QEomVQE

class QEomVQE(operator, var_form, optimizer, num_orbitals, num_particles, initial_point=None, max_evals_grouped=1, callback=None, qubit_mapping='parity', two_qubit_reduction=True, is_eom_matrix_symmetric=True, active_occupied=None, active_unoccupied=None, se_list=None, de_list=None, z2_symmetries=None, untapered_op=None, aux_operators=None, quantum_instance=None)

QEomVQE algorithm

Parameters

• operator (LegacyBaseOperator) – qubit operator

• var_form (Union[QuantumCircuit, VariationalForm]) – parameterized variational form.

• optimizer (Optimizer) – the classical optimization algorithm.

• num_orbitals (int) – total number of spin orbitals, has a min. value of 1.

• num_particles (Union[List[int], int]) – number of particles, if it is a list, the first number is alpha and the second number if beta.

• initial_point (Optional[ndarray]) – optimizer initial point, 1-D vector

• max_evals_grouped (int) – max number of evaluations performed simultaneously

• callback (Optional[Callable[[int, ndarray, float, float], None]]) – a callback that can access the intermediate data during the optimization. Internally, four arguments are provided as follows the index of evaluation, parameters of variational form, evaluated mean, evaluated standard deviation.

• qubit_mapping (str) – qubit mapping type

• two_qubit_reduction (bool) – two qubit reduction is applied or not

• is_eom_matrix_symmetric (bool) – is EoM matrix symmetric

• active_occupied (Optional[List[int]]) – list of occupied orbitals to include, indices are 0 to n where n is num particles // 2

• active_unoccupied (Optional[List[int]]) – list of unoccupied orbitals to include, indices are 0 to m where m is (num_orbitals - num particles) // 2

• se_list (Optional[List[List[int]]]) – single excitation list, overwrite the setting in active space

• de_list (Optional[List[List[int]]]) – double excitation list, overwrite the setting in active space

• z2_symmetries (Optional[Z2Symmetries]) – represent the Z2 symmetries

• untapered_op (Optional[LegacyBaseOperator]) – if the operator is tapered, we need untapered operator during building element of EoM matrix

• aux_operators (Optional[List[LegacyBaseOperator]]) – Auxiliary operators to be evaluated at each eigenvalue

• quantum_instance (Union[QuantumInstance, BaseBackend, None]) – Quantum Instance or Backend

Raises

ValueError – invalid parameter

Attributes

QEomVQE.aux_operators	Returns aux operators
QEomVQE.backend	Returns backend.
QEomVQE.expectation	The expectation value algorithm used to construct the expectation measurement from the observable.
QEomVQE.initial_point	Returns initial point
QEomVQE.operator	Returns operator
QEomVQE.optimal_params	The optimal parameters for the variational form.
QEomVQE.optimizer	Returns optimizer
QEomVQE.quantum_instance	Returns quantum instance.
QEomVQE.random	Return a numpy random.
QEomVQE.setting	Prepare the setting of VQE as a string.
QEomVQE.var_form	Returns variational form

Methods

QEomVQE.cleanup_parameterized_circuits()	set parameterized circuits to None
QEomVQE.compute_minimum_eigenvalue([…])	Computes minimum eigenvalue.
QEomVQE.construct_circuit(parameter)	Generate the ansatz circuit and expectation value measurement, and return their runnable composition.
QEomVQE.find_minimum([initial_point, …])	Optimize to find the minimum cost value.
QEomVQE.get_optimal_circuit()	Get the circuit with the optimal parameters.
QEomVQE.get_optimal_cost()	Get the minimal cost or energy found by the VQE.
QEomVQE.get_optimal_vector()	Get the simulation outcome of the optimal circuit.
QEomVQE.get_prob_vector_for_params(…[, …])	Helper function to get probability vectors for a set of params
QEomVQE.get_probabilities_for_counts(counts)	get probabilities for counts
QEomVQE.print_settings()	Preparing the setting of VQE into a string.
QEomVQE.run([quantum_instance])	Execute the algorithm with selected backend.
QEomVQE.set_backend(backend, **kwargs)	Sets backend with configuration.
QEomVQE.supports_aux_operators()	Whether computing the expectation value of auxiliary operators is supported.