Source code for qiskit_nature.second_q.formats.qcschema.qc_schema

# This code is part of a Qiskit project.
# (C) Copyright IBM 2022, 2023.
# 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
# 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.

"""The QCSchema (output) dataclass."""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Sequence, cast

# Sphinx is somehow unable to resolve this forward reference which gets inherited from QCSchemaInput
# Thus, we import it here manually to ensure the documentation can be built
from typing import Mapping  # pylint: disable=unused-import

import h5py

from qiskit_nature.version import __version__

from .qc_error import QCError
from .qc_model import QCModel
from .qc_schema_input import QCSchemaInput
from .qc_properties import QCProperties
from .qc_provenance import QCProvenance
from .qc_topology import QCTopology
from .qc_wavefunction import QCWavefunction

[docs]@dataclass class QCSchema(QCSchemaInput): """The full QCSchema as a dataclass. For more information refer to [here]( """ provenance: QCProvenance """An instance of :class:`QCProvenance`.""" return_result: float | Sequence[float] """The primary result of the computation. Its value depends on the type of computation (see also `driver`).""" success: bool """Whether the computation was successful.""" properties: QCProperties """An instance of :class:`QCProperties`.""" error: QCError | None = None """An instance of :class:`QCError` if the computation was not successful (`success = False`).""" wavefunction: QCWavefunction | None = None """An instance of :class:`QCWavefunction`."""
[docs] @classmethod def from_dict(cls, data: dict[str, Any]) -> QCSchema: error: QCError | None = None if "error" in data.keys(): error = QCError(**data.pop("error")) model = QCModel(**data.pop("model")) molecule = QCTopology(**data.pop("molecule")) provenance = QCProvenance(**data.pop("provenance")) properties = QCProperties(**data.pop("properties")) wavefunction: QCWavefunction | None = None if "wavefunction" in data.keys(): wavefunction = QCWavefunction.from_dict(data.pop("wavefunction")) return cls( **data, error=error, model=model, molecule=molecule, provenance=provenance, properties=properties, wavefunction=wavefunction, )
[docs] def to_hdf5(self, group: h5py.Group) -> None: group.attrs["schema_name"] = self.schema_name group.attrs["schema_version"] = self.schema_version group.attrs["driver"] = self.driver group.attrs["return_result"] = self.return_result group.attrs["success"] = self.success molecule_group = group.require_group("molecule") self.molecule.to_hdf5(molecule_group) model_group = group.require_group("model") self.model.to_hdf5(model_group) provenance_group = group.require_group("provenance") self.provenance.to_hdf5(provenance_group) properties_group = group.require_group("properties") if self.error is not None: error_group = group.require_group("error") self.error.to_hdf5(error_group) if self.wavefunction is not None: wavefunction_group = group.require_group("wavefunction") self.wavefunction.to_hdf5(wavefunction_group) keywords_group = group.require_group("keywords") for key, value in self.keywords.items(): keywords_group.attrs[key] = value
@classmethod def _from_hdf5_group(cls, h5py_group: h5py.Group) -> QCSchemaInput: data = dict(h5py_group.attrs.items()) data["molecule"] = cast(QCTopology, QCTopology.from_hdf5(h5py_group["molecule"])) data["model"] = cast(QCModel, QCModel.from_hdf5(h5py_group["model"])) data["provenance"] = cast(QCProvenance, QCProvenance.from_hdf5(h5py_group["provenance"])) data["properties"] = cast(QCProperties, QCProperties.from_hdf5(h5py_group["properties"])) if "error" in h5py_group.keys(): data["error"] = cast(QCError, QCError.from_hdf5(h5py_group["error"])) if "wavefunction" in h5py_group.keys(): data["wavefunction"] = cast( QCWavefunction, QCWavefunction.from_hdf5(h5py_group["wavefunction"]) ) data["keywords"] = dict(h5py_group["keywords"].attrs.items()) return cls(**data)