+++ b/qiskit_experiments/test/t2hahn_backend.py
@@ -1,6 +1,6 @@
# This code is part of Qiskit.
#
-# (C) Copyright IBM 2021.
+# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
From d95b3f993db6f3643d29549827d586b0f88e7ffc Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 31 Jan 2022 10:57:26 +0200
Subject: [PATCH 92/93] updated tutorial text
---
docs/tutorials/t2hahn_characterization.ipynb | 17 ++++++-----------
1 file changed, 6 insertions(+), 11 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index 5dcf62c61a..c03428b367 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -260,7 +260,7 @@
"source": [
"### 3. Number of echoes\n",
"The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `1` echoes. The analysis should fail for the circuit with `0` echoes. In order to see it, we will add frequency to the qubit and see how it affect the estimated $T_2$. <br>\n",
- "Note, that the provided delay time is the for each delay in the circuit and not the total time."
+ "The list `delays` is the times provided to each delay gate, not the total delay time."
]
},
{
@@ -306,19 +306,15 @@
"\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"\n",
- "# Delays for Hahn Echo Experiment with 4 echoes\n",
+ "# Delays for Hahn Echo Experiment with 1 echoes\n",
"delays3 = np.append(\n",
" (np.linspace(0.0, 25.5, num=26)).astype(float),\n",
" (np.linspace(26.5, 50, num=25)).astype(float),\n",
- " )\n",
- "# delays3 = np.append(\n",
- "# (np.linspace(0.0, 6.375, num=26)).astype(float),\n",
- "# (np.linspace(6.625, 12.5, num=25)).astype(float),\n",
- "# )\n",
+ " ) \n",
"delays3 = [float(_) * conversion_factor for _ in delays3]\n",
"\n",
"num_echoes = 1\n",
- "estimated_t2hahn2 = 20 * conversion_factor\n",
+ "estimated_t2hahn2 = 30 * conversion_factor\n",
"\n",
"# Create a T2Hahn experiment with 0 echoes\n",
"exp2_0echoes = T2Hahn(qubit2, delays2, num_echoes=0)\n",
@@ -349,7 +345,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -366,7 +362,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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7du248cYbGTduXEjXqc/3MFxCzb0f7r/PtVW4/yaKyArnXLn9cdanXwkJCdCoEYwdq69nzIADB2DHDp3CZ4wxxtREFvQrqWlT6NVLt99+A29a6+zsai2WMcYY45MN5KukhARISdHa/sCB8NRT+nrPHjjySBg0yJr6jakKZUfqG2OCZzX9EDRtCp07Q8eOuvTugw9qU/+4cZCRoX39dXjIhDHGmFrGavohiI+HOXNg82Z9ffCg/vQm6/EulnXPPZEvmzHGGFOW1fRDkJ2tffmeXCOHyMuDKVO0z98YY4ypbhb0Q/DKK+Aj10mR6Gh4+eXIlMcYY4zxx4J+CLKyAufdz8vT44wx4bVp0yZ69+5N586d6dq1Ky/b07UxAVmffghatNBFePytJZGYqMcZY8IrJiaGadOmcdxxx5GVlUW3bt0455xzgl7Bzpj6yGr6IRg4MHAynoICihboMaa+GjJkSFHO/XBp2bJlUZ74Fi1a0KxZM3bu3BnWzzCmrrGgH4LUVJ2e52tp8NhYuPpqy9Jn6r4hQ4YgIods3kVcpk+fzj/+8Q9A16q/8cYbw/r5K1asoKCggMMOOyys1w3kk08+4fzzz6dVq1aICAsWLAh4zv3330+PHj1o1KgRaWlpDBgwgG+//bbUMQUFBdx55520b9+ehIQE2rdvz/jx4znomSLUrl27cu/3ueeeWxVf09QhFvRDNGkSjBmjyXoaNNCleOPi9L3oaBg+XEf5l1g905g6qW/fvmRmZpbaunTpAkBKSkqVLeu6c+dOLr/88qKV+iIpJyeHLl26MH36dBITE4M6Z/HixQwfPpylS5fy0UcfERMTQ9++fUu1Ujz44IM88cQTPPbYY3z//fdMnz6dJ554gvvvvx+A5cuXl7rP//3vfxER/vznP1fJ9zR1h/Xph0hE5+GPGaOj+dev1/n7b70FX34Jjz8Ot9wC27ZBmzZ6vDF1UXx8vM/lZ4cMGcKOHTto1qwZH3/8MR9//DFPPPEEAOvWrSt3/fhLLrmEDz74gDvvvJNRo0YBsGbNGrp168bTTz/NoEGD2LdvHxdeeCG33XYbJ510UlV9NZ/OOecczjnnHEC/YzDee++9Uq+fffZZUlJSWLJkCQMGDABg6dKlDBgwoOh1u3btOP/88/niiy8AXSa5pHnz5tGoUSML+iYgq+mHSWoqDB2qDwCXXabZ+ACefhoyMyE/X1P0GlOfTZ8+nV69enHllVcW1VJ9NclPmzataM15gH379vGXv/yFgQMHMmjQIJxzDBkyhDPOOIPBgwcH/OzJkyeTnJxctLVs2bLU6+TkZD799NOwft9g7Nmzh8LCQlJL5O0+5ZRTWLRoEd9//z0Aq1ev5qOPPip6wCjJOce8efO47LLLgm5tMPWX1fTDLCoK0tNh/37405/g1Vfhvvs0ic8vv2gXQKC5/cbURu+++y7JyclFr0899VTeeeedUsekpKQQFxdHUlKSz1YBr5YtWzJ27Fgef/xxNmzYwLRp09i9e3dRC8GSJUt46aWX6Nq1K2+88QagteZjjjmm3Otdd911pWrCOTk5pcoL0KpVq6C/b7iMHDmS4447jl69ehXtu/XWW9mzZw+dO3cmOjqagwcPcscddzB8+PBDzn///fdZt24dQ4cOjWSxTS1lQb8KJCXpNnYsvP22bl98Acceq/37zZpVdwmNCb/TTjutVL96OGqd7dq1o3Hjxjz00EPMmTOHTz75pGj99lNOOYXCwsKgr9WkSROaNGlS9DqUteDDZcyYMXz22Wd89tlnRJeoDbz00ks888wzPP/88xx99NGsWrWKkSNH0r59e66++upS15g7dy49evTg2GOPjXTxTS1kzftVQATS0rTJ3ztIeeJE7ev/9VdtBTCmrklKSuLwww8v2sJVaz722GOZOXMm48ePL1Ubrqia1rw/evRoXnjhBT766CM6dOhQ6r2bb76ZcePGMWjQII455hgGDx7MmDFjigbyef3yyy/861//slq+CZrV9KtIYiI0bAhXXAHPPw+rV8Po0TqYr2VL7f+3pXdNfRQXF0dBBeaxOuc4+uijGT9+fEifW5Oa90eOHMlLL73EokWLOPLIIw95Py8vr1TNHyA6OvqQlo0FCxYQHx/PX/7ylyotr6k7LOhXobQ02L0bunaFLVvgtdd0f2Ki1vzHjdMpfzai39Qn7dq148svv2T9+vUkJyfTpEkToqLKb3R84okn+OSTT+jUqdMhQbCiqqJ5Pycnhx9//BGAwsJCNm7cyKpVq2jSpAlt2rQB4PHHH+fxxx8vGpR3ww038Oyzz/LGG2+QmppKlidPt7e1AWDAgAE88MADtG/fnqOPPpqVK1cydepULr/88qLPds7x1FNPMWjQoEMeXozxxZr3q1BcHMydC4sWld6fn6/z9qdOhQkTqqdsxlSXcePGERcXR+fOnUlLS2Pjxo3lHrd69WpuvvlmbrjhBn744QfyAi10UQ2++uorjj/+eI4//njy8/OZOHEixx9/PBNK/I+9Y8cO1q5dW/R65syZ7Nmzhz/84Q+0bNmyaJsyZUrRMTNmzGDgwIEMHz6co446irFjxzJ06FDuu+++omMWL17MDz/8YE37pkLEOVfdZagy3bt3d1999VVYr7l48WJ69+4d1LHZ2ZCR4T8xT0KCTumrorwlNVJF7mF91r17d3z9+60Jg9Cq0r59+zjxxBPp3LkzTz31FA0bNmTJkiX07NkzbJ9R1+9hJIRyD/39+65vwv03UURWOOe6l/ee1fSrUDBL70ZF2dK7xpR12223sWvXLmbNmkVSUhJHHHEE06dP99kqYIwJjgX9KhTM0rv5+VrTN8aohQsX8vjjj/OPf/yDlJQUAO644w4++ugjrrjiimounTG1mw3kq0LBLL2bkGCj+I0p6cwzz+TAgQOl9g0ePDiorHvGGP+spl+Fgll6t7AQTj7ZVuIzxhhT9SzoV6FAS+8C/PWvOp//118jVy5jjDH1U7UEfREZLiLrRGSviKwQkVMDHH+DiKwRkXwRWSsil/s7viYpb+ndxEQdwAfan5+UBDt32vK7xhhjqlbE+/RF5BJgOjAc+Mzz8x0R6eycO2RorohcDzwIDAW+AH4PzBWRbOfcm5EreeWUXXp361Yd0X/SSTBgALz7LixerK/XrtUc/du26XiAgQOtv98YY0z4VMdAvjHAAufcXM/rm0SkP3A9cHs5xw8G5jrnXvC8/llEegC3AjU+6Ht5l94Fnb+/fbs+CNx7L9xxB5x3Hjz1lD4Q5Odr7X/ECMvaZ4wxJnwi2rwvInFAN2BhmbcWAif5OC0eKNvwnQ/8XkRiw1vCyEhJgZgYzcvfqRNs2ABPPgn79ukUP+d0xL9l7TPGGBNOke7TbwZEA9vK7N8G+Fpc+z3gKhHpIao7cA0Q67lerRMVpc33Bw9qLR/09/Lk5cGUKfDbbxErnjHGmDqqNszTvwd9IFgKCPqA8HfgFuCQxbRFZBgwDCA9PZ3FixeHtTA5OTlhu+aBAxAbC7//fSe+/LIlRxyxk2HDvjmkKT8qCj74AJrVykecQ4XzHtZliYmJdOnSpdz3nHOI9fmExO5h6EK5h6mpqfZ3wCOifxOdcxHbgDjgIHBxmf1PAB8HODcWaI22FFwP7Aai/J3TrVs3F26LFi0K27X27XNuxAjntEHf9ybi3KRJYfvYahfOe1hf2T0Mnd3D0Nk9DI9w30fgK+cjLka0ed85tx9YAfQr81Y/tCbv79wDzrnNzrkCYBDwH+fcITX92iQuDtq10yl8/iQlaXeAMcYYE4rqmKc/FRgiIteIyFEiMh3IAGYDiMgzIvKM92AR+Z2IDBaRI0Tk9yLyItAF+Fs1lD3sLr9cs/L5U1AAF18cmfIYY4ypuyIe9J1zLwGjgPHAKuAU4Bzn3AbPIW08m1c0Os3va+B9IAE4yTm3PjIlrlpNm8KoUb5r+4mJMHZs/Vp61xhjTNWoloF8zrmZwEwf7/Uu83oNcHwEilVt7r9fp+fNnq21+oMHdV5+bCwMGaLz9Y0xxphQWe79GkAEHnoIPv0U7rpLE/k4B9deq1P6du7UhD3GGGNMKCzo1xBxcdCxo/bdz5mj+558En76SfP2Z2YG7vs3xhhj/LGgX4M0bqyZ+nr0gEGDYP9+uOUW3XfwoNb4jTHGmMqyoF+DeDP15efD+PGajOeLL+D553WFvh07rJnfGGNM5VnQr2GSkjQ3f3y8LrQDukrf1q3WzG+MMSY0FvRroLQ0Hch37rnQvz/k5MBtt+lofmvmN8YYU1kW9GugmBhIT9em/MmTteb/0Ufw6qvWzG+MMabyLOjXUA0bamKelBSdxgcwcSJs3677MzN1Tr8xxhgTLAv6NZSI1vYPHICBA6FPH11ed/BgmDFDB/f9+GN1l9IYY0xtYkG/BouP1zS9ubm6MA/At9/CI49oFr9jj9W+fl2E0BhjjPHPgn4N16SJ1uxffLH0/rw82LcPHntMp/cZY4wxgVjQr+F27YJ583wP3MvP15r/b79FtFjGGGNqIQv6Ndwrr0B0tP9joqLgmWf8H2OMMcZY0K/hsrK0Kd+fvXs1R//+/ZEpkzHGmNqpWpbWNcFr0UKz9OXm+j4mIUFH+q9dC8uWwbZtet7AgbpinzHGGAMW9Gu8gQNhxAj/x+zfD5s26UI9Bw5omt4GDfS8ceM0na9IZMprjDGm5rLm/RouNVUDd1KS72Ocg/nzdTS/Ny9/bq42+0+dChMmRKasxhhjajYL+rXApEkwZow24zdooLX2xESdx3/44Rro9+0r/9y8PJgyxUb3G2OMsaBfK4gUr7T36KNw9936eulSzdAXqOk+OhpefjkyZTXGGFNzWZ9+LZKaCkOH6u8FBbB+vc7jD5SRLy9PZwEYY4yp36ymX0tFR+sI/ZQU//39oO+3aBGZchljjKm5LOjXYg0awCWXBF5tr6AALr44MmUyxhhTc1nQr+UOPxyuuUYH9pUnMRGuvx4aN45osYwxxtRAFvRruehoeOghuOIKHc0fG1v8XlwcDBum4wD27Km+MhpjjKkZLOjXAUlJOpr/00/h3nvhiCN0/1FHwejRkJwMmZmWptcYY+o7C/p1RJMmkJamffevvw4tW8LXX2tynuhoiInRKX/e5D3GGGPqHwv6dURUlAb6/ft1RP/jj+u+GTNgyRJN7LN9u87zv+cemDsXsrOru9TGGGMiyebp1yHx8dC8OfzyC/TsCSNHapC/6SY4/3xdftdy8xtjTP1lNf06pnFj7ePPz4dRo+DEE3XVvXnzLDe/McbUdxb06xgRTcTjDe73368/ffXlW25+Y4ypPyzo10GxsZCRoQF9+XJt9vfHcvMbY0z9YEG/jmrQQEf0b9kSeKqe5eY3xpj6wYJ+HdasmTb1JyT4Py4x0XLzG2NMfWBBvw6LioIrrww8N7+gAC68MCJFMsYYU40s6NdxLVrolD1/ufmvuUZH81viHmOMqdss6NcDDz0E116rA/qiyvwXv+QSuP12nb+/bRs4Vz1lNMYYU/UsOU89IKLT8i6/HN59F379VX9+952O7t+7Vwf+7dqlv3/wgQ7sa9ECBg6E1NTq/gbGGGPCwYJ+PREdrQvwJCVpgL/mGjj7bA38f/sbPPIIzJ6tm2XtM8aYusmCfj2SkKC198xMaNRI8+8PGAD//Kem7v3iC83a55Wbqz+nTtWf99wT+TIbY4wJH+vTr2caNdIFefLyoHNnbfYHWLxYU/eWx7L2GWNM3VAtQV9EhovIOhHZKyIrROTUAMf/VURWiUieiGSJyD9ExGaWV4KILsoTFaW1+j/+EU47LfB5lrXPGGNqv4gHfRG5BJgOTAaOB5YC74hIGx/Hnww8C/wdOBq4EOgMPBeJ8tZF0dHQqpX23RcUQPfugc+xrH3GGFP7VUdNfwywwDk31zm3xjl3E5AJXO/j+F7AZufco865dc65z4EZwIkRKm+dFB+v/fu5uZCe7nsev1dSkmXtM8aY2i6iQV9E4oBuwMIyby0ETvJx2hKgpYgMENUMGAS8XXUlrR8aNdL8/GecEVzWvosvjky5jDHGVA1xEczGIiIZwBbgdOfcJyX2TwAudc518nHeRcACIBGdcfA+cIFz7pChZyIyDBgGkJ6e3u3FF18M63fIyckhOTk5rNesbvv3a2KeHTtg5co0nn32aEQcV131fxx11E6ioqBpUzjssPBM26uL9zDS7B6Gzu5h6Owehke472OfPn1WOOfK77h1zkVsAzIAB5xWZv8EYK2PczqjDwo3A12Bs4BvgGcCfV63bt1cuC1atCjs16xu+/c797//OXfTTc7Fxzsn4pzm5nMuNta5kSOd+/ln5378UY8NVV28h5Fm9zB0dg9DZ/cwPMJ9H4GvnI+4GOk+/R1AAZBeZn864GuY2O3Al865h51z3zjn3gOGA4NFpHXVFbX+iI2F1q1h+HD46it44AHo5GlzadFCE/nEx+vrrVu1qd8YY0ztE9Gg75zbD6wA+pV5qx86ir88SeiDQkne15ZnIEwSE6FlS4iJgcsug7fegq5dYdMmGDZMuwASE/VnVpbl6DfGmNqoOoLmVGCIiFwjIkeJyHS02X82gIg8IyLPlDj+TeACEbleRDp4pvA9BvzXObcx4qWvw1JSNM9+To4G+Kef1pH9y5bBbbdpoD9wAE45Bdq21Yx+2dnVXWpjjDHBinjQd869BIwCxgOrgFOAc5xzGzyHtPFs3uMXoNP8bgS+BV4B/gdcEKky1ydpadqUn5+vNf/58zV970sv6ej9E06AjRu1BWDUKMjIgDvvtJq/McbUBtWSe985NxOY6eO93uXsm4HOzTdVLCpKA/nGjVqrP/ZYeOIJuPpqrfGXlJenPy03vzHG1A7WJ24OERurGfv27tVBez17ahY/Xyw3vzHG1A4W9E25EhK0xp+TA//5D8TF+T8+Kspy8xtjTE1nQd/41LCh9vFv2aK1fn/y8/U4Y4wxNZcFfeOXNxNfQoL/4xIStFvgwIHIlMsYY0zFWdA3fonAlVcGzs1fWAjnnAObN8PBg5EpmzHGmIqxoG8CatoUxo71vRJfdDRce63O6S8s1GZ+y9pnjDE1jwV9E5R779V5+fHxGvxFilPzFhQUL8Szbx/07g3t2sGTT1ryHmOMqUmqZZ6+qX1EYPJkzc//9NOwe7fW7JOSYMQImD4dVq2Czz/Xfv3CQhgzRh8Uxo2DSZPCs0KfMcaYyrOgbyqkdWsYORIyM3V0v4jm4x8zBj7+uPSxlrzHGGNqFmveNxWWkgLNmukcfoCzzrLkPcYYUxtUKOiLSE8RuUtE3hWRb0TkBxFZJiILRORKEUmtqoKamqVpUw3+OTm6Ip+3f9+XqCj45z/194ICXbq3fXtbtMcYYyIpqKAvIleIyP+hy9+ORpe7/QH4AsgGTgSeArZ4HgDaV1F5TQ0hAs2ba5/+li2anMef/Hz46SddnOfrr+HHH2H9ehg92hbtMcaYSAnYpy8i3wBpwDPA5cAq5w798ywiKcB5wKXAahEZ4llRz9RRUVG6El96uibn8Rf4ExN1kN/y5dq37533n5urP63f3xhjql4wNf15QHvn3K3OuZXlBXwA59wu59xzzrlzgJ7Ab2Esp6mhoqN1Bb5AyXsOHoQvvvD9YGD9/sYYU/UCBn3n3HTnXIDM64ec87Vz7r3KF8vUJs2b6+h9X8l7RKBHD/+D/UDft0V7jDGm6tjofRMW992nU/ni47XZH4qT+DgHq1cXT+HzJS8PsrKqvqzGGFNfBT1PX0QuBC4AOgNNPLt3AquBfznn3gh34UztIQL33w833QTz5sGuXdrXf+qpMHQofPtt8QOAL0lJ0KJF5MpsjDH1TTAD+VKBN4GTgI3Ad8D/PG83AXoDV4jIMuA855xNwKrHMjJ0RP7mzZCcrLX+F16AP/0J/uf5V7NnT2y55xYUwMUXR7CwxhhTzwRT038EaAOc7pz7tLwDROQU4B/AFODq8BXP1EbJyRr8t27V35s0gVdegT594Ndf4cknjz3knKQkHRfQuHHky2uMMfVFMH365wPjfAV8AOfcZ8CtwIVhKpep5Ro10ul8e/Zok37TpvDBB/oAkJWVXHRcUpKOA7jiCrjhhuDm6mdnW3IfY4ypjGCCfjyagCeQ34C4kEpj6pSUFO2j371bg3nz5vDhh3DYYTo537tk78qVMH68jgPIyvId+J3TJD4ZGZbcxxhjKiOYoL8MuENEGvo6wPPe7WjGPmOKpKZqsPfW+Js3h4cf/prf/U6b+l94AfZ6JoQ2bKjHZWaWP+9/wgRN4rN3b+nkPnv36v4JEyL3vYwxpjYKJuiPQkfsbxCRZ0XkVhEZ5tluFZFngPWeY8ZUXVFNbdW0afECPc5Bkyb7efllOPJIrbEPHKiBHnQMQE6OjgcoKCi+Rna2Ju/xNe3PkvsYY0xgwSTnWQ0cC/wd6AVMBmZ7tsnAyWiK3uOcc99VXVFNbda0qfbn79mjr5s100Q8nTvDzz9r4N+8Wd9LTtba++bNmskPdCCgJfcxxpjQBJWcxzmX6Zwb7Zw7HGgAtPJsyc65jp73tlZlQU3tJqKBvmlTrcFrjR9eegm6dtX++Qsv1EV5QAf4HTwImzbBgQPa12/JfYwxJjQVzsjnnNvreQjIdM4FWFvNmGLewB8TU7KpXwN/jx7axP+nP8GaNXp8YqL23W/cqOclJfm/viX3McYY/wIGfRG5qKIXFZGWItKzckUydZmIBn1vU79zOr3v+ec1e9/27drUv3KlHp+YqAl+evQo3cdfHkvuY4wx/gVT058hIqtE5DoRaeLvQBE5VUTmAD8CXcNSQlMneZv6vYE/KQkWLIAzz9TBeJdcAp99psfGx0NaGlx5pe/aflISjBtnyX2MMcafYIL+EcBrwCRgm4h84xnFP1VE7heR2SKyUER2Aos9x/dzzs2pumKb2q5kH7+3qT8hAebMgT/+UafiDR4Mb7+tx8fGwq23wuWX63HeRX0aNNDXY8bApEnV932MMaY2CJiG1zmXB0wSkQeAPwL9gROBDCAB+BX4HpgOvOSc+77qimvqEm/gF4EdO3SefmwsPPaY1tjnz4drr4UHHoBLL9X37rhDs/d9/rkm/WnZUpv0rYZvjDGBBb3KnnNuv4h8iK6ot7cKy2TqkZKBf/t2DfxRUXDPPdoKMGUK3HIL7NwJN96o7x12mCb9adxYm/2jKjwc1Rhj6qdgBvJFi8hdIpINbAN2i8irItK4yktn6o2mTXUp3t27dcS+iKbYnTxZf3/gAU21W1Cgrxs21LS9ZZP4GGOM8S2YOtJ1wARgJbqK3r+AC4BHq7Bcph5KTdU8+nv2FKfZveIKmDUL4uKKm/vzPRNFvUl8vHP5jTHG+BdM0B8KzHXOneGcu9U5dzFwA3CZiNgCOyasUlJ0JP4pp8CJJ8Jzz+lUvuef16l977wDf/mLNveDHltYCBs2FOfwN8YYU75ggn4HoGxy05eAaKBt2Etk6i3vKnpHHqm1982bYeJEOOEE+PRTeO01Hbi3fLlm79uwQc9LSNBBfhs2FKf5NcYYc6hggn4ysLvMPu+fVp8r7xlTUeWtopefD/v26VS+N9+Ef/9bHwp++gkGDIAVK/S42Fit9W/Zoq0AtsyuMcYcKthxz61EpIN3Q2v/h+z3vGdMhQVaRS8/H2bP1nn5r78Op52mS/P++c/wn//oMdHROsBv+3bNwV/e8rzGGFOfBRv0XwF+KLF55+K/UWb/D2Eun6kngllFLypKA3yjRvDMMzp3f+9eHdw3c6bW7r0j+3NyNGe/DfAzxphiwczTv7LKS2HqvWBW0du7V5vvQZvzH3wQ2rWD++7T7ccf4f77NW1vgwZ6/IYN0KqV5vA3xpj6LpiMfH8P94eKyHDgZqAl8B0wyjn3qY9jFwBXlPNWnnOuQbjLZqpHixbaJ5+b6/uYpKTiKX0NG2qtfvhwaNsWRozQ1frWrYO5czXhT0KC1vQ3bNABgCkpkfs+xhhTE0U8l5mIXIKm7J0MHA8sBd4RkTY+ThmJPhyU3H4G/ln1pTWRMnBgcKvoDR2qmfh27y4erHfuufDGG/rg8OWX+tq7PG9srM7nz8yEbdusn98YU79VRwLTMcAC59xc59wa59xNQCZwfXkHO+d2OeeyvBvQER1IODdyRTZVLTVVV8kLtIpeaio0b67bnj3FDwrHHKOL8xx/vE71u+ACePddfS8qSscB7Nql/fz790fmOxljTE0T0aDvSebTDVhY5q2FwElBXmYo8J1zbmk4y2aq36RJulpeoFX0RKBJE+2rz8srHqyXng4vv1y8St/VV+uMAG/tPjm5OJFPoPEDxhhTF4mL4IRmEckAtgCnO+c+KbF/AnCpc65TgPNT0FaB251z030cMwwYBpCent7txRdfDFfxAcjJySE5OTms16xvAt3DggKdwnfggDbPp6b6HtnvnNbcRXTz7nvllcOYN68DhYVCr147uOWWNTRoUFD0fmGhXjvQjIGayv4dhs7uYejsHoZHuO9jnz59Vjjnupf3XtCr7NUQl6GtE8/6OsA5NweYA9C9e3fXu3fvsBZg8eLFhPua9U247+H+/Tqqv7BQR+n/9ht88IG2BuTmwrJlzRg37lSefhoOP1zPcU6n9TVooGMBYmrZ/wn27zB0dg9DZ/cwPCJ5HyPdp78DKADSy+xPB7KCOH8o8Kpzbme4C2Zqr7g4aNNGp+rde6+m7d2wAXbsKJ67/9NPcM45xYl8du2Cs8+Gnj116t/WrdX7HYwxJhIiGvSdc/uBFUC/Mm/1Q0fx+yQivweOxQbwmXJER8OTT8KCBZq219uPv3evBv7oaK31X3stnHeeDvjbsEFbCO67Dzp00IGCNrrfGFOXVUej5lTgWRH5EliCLt2bAcwGEJFnAJxzl5c5bxjwg3NuceSKamoLbxpfXyvtFRRo4HcOVq4s/Z53qd6ZM3UcwSOP1L7mfmOMCUbEp+w5514CRgHjgVXAKcA5zjnPmmm08WxFRKQhMAh4KmIFNbVKMGl84+KKB/uVJz9fWwu++cZG9xtj6qZqqc8452YCM32817ucfXvQ1f6MKVcwaXzz83XEvr8kQNHR8P77mhegWTMdDBhVHdksjDGmCtifM1MneNP4+hMbG3gBnvx8HQBYUAA9eugAwZkztfvAGGNqOwv6pk4IJo2vc4EX3omN1T7/bt00e9+WLTrALyMD7ryzOPVvdjZ06gTt22uuf3soMMbUBhb0TZ0QTBrfG28MPDp//35YvLj0DID8fB0g+MgjcMcdGvwzMnRVv/XrYfToQx8KjDGmJrIxyqbO8KbpnTJFg3dhoSbfKSgoTuOblASPPlo8Yr+kmBg4eNB3i0F+Pjz0kA4ILDlLwLsy4NSp+vOee8L3nYwxJpws6Js6Q0QD7pgxOpo/K0v7+i++WFfmA03eExVV+sEgKUkD/SmnwGefaS3fl4KC8h8YQAcSTpkCY8cWf54xxtQkFvRNnZOaqkvwlqfkg8Hzz8MPP0BaGlx0ETz9NHz0UWifHR2ti/74+nxjjKlOFvRNvZSaCjfcoM35mZlaS09L04F+oczRz8vTFgZjjKmJLOibei0mBlq31tH3p58eeAZAIElJ2qVgjDE1kY3eN/WeiCbh6doVrrkm8LQ+fw4ehD/+0ab0GWNqJgv6xngkJsK0abooT3x8cSa+pCR9PXgwpJddH7LM+VddBRMn2pQ+Y0zNZM37xpQQE6NT+kaNgmefhV9/1YB93nmQkqKDAP/yF1i2rPic+Hj9OWyY/pwzx6b0GWNqJqvpG1OOtm3hllu0ln7++dCoke6PjdXpgK+9Bq1a6b59++Dcc2HQIJg9O/CUvt9+i8hXMMaYQ1jQN8aHuDg47DAd1b9nj87r9zrxRFi6FG6/XWv6r70GZ54ZuPneO6XPGGOqgwV9Y/zwDvJr104Dek5OcWCPidHUvgsXQvfuhz4YlCcvD7ZurfJiG2NMuSzoGxOEhARt8m/S5NDgfvjh8PrrcOGFwV0nOhp27bJBfcaYyLOgb0yQoqKgWTOt9RcWlq71R0XBffdpl4A/hYX6cJCVBRs2+O7/N8aYqmBB35gKSkjQwN+kiQZ+b62/cWO4/np939d5112n5zVsqPs2btQHgAMHIlFyY0x9Z0HfmErw1vrbttXX3lr/zTcXz/MXOfScjh2LWwfi4jT45+TAunWawCfQ0r/GGBMKm6dvTAgSEqBNG52Gt327Tum75Rads//WW/DLL5qlb/FiWLUKRoyAf/xDV/s7+mi9RlKSBvvt2zXwN2+uSwKXfWgwxphQWdA3JkRRUdpk36CBBvndu7UGf+mlxceMHatT9SZPhi+/hP794ZJLYMkSDfgjRuhc/4QE2LJFs/s1b+67q8AYYyrDmveNCZP4eF28JyNDM/KVXK0vKkqD/CefwNVXaxP/Cy9on/7mzZq694QTNGtfcrIu/LN+va4AGGgaoDHGBMuCvjFhJKLZ+9q315r/7t2lg3ZKigb1sqP88/M1s9+cOfDww/oAUVgIJ52k13r0UW3+N8aYUFjQN6YKxMRAy5Y60M85ndtfWKh9/7Nna4AvT34+PPGE5uc/4QRtCdi6Fe64Q7MDjhsX+vK/xpj6y/r0jalCiYka+Hft0pr6669rch5/Dh6EJ58snbzHO59/5kz9/aKL9CEiyh7bjTEVYH8yjKliUVGQmqrN9Lt2BZeQx1e2vvx8mDdPWwrWr9cWBMvsZ4wJlgV9YyIkNhaOOEJr/6GIjtZgHxOjTf/r1pXODmiMMb5Y0DcmggYODD0BT16eZvCLidGpgdHROs1v/XrIzbXgb4zxzYK+MRGUmqqD8ZKSyn8/NlaDuT8isHJls6LgHhurwT8qSqf/WfA3xvhiQd+YCJs0CcaM0cQ73oF4SUk6Te+yywIP9HMOpk7twpln6gj/3/8enntOA33Z4G/N/saYkmz0vjERJqJT8saMgVde0QV3WrSAiy/WB4HkZHjqqfIH/CUkQM+esGrVPlavji/af+edMH68Lvhz880a/A8c0Gb/2FhdJyA52Ub7G1PfWdA3ppqkpsLQoYfunzZNa/7TpmngLizU1wUFupgPQP/+X3DbbacVneOd9z9rltbsb71Vg31srE4BzMrSFoRmzYpbA4wx9Y/9r29MDRMVBQ88oCPz27eHVq3g9tvhq690IZ/ZsyEmpvzRgPv3w4wZ2oLgHTAYE1OcBXDbNvj5Z9i5Ux8GAsnOhk6dtBxz5+prY0ztZUHfmBqqSRP48UfNynfTTdq0/9prwfX5jxwJfftqMiBvcI+O1uCfkAC//qrBf/v28nP7O6ddBhkZWob162H0aH195502TsCY2sqCvjE1XFSU5uxv316n6wWT3KdhQ1i7Fm68EU49FRYsKD4vKkrXBWjQQJMFrVunrQp79xafP2GCLv6zd29xi0Furr6eOlXfN8bUPhb0jakloqM18Pua7ueVlKTdAQ8/rMdv3Ki5+3v2hOnTi5voRfTYhg01mG/YoNvmzTBlSulVAkvKy9P3f/strF/PGBMBFvSNqUUGDgy84E5BAVx4Ifz1r/Dxx5rH/5hjYMcOeOghneI3YYI+DHglJGjwdw7mz9cHAn+io+Hll0P+OsaYCLOgb0wt4k3u42v0fWKijvBPSdHX0dFw3nnwzjvwwgtw+ulaU583D04+WQcGLl9e3EcfF6fLAZds6i9PXp7OCDDG1C4W9I2pZSZNgvT00sl9GjTQ18OHaz9+Tk7pFgEROO00eP55eP99bTGIioK33tJWgfbt9WFi+3Zo3jzw+gBJSZpbwBhTu9g8fWNqGREdRb91q/bT798Pf/ubJvdp3FgH3u3Zo835Bw/qw0BsbPH5nTtrDoBGjeDvf9eHgwMHtCXghRegR4/A0/kKCuCCC6ryWxpjqkK11PRFZLiIrBORvSKyQkRODXB8nIhM8pyzT0Q2isiISJXXmJooNVVH6K9bp0l+GjfW/SVH+2dkaIDevbs4gQ/oIL8XXih/fMDy5foQ4GtqYGIiXHWVDgj89ltdObBdO5vHb0xtEPGgLyKXANOBycDxwFLgHRFp4+e0F4H+wDCgE3Ax8E0VF9WYWi0qSgfntW8PbdpoEN+9W/viZ8/2P/XPuUMfCBITdX2AYcN0dsCsWdC9u87337ABRo2yefzG1HTVUdMfAyxwzs11zq1xzt0EZALXl3ewiJwJ/AE4xzn3vnNuvXPuC+fc4sgV2Zjayzs1r00brZF/9FHgNLwJCTrozzsgEDSQDxwI55+vU/bmzNHWA+88/rw8HQD4yCNw223FDw2W1c+YmiOiQV9E4oBuwMIyby0ETvJx2oXAcmCMiGwWkR9E5DERSa66khpTNyUkaA0/0Oj8ffu0b3/FCu3/P+EEPee55+APf4DHHvPdUpCfr/kAVq7URYUsq58xNYe4CP6fJyIZwBbgdOfcJyX2TwAudc51Kuecd4HewIfAJKAxMAP4xjk3sJzjh6HdAKSnp3d78cUXw/odcnJySE62541Q2D0MXSj3cMcO2LSpuIZenqgoDc5NmhTv++mnZN56qyUffphOfr6OAU5IOMjxx2+jR48sDjtsT9H8fm/Wv9zc8j8nKkpnIGRkVOorhIX9Owyd3cPwCPd97NOnzwrnXPdy33TORWwDMgAHnFZm/wRgrY9zFgL5QEqJfWd6rpPu7/O6devmwm3RokVhv2Z9Y/cwdMHew507nfvd75xr1865OXP09c6dziUkOKd17fK3+Hjnvv3WuS1bDt1GjPB/rneLjvb/fkKCc9nZVXqb/LJ/h6Gzexge4b6PwFfOR1yMdJ/+DqAASC+zPx3wleojE9jinNtVYt8az09/g/+Mqbf8LZgzdarOyfeVzjcpCW64QQft7dlz6II8rVsHTgUsEjirn4iOC8jN1fEDvXsH++2MMZUV0aDvnNsPrAD6lXmrHzqKvzxLgIwyffi/8/zcEN4SGlM3BFowxzntby8vwc+YMTpQr0MHXdY3KkqDf16eXuvccwOnAnYu8Fz/vXt1JsF332lK4J9+giee0BUAjTFVozpG708FhojINSJylIhMR5v9ZwOIyDMi8kyJ458HfgXmi8jRInIyOuXvFefcL5EuvDE1XXZ24AVzHnkExo7VBD+HH66j+h99FDIz4Z57tBYeFaVL8bZpoyPvGzfWQB0drXkBfGXtS0jQufuBavoJCfDNN5opcONGXejn5pv1QWP0aN/jAYwxlRfxjHzOuZdEpCkwHmgJfItOx/PW2tuUOT5HRPqig/eWA9nAG8BtESu0MbXIK6/4Tqzj5V0wZ+hQTfATSFwcNGumA/vy8+GWW7S2//TTmsinsFCb/AsKNPf/0KHQrVvphEBl7d0Ln35auvvAOyPgySf13FGjNHNgo0alWyWMMZVTLWl4nXMzgZk+3utdzr616OA9Y0wAWVm+a/lelV0wxzsqv0EDnZZ3881wxhkauIcPhz/+sTgz4HXXaZ+9r6l9zh06XsArP18fKG68UWv8u3fr/oYNix8AAj3YGGMOZbn3jaljWrTQWnduru9jwrFgTmysNvv//LPW2nft0r7/PXu0ZeDmm/W42bNLtwYcPAhHHql9+f7GBkRH64JAl16qr53Th4Hdu4sTDqWkaDdD3756zOLFoX0nY+o6aywzpo4ZODDwQLuCAl2gJxxENPC2aFE8+C82Vlf6Gz5cc/m3bauj/u+6C1atgjPPDNxfn5cHb76pA/y8n5OQoLX9Bg30QSIzE/77X00D/PPPMHMm7NwZnu9lTF1kQd+YOiY1NfCUvHHjipvhwyk6Wgf/tW6tDwDp6Rqk33lH0/8OGqS182CW7wXt8z/tNOjTBx54QB8YnNMHgLg4DfLegYCbNun3ysjQQYre2QbGmGLWvG9MHTRpkv6cMkX7zQsLtXZcUKBT8rzvV6XYWA3wKSlahtxcnVmQl6dB/M47/Z8fEwNnnw2ffAL/+59uM2Zoi0K/ftqdsHBh6cGC3vEDs2bp76NG6fdu2FAfMkouMWxMfWQ1fWPqIBGdeudvSl4kxcVpC0T79lqWDh3gmmt81/YTEzVB0OzZ8PHHGugbNNAHiKwsePZZ+Pe/fa8h4B0IePCgPnBkZWnz/88/axrisoMLe/e25ECmfrCavjF1WGpqcFPyIkVEM/3Fx+uiPQ0b6oI+3taIxET9OWyYNtU/9FDpgYDe2nrr1ppl0N/SIVFRxQMB4+N1X0EB/PabJgDat694DYItW/Qz5s7VMRGpqRG4GcZUAwv6xphqERUF99+vc/5fflkDb2qq1riTkvS9+fPLb77ftCnwKn35+TBvnnYTnHYatGypYw68Yx2iovShYu7c4oeKUaNgxAjtArn33si3iBhT1SzoG2OqVWqq1uxL2rZNm+d9Nd8HSvHrtXatBnCATp00+J9+Opx4ojb5z5tX+qHCm9/g0Ud1zMCECdqtEBdn4wFM3WBB3xhT4/z736En34mL01aEzz+HpUv1AWDtWq3Zx8RA27bH+UwclJ8PTz0FV1+tLQMiek5ycv16CMjOhp49tfvlb3+zro+6wAbyGWNqnGCyCoIG4vIkJsL11+s2fbpOEUxL0+yBRx+tffs//dTY77Wjo+GDD3TcQXKyBvk9e3SNgHXrdFDgtm2aj2D//vK7G7KztYWhfXt92MjODvydagJ/qzTeeWfgrpVIqa33tzpZ0DfG1DjerIL+JCVpJr6SOfkTE3XQ3tChxQMBTzhB5/Fv3661/h9/1HUBrrjiW7/Xz8vTgYDffKMPCdHRen3vQ0BcnAb8rVs1KP70k/6+a5e2FIwfX/VBs6qCXqBVGidMCM/nVFZteSipiSzoG2NqnGCyChYWwgsvaKCdPVuz/T30EPzf/8FNN8HkyZr7f9++4sCVl6evv/4aunbdEbAcH3+suQLattVphldfDR9+qMEvKkofApKTdUtM1Gv/8osmB6rKoFmVQS+YVRqnTNFZENWlpj+U1GQW9I0xNU5FsgqmpmrNfuJEXaDniCN0NcD5830v9nPgQODAGB2ttWjQY/ftg3ffhcsvh9/9Di64QEf4v/eeTgH0Tkc8cEAHIfr6bG/QzMrS8ypTU6/KoFeRVRqrQ214KKnJLOgbY2qkSZN05H3J5vsGDfR1oKyCr74aOHCJ+B6Ml5ioXQAbN5b/fkEBfPWVZv676iro2hVOPlmn+40fH9xUv0GDdJ0Cb0191KjgaupVHfSqcpXGcKhpDyW1LbGTBX1jTI0USlbBYAKXc9Cjh9bOy44JuPxy7QLwVVsHHUR47bXQq5eet369Pmy8/rrvqYZee/fCkiWHdj3s3QuPPAIjR2rmwJyc0sdAxYJeZfr8gx1PEeoqjZVVkx5KsrP13+OGDbVnIKEFfWNMjebNKrhunTbjB7NQUDCBKyoKhgzRP9ozZ2oN+7774MsvNeNfVIC/jnFx0LGjBuE1a7Tp/9574bjjgqvp+8o1kJ+vYxE2bdIHnlNP1WlzGzboYMSNGwMHvdxceO65yvX5R3qVRn/Ke2ipCQ8ltXkgoQV9Y0ydE0zgck4DV2qq1tgnTdI/3F276rmBauv5+TpoD7Sb4Jhj4MorNdjGxYVWfu90wYMHtcafmQkvvqgPAvHx2sXhT2wsLFtWuT7/6lyl0ctfUF27tvofSmrzQEIL+saYOieYwNWihe/AlZERuDaZkKALAOXnl661N24M113nezGhQC0IoDX5yZP1AWT9es0NcPfdcNJJ+nugJYMPHNDcAb6uHajPP5TxFOHgL6jOmgXdu1ffQ0ltH0hoQd8YUycFClwZGb7PDbal4MorNfA7p/3v3u2mm7QrouR4gaQkfd2nT+AHCtCgUVBQ3FScn6/9+089pesIeBcRKis21nfSIq+oKHj+ed/fMZKrNJYdCBdMUF2+HIYPr56Hkpo2kLCiLA2vMaZO8gauMWNKp5K9+GKtBS5e7Ptcb0vB1KnlB5+kJL1uq1b6ulkzrZHu369bXp4OxrvsMv28gwd1qeDzz9dynXBC5b9XQYHW/svyBvqjjtKEQv7k52sz+Y8/FicdSkjQB4mYGN2io6t+lUbvQLj9+4tXOAwmqMbE6LTJrVvL/29blWrSQMLKsKBvjKnTKhu4vLXFKVOKl/5t0ECDbnm1yagoDZwJCdCoke5r21YH+XkfBPLy9AHgqqtgwYLyZwfExmrt3t+iQtHRmlp4xw5tyofi4wMFfNAg37q1ZhcsLNRr7N1buuYvomMTvN/J24IQExNcF4U/zmkTfsl7O3q0Tnk88cTgg2qoDyWVWVvAO5AwN9f3MdU5uyEQa943xphyhKOJ21uLTknRJvmOHXWbMkVr/uVNFzz++OCyEV56KfzwA3z0kc4+GDkS+veHNm0Cl2vvXvj+e81ouGKFdkmUzC6YnFy80FBurg5Y3LxZWxh+/FFTDm/apN0Nhx+uDzezZmmyoWD467Nftixw90SoQTWU0fc1aXZDZVhN3xhj/AimNuntk/bXZeDlrS0//LDWLnv21OB5yy0wYAD885+aSthfjoDERK3px8bqlDZv5kCv++47dNngkgoLNWtgSampmmq4Y0f92aGDTpVr315bOEoqKNCUx+3b61TKwkJNPTx6NFxzjX6XxMTi1Qijo4u7DLx99r5mR/gagFj280MJqiUfOry8NfepU/XnPfeUf26wXT9V3c1QWRb0jTEmBOX1Swe7/Gx5DxRXXaVZ/fwpKNABgfn5GkhjY0u3PPztb7pv9mxtui8s1GB08KAGy1NP1Rru99/DwoV6THa21vpXrDj081q0KH4AaNtWExd9+CHceWd0UU3d+5Dy9NPaejFy5KE1YhHtsw/UPeD9PuU9AIQaVAM9dHhH348d6/szKtr1U5NY874xxlRCVSVoCWa64ejRcOSR2m0QHa0BNydHl/7NydGANmqUpgpu21b77++6C1at0hr6eedpoH//fX0QcK643/6MM2DYMF3BsGNH3ZeVpc3uzz8P998Pb7+trQjjx596SPny8/VhY906vWbZLoMdO/y3YoCWrXv3qhmdH47R95Gc3RBuVtM3xphKCKWJOJBgapIiGki9Dh4s3vbt08CamAjvvFN8TFSUlnfaNJ36V7L53/s9li3TREN//3vxdbds0SC+fr1mHly6VMsSE1PAwYOHRtADB6BfPw2eGRlw2GHF2+bN2uzvq+sBtNznnKPl/POf9XrXXadjFpo0Kb5Gye6DqCj9GR0deormYEffV/XshqpgQd8YYyooHE3E/pScbvjKKxqAWrTwPyXNO1YASj8MFBQUPwzs36/X8i45XB5vTf3aa7UlISZGWwvati3+7p99pr9Pnvwpt9zSu9zrJCdrq8OmTbpVxIED2pKRkKCfVTKIFxbqd8rN1ZaNkomKnNNjvfciNrb44cD7UJCWVrtH34fKgr4xxlRQRZqIhw6t/Od4lw0Ohbf2Gx+vrQWvvKIB0V9NOypKj7vkEg2k3mt4g2Zioj7Y+OqbT0rSlpC+fTU3wd69cNpp0LSprh/wxRfaFO7LwYM6NgK03C1b6taixaE/09OLBzV6eR8M8vO1nN4kRyK6eqK/6ZCgx19wgR4XqOWgtrGgb4wxFVSbE7QEU3bvnP02bTTweefx79unff6BcssfPAg//6wrEHoHEr77rl7zuutgxgxtCZk1S993TmvjBQWaXKh5c30o2LoVdu3SboXyEhJ5iWiCpPT00lvz5sU/mzfXB5ZWrbQMc+aUP7YgMVEXYvrtN23VECluJYiLK92K4O1W8G7e1zWZBX1jjKmg2pygJdiyt2pV/sI+7drpQMNHHy3/3MRE6NxZxwSUbE3wPmjMmaM/b7lFBwy+9ZbmAWjeXAcYpqSUvl5engb/zEzdsrKKf2ZlwbZt2nrg3b791v/3T03Vz2raVK/r7R6Ii9OHjwsv1BkU3m4B0P0FBfqAsm9f8euSvC0JUDw90fuAUDLLYdkHhUg/JFjQN8aYCho4ULPH+VNTE7SEo+zeEereWnDJgYbDh8MTT/ge7+AdM3DZZZq58IILStemCwr0d28ATUrSEfKHH+67PAcPasDPytIHiG3bird33tFg3bChHpOdXf66997pgS+8oBvoA0jTpro1a6aDCL2vvZt3X2pq8UNSYWFxpsOLLtIHgn/8o/SDARS/DrSAUjhZ0DfGmAqqzQlawlF270DDDz/UYFwy9/3LLweXO/+rr7QZ3TvQ0Ltuwf79uq+8QOjtXy9ZY/Zez9vvX9Jvv8Hnn2vwHTFCR/87pw8GO3boQ8GOHaVbCnbs0MyCv/6qXQu7dmlXRTAaNND726SJ/kxO1lwIoC0bJ5+s0ycbN9YtNVW/T2Wnd1aGBX1jjKmE2pygJVxlj44+dMpasOMdtm8vv/vAyzsYz7t5FzQ6cKB46eCSqxCWJALTp+u0RO+Ygrvu0vwJ110HN98ceHBeYaE+NPz6qz4IlHwY2Lmz9O/Z2fozN1e3zZsPvd6//qVbWdHRcOGFHfnDH/yXJ1ws6BtjTCVUZlpdTVGVZQ/XeAdvf3fJUfllOVfclF7y4eDuuzUzoK8xBfv3a/Ii7+d4uxdKDsaLitIae5MmcMQRgb+3czqFcOdOeOwxeP318jMKRkVpd0B8vD4s5ObCrl2FFc7mWFkW9I0xJgThmFZXXaqi7JEc71Cyqd/7cJCdHXhMwfz5cMcdOqagZFIjb/dCySl9l12mAf2554o/s+yDgndr1EgfOt54w/caAoWF2mVw1VVaDhE444wNjB7dlhEjtOvFm3ypKljQN8YYEzbVPd4h2BwKb7/t/4GnsFCb77Ozi1MWn3/+oQ8K3ocF74PCa68FHpFfWKgLInmXRY6LKwxbNsdAaviMQmOMMbXNpEka2Ksid34g4cih4BxMnKh5Cn7+GTZsgFtvhd/9TsdBNGmi8/9btdJMhR066HuHH66tGL5aGby8uQ98lW3KFB1PUBUs6BtjjAmr6lyQxjumwJ9AYwpKrqvgnUWQm6uvp071nZwoKkrXGgj0+YEEWvAnFBb0jTHGVAnvgjTr1mlTeiQGOA4ceGjinLL8jSnwrqvgq7UgUE08mM8PpCqzOVZL0BeR4SKyTkT2isgKETl0fcbiY3uLiCtnOzKSZTbGGFPzBbM08bhxvh9AQl16N9Dne1cG9KcqszlGPOiLyCXAdGAycDywFHhHRNoEOPVooGWJ7YeqLKcxxpjaKZQxBeEYE+Dv82+6KfBDRVVmc6yO0ftjgAXOubme1zeJSH/geuB2P+f94pzbUeWlM8YYU6uFkocgHHkGAn1+YmL1zW6IaNAXkTigGzClzFsLgZMCnP6ViMQDq4F7nXOLqqCIxhhj6ojK5CEIZ54BX59fNiMiRC6bY6Sb95sB0cC2Mvu3Ab6emzLRVoA/ARcBa4EP/Y0DMMYYYyoj1DEBwSg7uyEuLnKzG8RFMNO/iGQAW4DTnXOflNg/AbjUOdcpyOu8DRx0zp1fznvDgGEA6enp3V588cWwlN0rJyeH5OTksF6zvrF7GDq7h6Gzexi6unwPt27VZnlviIyK0t9btNBpeeEU7vvYp0+fFc657uW9F+k+/R1AAZBeZn86UJEJCl8Ag8p7wzk3B5gD0L17d9e7d++Kl9KPxYsXE+5r1jd2D0Nn9zB0dg9DV9fvYXZ2ZNZViOR9jGjQd87tF5EVQD+g5ISHfsCrFbjUcWizvzHGGFMlavO6Cr5Ux+j9qcCzIvIlsAS4DsgAZgOIyDMAzrnLPa9HAeuB74A44DLgQrSP3xhjjDFBinjQd869JCJNgfHofPtvgXOccxs8h5Sdrx8HPAy0BvLR4H+uc+7tCBXZGGOMqROqZZU959xMYKaP93qXef0Q8FAEimWMMcbUaZZ73xhjjKknLOgbY4wx9YQFfWOMMaaesKBvjDHG1BMW9I0xxph6woK+McYYU09Y0DfGGGPqCQv6xhhjTD0R0VX2Ik1EtgMbAh5YMc3QhYNM5dk9DJ3dw9DZPQyd3cPwCPd9bOucSyvvjTod9KuCiHzla8lCExy7h6Gzexg6u4ehs3sYHpG8j9a8b4wxxtQTFvSNMcaYesKCfsXNqe4C1AF2D0Nn9zB0dg9DZ/cwPCJ2H61P3xhjjKknrKZvjDHG1BMW9I0xxph6woJ+BYhIGxF5U0RyRWSHiDwmInHVXa6aSkSOFZEXRGSTiOSLyFoRuUVEosocd4yIfOw5ZouITBARqa5y11Qi0sxzf5yINCvznt3DAETkMhFZJSJ7Pf//PlPmfbuHfohIDxH5QER+82wfisjvyxxj97AEEZkuIl95/s2t93FMwHsmIn8SkdUiss/z84+VLVNMZU+sb0QkGngL+BU4FWgK/B0Q4KZqLFpN1g3YDgwGNgK/B+ai/+4mA4hII+B94BOgB3AkMB/IBR6JfJFrtPnAKiCj5E67h4GJyAjgduBm4HMgEfhdifftHvohIsnAu+jfwJ7o3707gPdEpI1zbo/dw3JFoXHiGODMsm8Gc89EpBfwEjAReA24CHhZRE52zn1R4RI552wLYgPOBgqBw0rsuwzYCzSq7vLVlg14CFhR4vX1wG4gscS+8cAWPANNbXMAI4EPgTMABzSzexj0vWvs+SPaz88xdg/938Punn937Uvsa+/Z193uYcD7Nw5YX87+gPfME/DfL3PeB8ALlSmLNe8Hrxewxjm3qcS+94B4tEZrgtMIyC7xuhfwqXMuv8S+99DabLsIlqvGEpHjgVuBy9EHz7LsHvp3JhANpHuaRreIyOsi0qHEMXYP/VuLttpdLSLxIhIPDEVb8L7zHGP3sOKCuWe9gIVlznsPOKkyH2hBP3gtgG1l9u0ACjzvmQBE5ARgCDCrxO7y7uu2Eu/VayLSAHgRuMk5t8XHYXYP/euA/q0bD4wB/gjEAotEJMlzjN1DP5xze4DewJ+BPM92Cdp64g1Ydg8rLph75uuYSt1TC/omIkSkE9ofOM0592p1l6cWeQz4zO5ZSKLQID/COfeuc+5L4FKgOTCgWktWS4hIIvA0Oh6iJ3AysBL4l+fB1NQSFvSDlwWkl9nXDG02zIp8cWoPETkSWAy86Jy7rczb5d3X9BLv1Xd/AIaIyEEROYj26wNkich93t+xe+hPpufnau8O59wuYCvQxrPL7qF/fwU6Alc655Y75z737GuDtpyA3cPKCOae+TqmUvfUgn7wlgFHiUjrEvv6AfuAFdVTpJpPRDqjAf9l59zocg5ZBpwqIgkl9vVD/yCvr/IC1nxnAscCx3m2azz7e6OtAGD3MJAlnp+dvDs8o9FbUrz0tt1D/5LQQXslx5QUevZ544jdw4oL5p4t8+yjzDFLK/WJ1T2qsbZsaI3+/4CPgOOBvugIyxnVXbaaugFHo31PL6L9T0VbiWNS0CfWF4Eu6HSU3cDY6i5/TdzQYF929L7dw8D37Q3gW7RZujPwsuePapLdw6Du35HoTKVZwFGe/7efBXYBre0e+rxvh6MP61PRQH6cZ4sL9p6hA/YOArd5/jvcDhwATqxUmar7ptSmDW3K+g86iOVXtKYVX93lqqkbcJcnQB2ylTnuGHSe6l60KXYi9XyKj597ekjQt3sY1H1riOaI2InOHnkT6Gj3sEL3sB/wGfCb5x4uAk6ye+j3ni328TewXUXuGTAQ+B7YD6wBLqpsmWzBHWOMMaaesD59Y4wxpp6woG+MMcbUExb0jTHGmHrCgr4xxhhTT1jQN8YYY+oJC/rGGGNMPWFB35haSkSGiIgrseWKyHrPCnJ/FhGp5HV7e67XO7wl9vuZpb5LFX3G+BKfsbkqPsOYms6CvjG138Xo8pvnAHeiqaFfAN73LJRSm1yEfpeqMN9z7ber6PrG1Hgx1V0AY0zIVjnnfizx+lkReRlNNfsQcFP1FKtSVjrn1lfFhZ0uTbxFRLZXxfWNqQ2spm9MHeR0Kd5/AUNLrBmPiCSJyIMisk5E9nt+3iEifv8WiMiZIvK2iGSKSJ6IfCsiY0UkusQxb4rIynLObS8ihSJyXUW/h4i08zTHDymz/5AuCBE5S0SWisguEckRkbUiMqGin2lMXWZB35i6620gHugOICIxwHvoSn3TgbOBp9AugYcDXKsDuqzvVcC5wN/RtRXuK3HMLOA4Efl9mXOHAbnAc5X/Kv6JSAfg38A64BLgfHSRE1vr3ZgSrHnfmLpro+dnS8/PvwCnAKc75z7x7PvQM95voog86Jz7pbwLOedme3/3DBD8FIgDxonI35xzhcC7wM/AtcCXnmNjgSuB55xze8L55co4wVOe651zuz37PqrCzzOmVrKavjF1l3f0vnc0fH90/filIhLj3YCFQCzQ0+eFRFqKyJMisgFd6esAcC/QGGgO4An8TwKDRCTFc+qFQLpnf1Va5SnTiyIyUESaV/HnGVMrWdA3pu46zPMz0/OzOdAWDY4lty897zct7yKe/v5/A+ehgf4MoAfFTfsJJQ6fB0QDgz2vrwO+dM4d0tcfTp6BjGehf9OeBbJE5HMROb0qP9eY2saa942pu85F1+he4Xn9K9rn/Wcfx6/3sb8jOi5gsHPuH96dIjKg7IHOuV9F5J/AtSLyHtAHHUMQqrJ/q5LL+exFwCIRiQdOBiYBb4lIO+fcjjCUwZhaz4K+MXWQiPwJHcw23TmX59n9LvAnIMc5930FLucd/X+gxPVjgUt9HD8TWIYOEtwFvFiBz/KlS5nXPrsinHP7gI9EJBmdwdAesKBvDBb0jakLjhORZuhAtjZoM/zFwPvA7SWOew4dVPehiDwCfO05pyP6gHBhiQeEktagYwHuE5ECNPiP9lUY59znnql7pwEzfFyzoq4RkU3ASrTV4UbP/rNEZCNwpufz3gY2Ac3Q774V+DYMn29MnWBB35ja72XPz73AL8B/gUHAK865opS2zrkDInIWcBs6ja49OpXuJ+AtdIDeIZxz+0XkQuBx4BlgJ/A0Ojtgrp8yHU/4BvBNAwYCk4Ef0QGCk4HrgQ/QB5izgfvRsQs7gc+AS51z+WEqgzG1npT4m2CMMWEhIkuAQufcqUEePwRNk3s4sME5d9Czvx06DuFK59yCEMsk6CDDecAfnHOtQ7meMbWR1fSNMWHhGUB3AtAXOAm4oBKX8aYTrtRiQQHcAdzj+X1LFVzfmBrPgr4xJlxaAkuB34DJzrl/V+DcN9FpgFVpHjqYEXx0ZRhT11nzvjHGGFNPWHIeY4wxpp6woG+MMcbUExb0jTHGmHrCgr4xxhhTT1jQN8YYY+oJC/rGGGNMPfH/t9AOhdV4wU4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
@@ -378,7 +374,6 @@
"source": [
"from qiskit_experiments.test.t2hahn_backend import T2HahnBackend\n",
"\n",
- "estimated_t2hahn2 = 30 * conversion_factor\n",
"detuning_frequency = 2 * np.pi * 10000\n",
"\n",
"# The behavior of the backend is determined by the following parameters\n",
From 8fc48495045d1e14824f23a7cedf9d8370f6c58b Mon Sep 17 00:00:00 2001
From: Itamar Goldman <itamargoldman@gmail.com>
Date: Mon, 31 Jan 2022 13:08:32 +0200
Subject: [PATCH 93/93] fixed text
---
docs/tutorials/t2hahn_characterization.ipynb | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/docs/tutorials/t2hahn_characterization.ipynb b/docs/tutorials/t2hahn_characterization.ipynb
index c03428b367..c1209e8f5d 100644
--- a/docs/tutorials/t2hahn_characterization.ipynb
+++ b/docs/tutorials/t2hahn_characterization.ipynb
@@ -259,7 +259,7 @@
"metadata": {},
"source": [
"### 3. Number of echoes\n",
- "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `1` echoes. The analysis should fail for the circuit with `0` echoes. In order to see it, we will add frequency to the qubit and see how it affect the estimated $T_2$. <br>\n",
+ "The user can provide the number of echoes that the circuit will perform. This will determine the amount of delay and echo gates. As the number of echoes increases, the total time of the circuit will grow. The echoes decrease the effects of $T_{1}$ noise and frequency inaccuracy estimation. Due to that, the Hahn Echo experiment improves our estimate for $T_{2}$. In the following code, we will compare results of the Hahn experiment with `0` echoes and `1` echo. The analysis should fail for the circuit with `0` echoes. In order to see it, we will add frequency to the qubit and see how it affect the estimated $T_2$. <br>\n",
"The list `delays` is the times provided to each delay gate, not the total delay time."
]
},
@@ -278,7 +278,7 @@
" └─────────┘└───────────────┘└──────────┘└╥┘\n",
"c: 1/═════════════════════════════════════════╩═\n",
" 0 \n",
- "The first circuit of hahn echo experiment with 1 echoes:\n",
+ "The first circuit of hahn echo experiment with 1 echo:\n",
" ┌─────────┐┌───────────────┐┌───────┐┌───────────────┐┌─────────┐┌─┐\n",
" q: ┤ Rx(π/2) ├┤ Delay(0.0[s]) ├┤ Rx(π) ├┤ Delay(0.0[s]) ├┤ Rx(π/2) ├┤M├\n",
" └─────────┘└───────────────┘└───────┘└───────────────┘└─────────┘└╥┘\n",
@@ -306,7 +306,7 @@
"\n",
"delays2 = [float(_) * conversion_factor for _ in delays2]\n",
"\n",
- "# Delays for Hahn Echo Experiment with 1 echoes\n",
+ "# Delays for Hahn Echo Experiment with 1 echo\n",
"delays3 = np.append(\n",
" (np.linspace(0.0, 25.5, num=26)).astype(float),\n",
" (np.linspace(26.5, 50, num=25)).astype(float),\n",
@@ -322,10 +322,10 @@
"print(\"The first circuit of hahn echo experiment with 0 echoes:\")\n",
"print(exp2_0echoes.circuits()[0])\n",
"\n",
- "# Create a T2Hahn experiment with 1 echoes. Print the first circuit as an example\n",
+ "# Create a T2Hahn experiment with 1 echo. Print the first circuit as an example\n",
"exp2_1echoes = T2Hahn(qubit2, delays3, num_echoes=num_echoes)\n",
"exp2_1echoes.analysis.set_options(p0={\"amp\": 0.5, \"tau\": estimated_t2hahn2, \"base\": 0.5})\n",
- "print(\"The first circuit of hahn echo experiment with 1 echoes:\")\n",
+ "print(\"The first circuit of hahn echo experiment with 1 echo:\")\n",
"print(exp2_1echoes.circuits()[0])\n"
]
},