# Copyright 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 collections import Counter
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any, Optional, TypeVar, Union
import numpy as np
from braket.circuits import Observable, ResultType, StandardObservable
from braket.circuits.observables import TensorProduct, observable_from_ir
from braket.ir.jaqcd import Expectation, Probability, Sample, Variance
from braket.task_result import (
AdditionalMetadata,
GateModelTaskResult,
ResultTypeValue,
TaskMetadata,
)
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 (TaskMetadata): Quantum task metadata.
additional_metadata (AdditionalMetadata): Additional metadata about the quantum task
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 and 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. Note that the keys in `Counter` are
unordered.
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: TaskMetadata
additional_metadata: AdditionalMetadata
result_types: list[ResultTypeValue] = None
values: list[Any] = None
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
_result_types_indices: dict[str, int] = None
def __post_init__(self):
if self.result_types is not None:
self._result_types_indices = {
GateModelQuantumTaskResult._result_type_hash(rt.type): i
for i, rt in enumerate(self.result_types)
}
else:
self._result_types_indices = {}
[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 quantum 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 is not found in result.
Result types must be added to the circuit before the circuit is run on a device.
"""
rt_ir = result_type.to_ir()
try:
rt_hash = GateModelQuantumTaskResult._result_type_hash(rt_ir)
result_type_index = self._result_types_indices[rt_hash]
return self.values[result_type_index]
except KeyError as e:
raise ValueError(
"Result type not found in result. "
"Result types must be added to circuit before circuit is run on device."
) from e
def __eq__(self, other: GateModelQuantumTaskResult) -> bool:
if isinstance(other, GateModelQuantumTaskResult):
return self.task_metadata.id == other.task_metadata.id
return NotImplemented
[docs]
def get_compiled_circuit(self) -> Optional[str]:
"""Get the compiled circuit, if one is available.
Returns:
Optional[str]: The compiled circuit or None.
"""
metadata = self.additional_metadata
if not metadata:
return None
if metadata.rigettiMetadata:
return metadata.rigettiMetadata.compiledProgram
elif metadata.oqcMetadata:
return metadata.oqcMetadata.compiledProgram
else:
return None
[docs]
@staticmethod
def measurement_counts_from_measurements(measurements: np.ndarray) -> Counter:
"""Creates measurement counts from measurements
Args:
measurements (np.ndarray): 2d array - row is shot and 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 = [
"".join([str(element) for element in measurements[j]]) for j in range(len(measurements))
]
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.
"""
shots = sum(measurement_counts.values())
measurement_probabilities = {
key: count / shots for key, count in measurement_counts.items()
}
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:
np.ndarray: 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_object(result: GateModelTaskResult) -> GateModelQuantumTaskResult:
"""Create GateModelQuantumTaskResult from GateModelTaskResult object.
Args:
result (GateModelTaskResult): GateModelTaskResult object
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_object_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
"""
obj = GateModelTaskResult.parse_raw(result)
GateModelQuantumTaskResult.cast_result_types(obj)
return GateModelQuantumTaskResult._from_object_internal(obj)
@classmethod
def _from_object_internal(cls, result: GateModelTaskResult) -> GateModelQuantumTaskResult:
if result.taskMetadata.shots > 0:
return GateModelQuantumTaskResult._from_object_internal_computational_basis_sampling(
result
)
else:
return GateModelQuantumTaskResult._from_dict_internal_simulator_only(result)
@classmethod
def _from_object_internal_computational_basis_sampling(
cls, result: GateModelTaskResult
) -> GateModelQuantumTaskResult:
task_metadata = result.taskMetadata
additional_metadata = result.additionalMetadata
if result.measurements:
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 result.measurementProbabilities:
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 populated in',
" the result obj",
)
measured_qubits = result.measuredQubits
if result.resultTypes:
# Jaqcd does not return anything in the resultTypes schema field since the
# result types are easily parsable from the IR. However, an OpenQASM program
# specifies result types inline and parsing result types is more involved
# (ie. may involve dereferencing logical qubits at runtime), so the parsed
# result type specifications need to be returned, even if not calculated
# during simulation.
if not isinstance(result.resultTypes[0], ResultTypeValue):
result_types = GateModelQuantumTaskResult._calculate_result_types(
json.dumps({"results": [rt.dict() for rt in result.resultTypes]}),
measurements,
measured_qubits,
)
else:
result_types = result.resultTypes
else:
result_types = GateModelQuantumTaskResult._calculate_result_types(
additional_metadata.action.json(), measurements, measured_qubits
)
values = [rt.value for rt in result_types]
return cls(
task_metadata=task_metadata,
additional_metadata=additional_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: GateModelTaskResult
) -> GateModelQuantumTaskResult:
task_metadata = result.taskMetadata
additional_metadata = result.additionalMetadata
result_types = result.resultTypes
values = [rt.value for rt in result_types]
return cls(
task_metadata=task_metadata,
additional_metadata=additional_metadata,
result_types=result_types,
values=values,
)
[docs]
@staticmethod
def cast_result_types(gate_model_task_result: GateModelTaskResult) -> None:
"""Casts the result types to the types expected by the SDK.
Args:
gate_model_task_result (GateModelTaskResult): GateModelTaskResult representing the
results.
"""
if gate_model_task_result.resultTypes:
for result_type in gate_model_task_result.resultTypes:
type = result_type.type.type
if type == "amplitude":
for state in result_type.value:
result_type.value[state] = complex(*result_type.value[state])
elif 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])
@staticmethod
def _calculate_result_types(
ir_string: str, measurements: np.ndarray, measured_qubits: list[int]
) -> list[ResultTypeValue]:
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
)
casted_result_type = Probability(targets=targets)
elif rt_type == "sample":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._samples_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
casted_result_type = Sample(targets=targets, observable=ir_observable)
elif rt_type == "variance":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._variance_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
casted_result_type = Variance(targets=targets, observable=ir_observable)
elif rt_type == "expectation":
value = GateModelQuantumTaskResult._calculate_for_targets(
GateModelQuantumTaskResult._expectation_from_measurements,
measurements,
measured_qubits,
observable,
targets,
)
casted_result_type = Expectation(targets=targets, observable=ir_observable)
else:
raise ValueError(f"Unknown result type {rt_type}")
result_types.append(ResultTypeValue.construct(type=casted_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
two_powers = 2 ** np.arange(measurements.shape[-1])[::-1] # 2^(n-1), ..., 2, 1
return measurements @ two_powers
@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)
if isinstance(observable, TensorProduct):
return np.array([observable.eigenvalue(index).real for index in indices])
return observable.eigenvalues[indices].real
@staticmethod
def _result_type_hash(rt_type: dict) -> str:
if hasattr(rt_type, "observable") and isinstance(rt_type.observable, list):
rt_type.observable = GateModelQuantumTaskResult._replace_neg_zero(rt_type.observable)
return repr(dict(sorted(dict(rt_type).items(), key=lambda x: x[0])))
@staticmethod
def _replace_neg_zero(observable_matrix: Union[list, int]) -> Union[list, int]:
if isinstance(observable_matrix, list):
return [GateModelQuantumTaskResult._replace_neg_zero(x) for x in observable_matrix]
else:
return 0 if observable_matrix == 0 else observable_matrix