adding context

docs/tutorials/Lindblad_dynamics_simulation.rst

@@ -65,6 +65,7 @@ Below, we first set the number of qubits :math:`N` to be simulated, and then pre
 single-qubit Pauli operators that will be used in the rest of this tutorial.
 
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     import numpy as np
@@ -102,6 +103,7 @@ derivatives of parameters, we do not use the :class:`Signal` class in this tutor
 tutorials for various generalizations of this approach supported with ``qiskit-dynamics``.
 
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     :include-source:
 
     from qiskit_dynamics import Solver, Signal
@@ -143,6 +145,7 @@ We now define the initial state for the simulation, the time span to simulate fo
 intermediate times for which the solution is requested.
 
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     from qiskit.quantum_info import DensityMatrix
@@ -179,6 +182,7 @@ observing that this equality holds is a simple and useful verification of the nu
 that will be added in the next section.
 
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     n_times = len(sol.y)
@@ -223,6 +227,7 @@ a mixed state), becoming tilted along :math:`-y`. This complex dependence of the
 parameters can be systematically analyzed - we encourage you to try it!
 
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     from qiskit.visualization import plot_bloch_vector

docs/tutorials/Rabi_oscillations.rst

@@ -36,6 +36,7 @@ using matrices and :class:`.Signal` instances. For the time-independent :math:`z
 signal to a constant, while for the trasverse driving term we setup a harmonic signal.
 
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     import numpy as np
@@ -64,6 +65,7 @@ We now define the initial state for the simulation, the time span to simulate fo
 intermediate times for which the solution is requested, and solve the evolution.
 
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     :include-source:
 
     from qiskit.quantum_info.states import Statevector
@@ -99,6 +101,7 @@ gates used to manipulate quantum devices - in particular this is a realization o
 gate.
 
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     from qiskit.visualization import plot_bloch_vector
@@ -165,6 +168,7 @@ state vectors and density matrices. The shrinking of the qubit’s state within
 to the incoherent evolution can be clearly seen in the plots below.
 
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     Gamma_1 = .8

docs/tutorials/dynamics_backend.rst

@@ -29,12 +29,14 @@ when using a JAX solver method. Here we configure JAX to run on CPU in 64 bit mo
 array libraries>` for more information.
 
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     # a parallelism warning raised by JAX is being raised due to somethign outside of Dynamics
     import warnings
     warnings.filterwarnings("ignore", message="os.fork")
 
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     # Configure JAX
@@ -69,6 +71,7 @@ where
   respectively.
 
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     import numpy as np
@@ -120,6 +123,7 @@ outcomes of :meth:`.DynamicsBackend.run` are independent of the choice of rotati
 performance.
 
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     from qiskit_dynamics import Solver
@@ -149,6 +153,7 @@ Furthermore, note that in the solver options we set the max step size to the pul
 variable step solvers from accidentally stepping over pulses in schedules with long idle times.
 
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     from qiskit_dynamics import DynamicsBackend
@@ -186,6 +191,7 @@ that the usual instructions work on the :class:`.DynamicsBackend`.
 
 
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     import time
@@ -221,6 +227,7 @@ that the usual instructions work on the :class:`.DynamicsBackend`.
 Visualize one of the schedules.
 
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     schedules[3].draw()
@@ -229,6 +236,7 @@ Retrieve the counts for one of the experiments as would be done using the result
 backend.
 
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     result.get_counts(3)
@@ -248,6 +256,7 @@ Build a simple circuit. Here we build one consisting of a single Hadamard gate o
 followed by measurement.
 
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     from qiskit import QuantumCircuit
@@ -263,6 +272,7 @@ we are only demonstrating the mechanics of adding a calibration; we have not att
 the schedule to implement the Hadamard gate with high fidelity.
 
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     with pulse.build() as h_q0:
@@ -276,6 +286,7 @@ the schedule to implement the Hadamard gate with high fidelity.
 Call run on the circuit, and get counts as usual.
 
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     start_time = time.time()
@@ -294,6 +305,7 @@ Hadamard gate on qubit :math:`0` to `backend.target`, which impacts how jobs are
 backend. See the :class:`~qiskit.transpiler.Target` class documentation for further information.
 
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     from qiskit.circuit.library import HGate
@@ -305,6 +317,7 @@ Rebuild the same circuit, however this time we do not need to add the calibratio
 gate to the circuit object.
 
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     circ2 = QuantumCircuit(1, 1)
@@ -318,6 +331,7 @@ gate to the circuit object.
     print(f"Run time: {time.time() - start_time}")
 
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     result.get_counts(0)
@@ -348,6 +362,7 @@ To enable running of the single qubit experiments, we add the following to the `
   will pass.
 
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     from qiskit.circuit.library import XGate, SXGate, RZGate, CXGate
@@ -389,6 +404,7 @@ object. Here we use the
 template library to initialize our calibrations.
 
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     import pandas as pd
@@ -406,6 +422,7 @@ Next, run a rough amplitude calibration for ``X`` and ``SX`` gates for both qubi
 experiments.
 
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     from qiskit_experiments.library.calibration import RoughXSXAmplitudeCal
@@ -419,6 +436,7 @@ experiments.
 Run the Rabi experiments.
 
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     start_time = time.time()
@@ -431,18 +449,21 @@ Run the Rabi experiments.
 Plot the results.
 
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     rabi0_data.figure(0)
 
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     rabi1_data.figure(0)
 
 Observe the updated parameters for qubit 0.
 
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     pd.DataFrame(**cals.parameters_table(qubit_list=[0, ()], parameters="amp"))
@@ -454,6 +475,7 @@ Run rough Drag parameter calibration for the ``X`` and ``SX`` gates. This follow
 as above.
 
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     from qiskit_experiments.library.calibration import RoughDragCal
@@ -467,6 +489,7 @@ as above.
     cal_drag0.circuits()[5].draw(output="mpl")
 
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     start_time = time.time()
@@ -477,19 +500,22 @@ as above.
     print(f"Run time: {time.time() - start_time}")
 
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     drag0_data.figure(0)
 
 
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     drag1_data.figure(0)
 
 The updated calibrations object:
 
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     pd.DataFrame(**cals.parameters_table(qubit_list=[0, ()], parameters="amp"))
@@ -505,6 +531,7 @@ the control channel index used to drive the corresponding cross-resonance intera
 required by the experiment to determine which channel to drive for each control-target pair.
 
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     # set the control channel map
@@ -514,6 +541,7 @@ Build the characterization experiment object, and update gate definitions in ``t
 values for the single qubit gates calibrated above.
 
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     from qiskit_experiments.library import CrossResonanceHamiltonian
@@ -527,13 +555,15 @@ values for the single qubit gates calibrated above.
     backend.target.update_from_instruction_schedule_map(cals.get_inst_map())
 
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     cr_ham_experiment.circuits()[10].draw("mpl")
 
 Run the simulation.
 
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     start_time = time.time()
@@ -544,6 +574,7 @@ Run the simulation.
 
 
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     data_cr.figure(0)