qc2.algorithms.qiskit
=====================

.. py:module:: qc2.algorithms.qiskit

.. autoapi-nested-parse::

   qc2 algorithms package for qiskit.



Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/qc2/algorithms/qiskit/iqpe/index
   /autoapi/qc2/algorithms/qiskit/oo_vqe/index
   /autoapi/qc2/algorithms/qiskit/pebase/index
   /autoapi/qc2/algorithms/qiskit/qpe/index
   /autoapi/qc2/algorithms/qiskit/vqe/index


Classes
-------

.. autoapisummary::

   qc2.algorithms.qiskit.VQE
   qc2.algorithms.qiskit.oo_VQE
   qc2.algorithms.qiskit.QPE
   qc2.algorithms.qiskit.IQPE


Package Contents
----------------

.. py:class:: VQE(qc2data=None, ansatz=None, active_space=None, mapper=None, estimator=None, optimizer=None, reference_state=None, init_params=None, verbose=0)

   Bases: :py:obj:`qc2.algorithms.base.vqe_base.VQEBASE`


   Main class for the VQE algorithm with Qiskit-Nature.

   This class initializes and executes the VQE algorithm using specified
   quantum components like ansatz, optimizer, and estimator.

   .. attribute:: active_space

      Describes the active space for quantum
      simulation. Defaults to ``ActiveSpace((2, 2), 2)``.

      :type: ActiveSpace

   .. attribute:: mapper

      Strategy for fermionic-to-qubit mapping.
      Defaults to :class:`qiskit.JordanWignerMapper`.

      :type: QubitMapper

   .. attribute:: estimator

      Method for estimating the
      expectation value. Defaults to :class:`qiskit.Estimator`

      :type: BaseEstimator

   .. attribute:: optimizer

      Optimization routine for circuit
      variational parameters. Defaults to
      :class:`qiskit_algorithms.SLSQP`.

      :type: qiskit.Optmizer

   .. attribute:: reference_state

      Reference state for the VQE
      algorithm. Defaults to :class:`qiskit.HartreeFock`.

      :type: QuantumCircuit

   .. attribute:: ansatz

      The ansatz for the VQE algorithm.
      Defaults to :class:`qiskit.UCCSD`.

      :type: UCC

   .. attribute:: params

      List of initial VQE circuit parameters.
      Defaults to a list with entries of zero.

      :type: List

   .. attribute:: verbose

      Verbosity level. Defaults to 0.

      :type: int


   .. py:attribute:: active_space


   .. py:attribute:: mapper


   .. py:attribute:: estimator


   .. py:attribute:: optimizer


   .. py:attribute:: reference_state


   .. py:attribute:: ansatz


   .. py:attribute:: params


   .. py:attribute:: verbose
      :value: 0



   .. py:method:: _get_default_reference(active_space: qc2.algorithms.utils.active_space.ActiveSpace, mapper: qiskit_nature.second_q.mappers.QubitMapper) -> qiskit.circuit.QuantumCircuit
      :staticmethod:


      Set up the default reference state circuit based on Hartree Fock.

      :param active_space: description of the active space.
      :type active_space: ActiveSpace
      :param mapper: mapper class instance.
      :type mapper: mapper

      :returns: Hartree-Fock circuit as the reference state.
      :rtype: QuantumCircuit



   .. py:method:: _get_default_ansatz(active_space: qc2.algorithms.utils.active_space.ActiveSpace, mapper: qiskit_nature.second_q.mappers.QubitMapper, reference_state: qiskit.circuit.QuantumCircuit) -> qiskit_nature.second_q.circuit.library.UCC
      :staticmethod:


      Set up the default UCC ansatz from a Hartree Fock reference state.

      :param active_space: Description of the active space.
      :type active_space: ActiveSpace
      :param mapper: Mapper class instance.
      :type mapper: QubitMapper
      :param reference_state: Reference state circuit.
      :type reference_state: QuantumCircuit

      :returns: UCC ansatz quantum circuit.
      :rtype: UCC



   .. py:method:: _get_default_init_params(nparams: List) -> List
      :staticmethod:


      Generates a list of initial circuit parameters for the ansatz.

      :param nparams: Number of parameters in the ansatz.
      :type nparams: int

      :returns: List of initial parameter values (all zeros).
      :rtype: List[float]



   .. py:method:: run(*args, **kwargs) -> qc2.algorithms.algorithms_results.VQEResults

      Executes the VQE algorithm.

      :param \*args: Variable length argument list to be passed to
                     the :class:`qiskit_algorithm.VQE` class.
      :param \*\*kwargs: Arbitrary keyword arguments to be passed to
                         the :class:`qiskit_algorithm.VQE` class.

      :returns:     An instance of :class:`qc2.algorithms.qiskit.vqe.VQEResults`
                    class with all VQE info.
      :rtype: VQEResults

      **Example**

      >>> from ase.build import molecule
      >>> from qc2.ase import PySCF
      >>> from qc2.data import qc2Data
      >>> from qc2.algorithms.qiskit import VQE
      >>> from qc2.algorithms.utils import ActiveSpace
      >>>
      >>> mol = molecule('H2O')
      >>>
      >>> hdf5_file = 'h2o.hdf5'
      >>> qc2data = qc2Data(hdf5_file, mol, schema='qcschema')
      >>> qc2data.molecule.calc = PySCF()
      >>> qc2data.run()
      >>> qc2data.algorithm = VQE(
      ...     active_space=ActiveSpace(
      ...         num_active_electrons=(2, 2),
      ...         num_active_spatial_orbitals=4
      ...     ),
      ...     optimizer=SLSQP(),
      ...     estimator=Estimator(),
      ... )
      >>> results = qc2data.algorithm.run()



.. py:class:: oo_VQE(qc2data=None, ansatz=None, active_space=None, mapper=None, estimator=None, optimizer=None, reference_state=None, init_circuit_params=None, init_orbital_params=None, freeze_active=False, max_iterations=50, conv_tol=1e-07, verbose=0)

   Bases: :py:obj:`qc2.algorithms.qiskit.vqe.VQE`


   Main class for orbital-optimized VQE with Qiskit-Nature.

   This class extends the VQE class to include orbital optimization. It
   supports customized ansatzes, active space definitions, qubit mapping
   strategies, estimation methods, and optimization routines. Orbital
   optimization is performed alongside VQE parameter optimization using
   analytic first and second derivatives

   .. attribute:: freeze_active

      If True, freezes the active
      space during optimization.

      :type: bool

   .. attribute:: orbital_params

      List of orbital optimization parameters.
      Defaults to a list with entries of zero.

      :type: List

   .. attribute:: circuit_params

      List of VQE circuit parameters.
      Defaults to a list with entries of zero.

      :type: List

   .. attribute:: oo_problem

      An instance of
      :class:`~qc2.algorithms.utils.orbital_optimization.OrbitalOptimization`
      problem class. Defaults to None.

      :type: OrbitalOptimization

   .. attribute:: max_iterations

      Maximum number of iterations for the combined
      circuit-orbitals parameters optimization. Defaults to 50.

      :type: int

   .. attribute:: conv_tol

      Convergence tolerance for the optimization.
      Defaults to 1e-7

      :type: float

   .. attribute:: verbose

      Verbosity level. Defaults to 0.

      :type: int


   .. py:attribute:: freeze_active
      :value: False



   .. py:attribute:: orbital_params
      :value: None



   .. py:attribute:: circuit_params


   .. py:attribute:: oo_problem
      :value: None



   .. py:attribute:: max_iterations
      :value: 50



   .. py:attribute:: conv_tol
      :value: 1e-07



   .. py:method:: run() -> qc2.algorithms.algorithms_results.OOVQEResults

      Optimizes both the circuit and orbital parameters.

      :returns:     An instance of :class:`qc2.algorithms.qiskit.vqe.OOVQEResults`
                    class with all oo-VQE info.
      :rtype: OOVQEResults

      **Example**

      >>> from ase.build import molecule
      >>> from qc2.ase import PySCF
      >>> from qc2.data import qc2Data
      >>> from qc2.algorithms.qiskit import oo_VQE
      >>> from qc2.algorithms.utils import ActiveSpace
      >>>
      >>> mol = molecule('H2O')
      >>>
      >>> hdf5_file = 'h2o.hdf5'
      >>> qc2data = qc2Data(hdf5_file, mol, schema='qcschema')
      >>> qc2data.molecule.calc = PySCF()
      >>> qc2data.run()
      >>> qc2data.algorithm = oo_VQE(
      ...     active_space=ActiveSpace(
      ...         num_active_electrons=(2, 2),
      ...         num_active_spatial_orbitals=4
      ...     ),
      ...     mapper="jw",
      ...     optimizer=SLSQP(),
      ...     estimator=Estimator(),
      ... )
      >>> results = qc2data.algorithm.run()



   .. py:method:: _circuit_optimization(theta: List, kappa: List) -> Tuple[List, float]

      Get total energy and best circuit parameters for a given kappa.

      :param theta: List with circuit variational parameters.
      :type theta: List
      :param kappa: List with orbital rotation parameters.
      :type kappa: List

      :returns:     Optimized circuit parameters and associated energy.
      :rtype: Tuple[List, float]



   .. py:method:: _get_energy_from_parameters(theta: List, kappa: List) -> float

      Calculates total energy given circuit and orbital parameters.

      :param theta: List with circuit variational parameters.
      :type theta: List
      :param kappa: List with orbital rotation parameters.
      :type kappa: List

      :returns:     Total ground-state energy for a given circuit
                    and orbital parameters.
      :rtype: float



   .. py:method:: _get_rdms(theta: List, sum_spin=True) -> Tuple[numpy.ndarray, numpy.ndarray]

      Calculates 1- and 2-electron reduced density matrices (RDMs).

      :param theta: circuit parameters at which
                    RDMs are calculated.
      :type theta: List
      :param sum_spin: If True, the spin-summed 1-RDM and 2-RDM will be
                       returned. If False, the full 1-RDM and 2-RDM will be returned.
                       Defaults to True.
      :type sum_spin: bool

      :returns:     1- and 2-RDMs.
      :rtype: Tuple[np.ndarray, np.ndarray]



.. py:class:: QPE(qc2data=None, num_evaluation_qubits=None, active_space=None, mapper=None, sampler=None, reference_state=None, verbose=0)

   Bases: :py:obj:`qc2.algorithms.qiskit.pebase.PEBase`


   .. py:attribute:: num_evaluation_qubits
      :value: 3



   .. py:attribute:: solver


.. py:class:: IQPE(qc2data=None, num_iterations=None, active_space=None, mapper=None, sampler=None, reference_state=None, verbose=0)

   Bases: :py:obj:`qc2.algorithms.qiskit.pebase.PEBase`


   .. py:attribute:: num_iterations
      :value: 3



   .. py:attribute:: solver