Source code for braket.tasks.gate_model_quantum_task_result

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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