qiskit_nature.second_q.problems.base_problem のソースコード

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# (C) Copyright IBM 2021, 2023.
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"""The Base Problem class."""

from __future__ import annotations

from typing import Callable

import numpy as np
from qiskit_algorithms import EigensolverResult, MinimumEigensolverResult
from qiskit.quantum_info.analysis.z2_symmetries import Z2Symmetries

from qiskit_nature.second_q.mappers import QubitMapper, TaperedQubitMapper
from qiskit_nature.second_q.operators import SparseLabelOp
from qiskit_nature.second_q.hamiltonians import Hamiltonian

from .eigenstate_result import EigenstateResult
from .properties_container import PropertiesContainer

[ドキュメント]class BaseProblem: """The base representation of a second-quantization problem. If none of the specific subclasses of this class fit your use case, you can instantiate this class itself with your custom :class:`.Hamiltonian` instance and pass it into one of the available algorithms. The following attributes can be read and updated once the ``BaseProblem`` object has been constructed. Attributes: properties (PropertiesContainer): a container for additional observable operator factories. """ def __init__(self, hamiltonian: Hamiltonian) -> None: """ Args: driver: A driver encoding the molecule information. transformers: A list of transformations to be applied to the driver result. main_property_name: A main property name for the problem """ self._hamiltonian = hamiltonian self.properties = PropertiesContainer() @property def hamiltonian(self) -> Hamiltonian: """Returns the hamiltonian wrapped by this problem.""" return self._hamiltonian
[ドキュメント] def second_q_ops(self) -> tuple[SparseLabelOp, dict[str, SparseLabelOp]]: """Returns the second quantized operators associated with this problem. Returns: A tuple, with the first object being the main operator and the second being a dictionary of auxiliary operators. """ main_op = self.hamiltonian.second_q_op() aux_ops: dict[str, SparseLabelOp] = {} for prop in self.properties: aux_ops.update(prop.second_q_ops()) return main_op, aux_ops
def _symmetry_sector_locator( self, z2_symmetries: Z2Symmetries, mapper: QubitMapper, ) -> list[int] | None: # pylint: disable=unused-argument """Given the detected Z2Symmetries, it can determine the correct sector of the tapered operators so the correct one can be returned Args: z2_symmetries: the z2 symmetries object. mapper: the ``QubitMapper`` used for the operator conversion that symmetries are to be determined for. Returns: the sector of the tapered operators with the problem solution """ return None
[ドキュメント] def get_tapered_mapper(self, mapper: QubitMapper) -> TaperedQubitMapper: """Builds a ``TaperedQubitMapper`` from one of the mappers. This simplifies the identification of the Pauli operator symmetries and of the symmetry sector in which lies the solution of the problem. Args: mapper: ``QubitMapper`` object implementing the mapping of second quantized operators to Pauli operators. Raises: ValueError: If the mapper is a ``TaperedQubitMapper``. Returns: A ``TaperedQubitMapper`` with pre-built symmetry specifications. """ if isinstance(mapper, TaperedQubitMapper): raise ValueError( "TaperedQubitMapper instance cannot be built from another " "TaperedQubitMapper. If you want to update your TaperedQubitMapper " "instance please build a new one starting from the standard mappers." ) qubit_op, _ = self.second_q_ops() mapped_op = mapper.map(qubit_op) z2_symmetries = Z2Symmetries.find_z2_symmetries(mapped_op) # pylint: disable=assignment-from-none # Known issue for abstract class methods https://github.com/PyCQA/pylint/issues/2559 tapering_values = self._symmetry_sector_locator(z2_symmetries, mapper) z2_symmetries.tapering_values = tapering_values return TaperedQubitMapper(mapper, z2_symmetries)
[ドキュメント] def interpret( self, raw_result: EigenstateResult | EigensolverResult | MinimumEigensolverResult, ) -> EigenstateResult: """Interprets an EigenstateResult in the context of this problem. Args: raw_result: an eigenstate result object. Returns: An interpreted `EigenstateResult` in the form of a subclass of it. The actual type depends on the problem that implements this method. """ return EigenstateResult.from_result(raw_result)
[ドキュメント] def get_default_filter_criterion( self, ) -> Callable[[list | np.ndarray, float, list[float] | None], bool] | None: """Returns a default filter criterion method to filter the eigenvalues computed by the eigen solver. For more information see also :meth:`~qiskit_algorithms.NumPyEigensolver.filter_criterion`. In the fermionic case the default filter ensures that the number of particles is being preserved. """ return None