qiskit.primitives.sampler의 소스 코드

# This code is part of Qiskit.
# (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
# 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.
Sampler class

from __future__ import annotations

from collections.abc import Sequence
from typing import Any

import numpy as np

from qiskit.circuit import QuantumCircuit
from qiskit.exceptions import QiskitError
from qiskit.quantum_info import Statevector
from qiskit.result import QuasiDistribution

from .base import BaseSampler, SamplerResult
from .primitive_job import PrimitiveJob
from .utils import (

[문서]class Sampler(BaseSampler[PrimitiveJob[SamplerResult]]): """ Sampler class. :class:`~Sampler` is a reference implementation of :class:`~BaseSampler`. :Run Options: - **shots** (None or int) -- The number of shots. If None, it calculates the probabilities. Otherwise, it samples from multinomial distributions. - **seed** (np.random.Generator or int) -- Set a fixed seed or generator for the multinomial distribution. If shots is None, this option is ignored. """ def __init__(self, *, options: dict | None = None): """ Args: options: Default options. Raises: QiskitError: if some classical bits are not used for measurements. """ super().__init__(options=options) self._qargs_list = [] self._circuit_ids = {} def _call( self, circuits: Sequence[int], parameter_values: Sequence[Sequence[float]], **run_options, ) -> SamplerResult: shots = run_options.pop("shots", None) seed = run_options.pop("seed", None) if seed is None: rng = np.random.default_rng() elif isinstance(seed, np.random.Generator): rng = seed else: rng = np.random.default_rng(seed) # Initialize metadata metadata: list[dict[str, Any]] = [{} for _ in range(len(circuits))] bound_circuits = [] qargs_list = [] for i, value in zip(circuits, parameter_values): if len(value) != len(self._parameters[i]): raise QiskitError( f"The number of values ({len(value)}) does not match " f"the number of parameters ({len(self._parameters[i])})." ) bound_circuits.append( self._circuits[i] if len(value) == 0 else self._circuits[i].bind_parameters(dict(zip(self._parameters[i], value))) ) qargs_list.append(self._qargs_list[i]) probabilities = [ Statevector(bound_circuit_to_instruction(circ)).probabilities_dict( qargs=qargs, decimals=16 ) for circ, qargs in zip(bound_circuits, qargs_list) ] if shots is not None: for i, prob_dict in enumerate(probabilities): counts = rng.multinomial(shots, np.fromiter(prob_dict.values(), dtype=float)) probabilities[i] = { key: count / shots for key, count in zip(prob_dict.keys(), counts) if count > 0 } for metadatum in metadata: metadatum["shots"] = shots quasis = [QuasiDistribution(p, shots=shots) for p in probabilities] return SamplerResult(quasis, metadata) def _run( self, circuits: tuple[QuantumCircuit, ...], parameter_values: tuple[tuple[float, ...], ...], **run_options, ): circuit_indices = [] for circuit in circuits: key = _circuit_key(circuit) index = self._circuit_ids.get(key) if index is not None: circuit_indices.append(index) else: circuit_indices.append(len(self._circuits)) self._circuit_ids[key] = len(self._circuits) circuit, qargs = self._preprocess_circuit(circuit) self._circuits.append(circuit) self._qargs_list.append(qargs) self._parameters.append(circuit.parameters) job = PrimitiveJob(self._call, circuit_indices, parameter_values, **run_options) job.submit() return job @staticmethod def _preprocess_circuit(circuit: QuantumCircuit): circuit = init_circuit(circuit) q_c_mapping = final_measurement_mapping(circuit) if set(range(circuit.num_clbits)) != set(q_c_mapping.values()): raise QiskitError( "Some classical bits are not used for measurements." f" the number of classical bits ({circuit.num_clbits})," f" the used classical bits ({set(q_c_mapping.values())})." ) c_q_mapping = sorted((c, q) for q, c in q_c_mapping.items()) qargs = [q for _, q in c_q_mapping] circuit = circuit.remove_final_measurements(inplace=False) return circuit, qargs