qiskit.primitives.backend_estimator のソースコード

# 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.
Expectation value class

from __future__ import annotations

import copy
from collections.abc import Sequence
from itertools import accumulate

import numpy as np

from qiskit.circuit import QuantumCircuit
from qiskit.compiler import transpile
from qiskit.opflow import PauliSumOp
from qiskit.providers import BackendV1, BackendV2, Options
from qiskit.quantum_info import Pauli, PauliList
from qiskit.quantum_info.operators.base_operator import BaseOperator
from qiskit.result import Counts, Result
from qiskit.transpiler import PassManager

from .base import BaseEstimator, EstimatorResult
from .primitive_job import PrimitiveJob
from .utils import _circuit_key, _observable_key, init_observable

def _run_circuits(
    circuits: QuantumCircuit | list[QuantumCircuit],
    backend: BackendV1 | BackendV2,
) -> tuple[list[Result], list[dict]]:
    """Remove metadata of circuits and run the circuits on a backend.
        circuits: The circuits
        backend: The backend
        monitor: Enable job minotor if True
        **run_options: run_options
        The result and the metadata of the circuits
    if isinstance(circuits, QuantumCircuit):
        circuits = [circuits]
    metadata = []
    for circ in circuits:
        circ.metadata = {}
    if isinstance(backend, BackendV1):
        max_circuits = getattr(backend.configuration(), "max_experiments", None)
    elif isinstance(backend, BackendV2):
        max_circuits = backend.max_circuits
    if max_circuits:
        jobs = [
            backend.run(circuits[pos : pos + max_circuits], **run_options)
            for pos in range(0, len(circuits), max_circuits)
        result = [x.result() for x in jobs]
        result = [backend.run(circuits, **run_options).result()]
    return result, metadata

def _prepare_counts(results: list[Result]):
    counts = []
    for res in results:
        count = res.get_counts()
        if not isinstance(count, list):
            count = [count]
    return counts

[ドキュメント]class BackendEstimator(BaseEstimator[PrimitiveJob[EstimatorResult]]): """Evaluates expectation value using Pauli rotation gates. The :class:`~.BackendEstimator` class is a generic implementation of the :class:`~.BaseEstimator` interface that is used to wrap a :class:`~.BackendV2` (or :class:`~.BackendV1`) object in the :class:`~.BaseEstimator` API. It facilitates using backends that do not provide a native :class:`~.BaseEstimator` implementation in places that work with :class:`~.BaseEstimator`, such as algorithms in :mod:`qiskit.algorithms` including :class:`~.qiskit.algorithms.minimum_eigensolvers.VQE`. However, if you're using a provider that has a native implementation of :class:`~.BaseEstimator`, it is a better choice to leverage that native implementation as it will likely include additional optimizations and be a more efficient implementation. The generic nature of this class precludes doing any provider- or backend-specific optimizations. """ def __init__( self, backend: BackendV1 | BackendV2, options: dict | None = None, abelian_grouping: bool = True, bound_pass_manager: PassManager | None = None, skip_transpilation: bool = False, ): """Initalize a new BackendEstimator instance Args: backend: Required: the backend to run the primitive on options: Default options. abelian_grouping: Whether the observable should be grouped into commuting bound_pass_manager: An optional pass manager to run after parameter binding. skip_transpilation: If this is set to True the internal compilation of the input circuits is skipped and the circuit objects will be directly executed when this object is called. """ super().__init__(options=options) self._abelian_grouping = abelian_grouping self._backend = backend self._transpile_options = Options() self._bound_pass_manager = bound_pass_manager self._preprocessed_circuits: list[tuple[QuantumCircuit, list[QuantumCircuit]]] | None = None self._transpiled_circuits: list[QuantumCircuit] | None = None self._grouping = list(zip(range(len(self._circuits)), range(len(self._observables)))) self._skip_transpilation = skip_transpilation self._circuit_ids = {} self._observable_ids = {} @property def transpile_options(self) -> Options: """Return the transpiler options for transpiling the circuits.""" return self._transpile_options
[ドキュメント] def set_transpile_options(self, **fields): """Set the transpiler options for transpiler. Args: **fields: The fields to update the options """ self._transpiled_circuits = None self._transpile_options.update_options(**fields)
@property def preprocessed_circuits( self, ) -> list[tuple[QuantumCircuit, list[QuantumCircuit]]]: """ Transpiled quantum circuits produced by preprocessing Returns: List of the transpiled quantum circuit """ self._preprocessed_circuits = self._preprocessing() return self._preprocessed_circuits @property def transpiled_circuits(self) -> list[QuantumCircuit]: """ Transpiled quantum circuits. Returns: List of the transpiled quantum circuit Raises: QiskitError: if the instance has been closed. """ self._transpile() return self._transpiled_circuits @property def backend(self) -> BackendV1 | BackendV2: """ Returns: The backend which this estimator object based on """ return self._backend def _transpile(self): """Split Transpile""" self._transpiled_circuits = [] for common_circuit, diff_circuits in self.preprocessed_circuits: # 1. transpile a common circuit if self._skip_transpilation: transpiled_circuit = common_circuit.copy() perm_pattern = list(range(common_circuit.num_qubits)) else: transpiled_circuit = transpile( common_circuit, self.backend, **self.transpile_options.__dict__ ) if transpiled_circuit.layout is not None: layout = transpiled_circuit.layout virtual_bit_map = layout.initial_layout.get_virtual_bits() perm_pattern = [virtual_bit_map[v] for v in common_circuit.qubits] if layout.final_layout is not None: final_mapping = dict( enumerate(layout.final_layout.get_virtual_bits().values()) ) perm_pattern = [final_mapping[i] for i in perm_pattern] else: perm_pattern = list(range(transpiled_circuit.num_qubits)) # 2. transpile diff circuits transpile_opts = copy.copy(self.transpile_options) transpile_opts.update_options(initial_layout=perm_pattern) diff_circuits = transpile(diff_circuits, self.backend, **transpile_opts.__dict__) # 3. combine transpiled_circuits = [] for diff_circuit in diff_circuits: transpiled_circuit_copy = transpiled_circuit.copy() for creg in diff_circuit.cregs: if creg not in transpiled_circuit_copy.cregs: transpiled_circuit_copy.add_register(creg) transpiled_circuit_copy.compose(diff_circuit, inplace=True) transpiled_circuit_copy.metadata = diff_circuit.metadata transpiled_circuits.append(transpiled_circuit_copy) self._transpiled_circuits += transpiled_circuits def _call( self, circuits: Sequence[int], observables: Sequence[int], parameter_values: Sequence[Sequence[float]], **run_options, ) -> EstimatorResult: # Transpile self._grouping = list(zip(circuits, observables)) transpiled_circuits = self.transpiled_circuits num_observables = [len(m) for (_, m) in self.preprocessed_circuits] accum = [0] + list(accumulate(num_observables)) # Bind parameters parameter_dicts = [ dict(zip(self._parameters[i], value)) for i, value in zip(circuits, parameter_values) ] bound_circuits = [ transpiled_circuits[circuit_index] if len(p) == 0 else transpiled_circuits[circuit_index].bind_parameters(p) for i, (p, n) in enumerate(zip(parameter_dicts, num_observables)) for circuit_index in range(accum[i], accum[i] + n) ] bound_circuits = self._bound_pass_manager_run(bound_circuits) # Run result, metadata = _run_circuits(bound_circuits, self._backend, **run_options) return self._postprocessing(result, accum, metadata) def _run( self, circuits: tuple[QuantumCircuit, ...], observables: tuple[BaseOperator | PauliSumOp, ...], parameter_values: tuple[tuple[float, ...], ...], **run_options, ): circuit_indices = [] for circuit in circuits: index = self._circuit_ids.get(_circuit_key(circuit)) if index is not None: circuit_indices.append(index) else: circuit_indices.append(len(self._circuits)) self._circuit_ids[_circuit_key(circuit)] = len(self._circuits) self._circuits.append(circuit) self._parameters.append(circuit.parameters) observable_indices = [] for observable in observables: observable = init_observable(observable) index = self._observable_ids.get(_observable_key(observable)) if index is not None: observable_indices.append(index) else: observable_indices.append(len(self._observables)) self._observable_ids[_observable_key(observable)] = len(self._observables) self._observables.append(observable) job = PrimitiveJob( self._call, circuit_indices, observable_indices, parameter_values, **run_options ) job.submit() return job @staticmethod def _measurement_circuit(num_qubits: int, pauli: Pauli): # Note: if pauli is I for all qubits, this function generates a circuit to measure only # the first qubit. # Although such an operator can be optimized out by interpreting it as a constant (1), # this optimization requires changes in various methods. So it is left as future work. qubit_indices = np.arange(pauli.num_qubits)[pauli.z | pauli.x] if not np.any(qubit_indices): qubit_indices = [0] meas_circuit = QuantumCircuit(num_qubits, len(qubit_indices)) for clbit, i in enumerate(qubit_indices): if pauli.x[i]: if pauli.z[i]: meas_circuit.sdg(i) meas_circuit.h(i) meas_circuit.measure(i, clbit) return meas_circuit, qubit_indices def _preprocessing(self) -> list[tuple[QuantumCircuit, list[QuantumCircuit]]]: """ Preprocessing for evaluation of expectation value using pauli rotation gates. """ preprocessed_circuits = [] for group in self._grouping: circuit = self._circuits[group[0]] observable = self._observables[group[1]] diff_circuits: list[QuantumCircuit] = [] if self._abelian_grouping: for obs in observable.group_commuting(qubit_wise=True): basis = Pauli( (np.logical_or.reduce(obs.paulis.z), np.logical_or.reduce(obs.paulis.x)) ) meas_circuit, indices = self._measurement_circuit(circuit.num_qubits, basis) paulis = PauliList.from_symplectic( obs.paulis.z[:, indices], obs.paulis.x[:, indices], obs.paulis.phase, ) meas_circuit.metadata = { "paulis": paulis, "coeffs": np.real_if_close(obs.coeffs), } diff_circuits.append(meas_circuit) else: for basis, obs in zip(observable.paulis, observable): # type: ignore meas_circuit, indices = self._measurement_circuit(circuit.num_qubits, basis) paulis = PauliList.from_symplectic( obs.paulis.z[:, indices], obs.paulis.x[:, indices], obs.paulis.phase, ) meas_circuit.metadata = { "paulis": paulis, "coeffs": np.real_if_close(obs.coeffs), } diff_circuits.append(meas_circuit) preprocessed_circuits.append((circuit.copy(), diff_circuits)) return preprocessed_circuits def _postprocessing( self, result: list[Result], accum: list[int], metadata: list[dict] ) -> EstimatorResult: """ Postprocessing for evaluation of expectation value using pauli rotation gates. """ counts = _prepare_counts(result) expval_list = [] var_list = [] shots_list = [] for i, j in zip(accum, accum[1:]): combined_expval = 0.0 combined_var = 0.0 for k in range(i, j): meta = metadata[k] paulis = meta["paulis"] coeffs = meta["coeffs"] count = counts[k] expvals, variances = _pauli_expval_with_variance(count, paulis) # Accumulate combined_expval += np.dot(expvals, coeffs) combined_var += np.dot(variances, coeffs**2) expval_list.append(combined_expval) var_list.append(combined_var) shots_list.append(sum(counts[i].values())) metadata = [{"variance": var, "shots": shots} for var, shots in zip(var_list, shots_list)] return EstimatorResult(np.real_if_close(expval_list), metadata) def _bound_pass_manager_run(self, circuits): if self._bound_pass_manager is None: return circuits else: output = self._bound_pass_manager.run(circuits) if not isinstance(output, list): output = [output] return output
def _paulis2inds(paulis: PauliList) -> list[int]: """Convert PauliList to diagonal integers. These are integer representations of the binary string with a 1 where there are Paulis, and 0 where there are identities. """ # Treat Z, X, Y the same nonid = paulis.z | paulis.x inds = [0] * paulis.size # bits are packed into uint8 in little endian # e.g., i-th bit corresponds to coefficient 2^i packed_vals = np.packbits(nonid, axis=1, bitorder="little") for i, vals in enumerate(packed_vals): for j, val in enumerate(vals): inds[i] += val.item() * (1 << (8 * j)) return inds def _parity(integer: int) -> int: """Return the parity of an integer""" return bin(integer).count("1") % 2 def _pauli_expval_with_variance(counts: Counts, paulis: PauliList) -> tuple[np.ndarray, np.ndarray]: """Return array of expval and variance pairs for input Paulis. Note: All non-identity Pauli's are treated as Z-paulis, assuming that basis rotations have been applied to convert them to the diagonal basis. """ # Diag indices size = len(paulis) diag_inds = _paulis2inds(paulis) expvals = np.zeros(size, dtype=float) denom = 0 # Total shots for counts dict for bin_outcome, freq in counts.items(): outcome = int(bin_outcome, 2) denom += freq for k in range(size): coeff = (-1) ** _parity(diag_inds[k] & outcome) expvals[k] += freq * coeff # Divide by total shots expvals /= denom # Compute variance variances = 1 - expvals**2 return expvals, variances