`qc2.algorithms.qiskit`

qc2 algorithms package for qiskit.

Submodules

Package Contents

Classes

`VQE`	Main class for the VQE algorithm with Qiskit-Nature.
`oo_VQE`	Main class for orbital-optimized VQE with Qiskit-Nature.

class qc2.algorithms.qiskit.VQE(qc2data=None, ansatz=None, active_space=None, mapper=None, estimator=None, optimizer=None, reference_state=None, init_params=None, verbose=0)[source]

Bases: 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.

active_space

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

Type:: ActiveSpace

mapper

Strategy for fermionic-to-qubit mapping. Defaults to qiskit.JordanWignerMapper.

Type:: QubitMapper

estimator

Method for estimating the expectation value. Defaults to qiskit.Estimator

Type:: BaseEstimator

optimizer

Optimization routine for circuit variational parameters. Defaults to qiskit_algorithms.SLSQP.

Type:: qiskit.Optmizer

reference_state

Reference state for the VQE algorithm. Defaults to qiskit.HartreeFock.

Type:: QuantumCircuit

ansatz

The ansatz for the VQE algorithm. Defaults to qiskit.UCCSD.

Type:: UCC

params

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

Type:: List

verbose

Verbosity level. Defaults to 0.

Type:: int

static _get_default_reference(active_space: qc2.algorithms.utils.active_space.ActiveSpace, mapper: qiskit_nature.second_q.mappers.QubitMapper) → qiskit.circuit.QuantumCircuit[source]

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

Parameters:

active_space (ActiveSpace) – description of the active space.
mapper (mapper) – mapper class instance.

Returns:

Hartree-Fock circuit as the reference state.

Return type:

QuantumCircuit

static _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[source]

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

Parameters:

active_space (ActiveSpace) – Description of the active space.
mapper (QubitMapper) – Mapper class instance.
reference_state (QuantumCircuit) – Reference state circuit.

Returns:

UCC ansatz quantum circuit.

Return type:

UCC

static _get_default_init_params(nparams: List) → List[source]

Generates a list of initial circuit parameters for the ansatz.

Parameters:: nparams (int) – Number of parameters in the ansatz.
Returns:: List of initial parameter values (all zeros).
Return type:: List[float]

run(*args, **kwargs) → qc2.algorithms.algorithms_results.VQEResults[source]

Executes the VQE algorithm.

Parameters:

*args – Variable length argument list to be passed to the qiskit_algorithm.VQE class.
**kwargs – Arbitrary keyword arguments to be passed to the qiskit_algorithm.VQE class.

Returns:

An instance of qc2.algorithms.qiskit.vqe.VQEResults class with all VQE info.

Return type:

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()

class qc2.algorithms.qiskit.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)[source]

Bases: 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

freeze_active

If True, freezes the active space during optimization.

Type:: bool

orbital_params

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

Type:: List

circuit_params

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

Type:: List

oo_problem

An instance of OrbitalOptimization problem class. Defaults to None.

Type:: OrbitalOptimization

max_iterations

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

Type:: int

conv_tol

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

Type:: float

verbose

Verbosity level. Defaults to 0.

Type:: int

run() → qc2.algorithms.algorithms_results.OOVQEResults[source]

Optimizes both the circuit and orbital parameters.

Returns:: An instance of qc2.algorithms.qiskit.vqe.OOVQEResults class with all oo-VQE info.
Return type:: 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()

_circuit_optimization(theta: List, kappa: List) → Tuple[List, float][source]

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

Parameters:

theta (List) – List with circuit variational parameters.
kappa (List) – List with orbital rotation parameters.

Returns:

Optimized circuit parameters and associated energy.

Return type:

Tuple[List, float]

_get_energy_from_parameters(theta: List, kappa: List) → float[source]

Calculates total energy given circuit and orbital parameters.

Parameters:

theta (List) – List with circuit variational parameters.
kappa (List) – List with orbital rotation parameters.

Returns:

Total ground-state energy for a given circuit and orbital parameters.

Return type:

float

_get_rdms(theta: List, sum_spin=True) → Tuple[numpy.ndarray, numpy.ndarray][source]

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

Parameters:

theta (List) – circuit parameters at which RDMs are calculated.
sum_spin (bool) – 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.

Returns:

1- and 2-RDMs.

Return type:

Tuple[np.ndarray, np.ndarray]

qc2.algorithms.qiskit

Submodules

Package Contents

Classes

`qc2.algorithms.qiskit`