# Copyright 2019-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import annotations
import json
from dataclasses import dataclass
from typing import Any, Callable, Counter, Dict, List, Optional, TypeVar, Union
import numpy as np
from braket.circuits import Observable, ResultType, StandardObservable
from braket.circuits.observables import observable_from_ir
T = TypeVar("T")
[docs]@dataclass
class GateModelQuantumTaskResult:
"""
Result of a gate model quantum task execution. This class is intended
to be initialized by a QuantumTask class.
Args:
task_metadata (Dict[str, Any]): Dictionary of task metadata. task_metadata must have
keys 'Id', 'Shots', 'Ir', and 'IrType'.
result_types (List[Dict[str, Any]]): List of dictionaries where each dictionary
has two keys: 'Type' (the result type in IR JSON form) and
'Value' (the result value for this result type).
This can be an empty list if no result types are specified in the IR.
This is calculated from `measurements` and
the IR of the circuit program when `shots>0`.
values (List[Any]): The values for result types requested in the circuit.
This can be an empty list if no result types are specified in the IR.
This is calculated from `measurements` and
the IR of the circuit program when `shots>0`.
measurements (numpy.ndarray, optional): 2d array - row is shot, column is qubit.
Default is None. Only available when shots > 0. The qubits in `measurements`
are the ones in `GateModelQuantumTaskResult.measured_qubits`.
measured_qubits (List[int], optional): The indices of the measured qubits. Default
is None. Only available when shots > 0. Indicates which qubits are in
`measurements`.
measurement_counts (Counter, optional): A Counter of measurements. Key is the measurements
in a big endian binary string. Value is the number of times that measurement occurred.
Default is None. Only available when shots > 0.
measurement_probabilities (Dict[str, float], optional):
A dictionary of probabilistic results.
Key is the measurements in a big endian binary string.
Value is the probability the measurement occurred.
Default is None. Only available when shots > 0.
measurements_copied_from_device (bool, optional): flag whether `measurements`
were copied from device. If false, `measurements` are calculated from device data.
Default is None. Only available when shots > 0.
measurement_counts_copied_from_device (bool, optional): flag whether `measurement_counts`
were copied from device. If False, `measurement_counts` are calculated from device data.
Default is None. Only available when shots > 0.
measurement_probabilities_copied_from_device (bool, optional): flag whether
`measurement_probabilities` were copied from device. If false,
`measurement_probabilities` are calculated from device data. Default is None.
Only available when shots > 0.
"""
task_metadata: Dict[str, Any]
result_types: List[Dict[str, str]]
values: List[Any]
measurements: np.ndarray = None
measured_qubits: List[int] = None
measurement_counts: Counter = None
measurement_probabilities: Dict[str, float] = None
measurements_copied_from_device: bool = None
measurement_counts_copied_from_device: bool = None
measurement_probabilities_copied_from_device: bool = None
[docs] def get_value_by_result_type(self, result_type: ResultType) -> Any:
"""
Get value by result type. The result type must have already been
requested in the circuit sent to the device for this task result.
Args:
result_type (ResultType): result type requested
Returns:
Any: value of the result corresponding to the result type
Raises:
ValueError: If result type not found in result.
Result types must be added to circuit before circuit is run on device.
"""
rt_json = result_type.to_ir().json()
for rt in self.result_types:
if rt_json == json.dumps(rt["Type"]):
return rt["Value"]
raise ValueError(
"Result type not found in result. "
+ "Result types must be added to circuit before circuit is run on device."
)
def __eq__(self, other) -> bool:
if isinstance(other, GateModelQuantumTaskResult) and self.task_metadata.get("Id"):
return self.task_metadata["Id"] == other.task_metadata["Id"]
return NotImplemented
[docs] @staticmethod
def measurement_counts_from_measurements(measurements: np.ndarray) -> Counter:
"""
Creates measurement counts from measurements
Args:
measurements (numpy.ndarray): 2d array - row is shot, column is qubit.
Returns:
Counter: A Counter of measurements. Key is the measurements in a big endian binary
string. Value is the number of times that measurement occurred.
"""
bitstrings = []
for j in range(len(measurements)):
bitstrings.append("".join([str(element) for element in measurements[j]]))
return Counter(bitstrings)
[docs] @staticmethod
def measurement_probabilities_from_measurement_counts(
measurement_counts: Counter,
) -> Dict[str, float]:
"""
Creates measurement probabilities from measurement counts
Args:
measurement_counts (Counter): A Counter of measurements. Key is the measurements
in a big endian binary string. Value is the number of times that measurement
occurred.
Returns:
Dict[str, float]: A dictionary of probabilistic results. Key is the measurements
in a big endian binary string. Value is the probability the measurement occurred.
"""
measurement_probabilities = {}
shots = sum(measurement_counts.values())
for key, count in measurement_counts.items():
measurement_probabilities[key] = count / shots
return measurement_probabilities
[docs] @staticmethod
def measurements_from_measurement_probabilities(
measurement_probabilities: Dict[str, float], shots: int
) -> np.ndarray:
"""
Creates measurements from measurement probabilities
Args:
measurement_probabilities (Dict[str, float]): A dictionary of probabilistic results.
Key is the measurements in a big endian binary string.
Value is the probability the measurement occurred.
shots (int): number of iterations on device
Returns:
Dict[str, float]: A dictionary of probabilistic results.
Key is the measurements in a big endian binary string.
Value is the probability the measurement occurred.
"""
measurements_list = []
for bitstring in measurement_probabilities:
measurement = list(bitstring)
individual_measurement_list = [measurement] * int(
round(measurement_probabilities[bitstring] * shots)
)
measurements_list.extend(individual_measurement_list)
return np.asarray(measurements_list, dtype=int)
[docs] @staticmethod
def from_dict(result: Dict[str, Any]):
"""
Create GateModelQuantumTaskResult from dict.
Args:
result (Dict[str, Any]): Results dict with GateModelQuantumTaskResult attributes as keys
Returns:
GateModelQuantumTaskResult: A GateModelQuantumTaskResult based on the given dict
Raises:
ValueError: If neither "Measurements" nor "MeasurementProbabilities" is a key
in the result dict
"""
return GateModelQuantumTaskResult._from_dict_internal(result)
[docs] @staticmethod
def from_string(result: str) -> GateModelQuantumTaskResult:
"""
Create GateModelQuantumTaskResult from string.
Args:
result (str): JSON object string, with GateModelQuantumTaskResult attributes as keys.
Returns:
GateModelQuantumTaskResult: A GateModelQuantumTaskResult based on the given string
Raises:
ValueError: If neither "Measurements" nor "MeasurementProbabilities" is a key
in the result dict
"""
json_obj = json.loads(result)
for result_type in json_obj.get("ResultTypes", []):
type = result_type["Type"]["type"]
if type == "probability":
result_type["Value"] = np.array(result_type["Value"])
elif type == "statevector":
result_type["Value"] = np.array([complex(*value) for value in result_type["Value"]])
elif type == "amplitude":
for state in result_type["Value"]:
result_type["Value"][state] = complex(*result_type["Value"][state])
return GateModelQuantumTaskResult._from_dict_internal(json_obj)
@classmethod
def _from_dict_internal(cls, result: Dict[str, Any]):
if result["TaskMetadata"]["Shots"] > 0:
return GateModelQuantumTaskResult._from_dict_internal_computational_basis_sampling(
result
)
else:
return GateModelQuantumTaskResult._from_dict_internal_simulator_only(result)
@classmethod
def _from_dict_internal_computational_basis_sampling(cls, result: Dict[str, Any]):
task_metadata = result["TaskMetadata"]
if "Measurements" in result:
measurements = np.asarray(result["Measurements"], dtype=int)
m_counts = GateModelQuantumTaskResult.measurement_counts_from_measurements(measurements)
m_probs = GateModelQuantumTaskResult.measurement_probabilities_from_measurement_counts(
m_counts
)
measurements_copied_from_device = True
m_counts_copied_from_device = False
m_probabilities_copied_from_device = False
elif "MeasurementProbabilities" in result:
shots = task_metadata["Shots"]
m_probs = result["MeasurementProbabilities"]
measurements = GateModelQuantumTaskResult.measurements_from_measurement_probabilities(
m_probs, shots
)
m_counts = GateModelQuantumTaskResult.measurement_counts_from_measurements(measurements)
measurements_copied_from_device = False
m_counts_copied_from_device = False
m_probabilities_copied_from_device = True
else:
raise ValueError(
'One of "Measurements" or "MeasurementProbabilities" must be in the result dict'
)
measured_qubits = result["MeasuredQubits"]
if len(measured_qubits) != measurements.shape[1]:
raise ValueError(
f"Measured qubits {measured_qubits} is not equivalent to number of qubits "
+ f"{measurements.shape[1]} in measurements"
)
result_types = GateModelQuantumTaskResult._calculate_result_types(
result["TaskMetadata"]["Ir"], measurements, measured_qubits
)
values = [rt["Value"] for rt in result_types]
return cls(
task_metadata=task_metadata,
result_types=result_types,
values=values,
measurements=measurements,
measured_qubits=measured_qubits,
measurement_counts=m_counts,
measurement_probabilities=m_probs,
measurements_copied_from_device=measurements_copied_from_device,
measurement_counts_copied_from_device=m_counts_copied_from_device,
measurement_probabilities_copied_from_device=m_probabilities_copied_from_device,
)
@classmethod
def _from_dict_internal_simulator_only(cls, result: Dict[str, Any]):
task_metadata = result["TaskMetadata"]
result_types = result["ResultTypes"]
values = [rt["Value"] for rt in result_types]
return cls(task_metadata=task_metadata, result_types=result_types, values=values)
@staticmethod
def _calculate_result_types(
ir_string: str, measurements: np.ndarray, measured_qubits: List[int]
) -> List[Dict[str, Any]]:
ir = json.loads(ir_string)
result_types = []
if not ir.get("results"):
return result_types
for result_type in ir["results"]:
ir_observable = result_type.get("observable")
observable = observable_from_ir(ir_observable) if ir_observable else None
targets = result_type.get("targets")
rt_type = result_type["type"]
if rt_type == "probability":
value = GateModelQuantumTaskResult._probability_from_measurements(
measurements, measured_qubits, targets
)
elif rt_type == "sample":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._samples_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
elif rt_type == "variance":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._variance_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
elif rt_type == "expectation":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._expectation_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
else:
raise ValueError(f"Unknown result type {rt_type}")
result_types.append({"Type": result_type, "Value": value})
return result_types
@staticmethod
def _selected_measurements(
measurements: np.ndarray, measured_qubits: List[int], targets: Optional[List[int]]
) -> np.ndarray:
if targets is not None and targets != measured_qubits:
# Only some qubits targeted
columns = [measured_qubits.index(t) for t in targets]
measurements = measurements[:, columns]
return measurements
@staticmethod
def _calculate_for_targets(
calculate_function: Callable[[np.ndarray, List[int], Observable, List[int]], T],
measurements: np.ndarray,
measured_qubits: List[int],
observable: Observable,
targets: List[int],
) -> Union[T, List[T]]:
if targets:
return calculate_function(measurements, measured_qubits, observable, targets)
else:
return [
calculate_function(measurements, measured_qubits, observable, [i])
for i in measured_qubits
]
@staticmethod
def _measurements_base_10(measurements: np.ndarray) -> np.ndarray:
# convert samples from a list of 0, 1 integers, to base 10 representation
shots, num_measured_qubits = measurements.shape
unraveled_indices = [2] * num_measured_qubits
return np.ravel_multi_index(measurements.T, unraveled_indices)
@staticmethod
def _probability_from_measurements(
measurements: np.ndarray, measured_qubits: List[int], targets: Optional[List[int]]
) -> np.ndarray:
measurements = GateModelQuantumTaskResult._selected_measurements(
measurements, measured_qubits, targets
)
shots, num_measured_qubits = measurements.shape
# convert measurements from a list of 0, 1 integers, to base 10 representation
indices = GateModelQuantumTaskResult._measurements_base_10(measurements)
# count the basis state occurrences, and construct the probability vector
basis_states, counts = np.unique(indices, return_counts=True)
probabilities = np.zeros([2 ** num_measured_qubits], dtype=np.float64)
probabilities[basis_states] = counts / shots
return probabilities
@staticmethod
def _variance_from_measurements(
measurements: np.ndarray,
measured_qubits: List[int],
observable: Observable,
targets: List[int],
) -> float:
samples = GateModelQuantumTaskResult._samples_from_measurements(
measurements, measured_qubits, observable, targets
)
return np.var(samples)
@staticmethod
def _expectation_from_measurements(
measurements: np.ndarray,
measured_qubits: List[int],
observable: Observable,
targets: List[int],
) -> float:
samples = GateModelQuantumTaskResult._samples_from_measurements(
measurements, measured_qubits, observable, targets
)
return np.mean(samples)
@staticmethod
def _samples_from_measurements(
measurements: np.ndarray,
measured_qubits: List[int],
observable: Observable,
targets: List[int],
) -> np.ndarray:
measurements = GateModelQuantumTaskResult._selected_measurements(
measurements, measured_qubits, targets
)
if isinstance(observable, StandardObservable):
# Process samples for observables with eigenvalues {1, -1}
return 1 - 2 * measurements.flatten()
# Replace the basis state in the computational basis with the correct eigenvalue.
# Extract only the columns of the basis samples required based on ``targets``.
indices = GateModelQuantumTaskResult._measurements_base_10(measurements)
return observable.eigenvalues[indices].real