VQEAdapt

class VQEAdapt(operator, var_form_base, optimizer, initial_point=None, excitation_pool=None, threshold=1e-05, delta=1, max_iterations=None, max_evals_grouped=1, aux_operators=None, quantum_instance=None)

Bases: qiskit.aqua.algorithms.vq_algorithm.VQAlgorithm

DEPRECATED. The Adaptive VQE algorithm.

See https://arxiv.org/abs/1812.11173

Parameters

• operator (LegacyBaseOperator) – Qubit operator

• var_form_base (VariationalForm) – base parameterized variational form

• optimizer (Optimizer) – the classical optimizer algorithm

• initial_point (Optional[ndarray]) – optimizer initial point

• excitation_pool (Optional[List[WeightedPauliOperator]]) – list of excitation operators

• threshold (float) – absolute threshold value for gradients, has a min. value of 1e-15.

• delta (float) – finite difference step size for gradient computation, has a min. value of 1e-5.

• max_iterations (Optional[int]) – maximum number of macro iterations of the VQEAdapt algorithm.

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

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

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

Raises

• ValueError – if var_form_base is not an instance of UCCSD.

• See also – qiskit/chemistry/components/variational_forms/uccsd_adapt.py

Methods

cleanup_parameterized_circuits	set parameterized circuits to None
find_minimum	Optimize to find the minimum cost value.
get_optimal_circuit	get optimal circuit
get_optimal_cost	get optimal cost
get_optimal_vector	get optimal vector
get_prob_vector_for_params	Helper function to get probability vectors for a set of params
get_probabilities_for_counts	get probabilities for counts
run	Execute the algorithm with selected backend.
set_backend	Sets backend with configuration.

Attributes

backend

Returns backend.

Return type

Union[Backend, BaseBackend]

initial_point

Returns initial point

Return type

Optional[ndarray]

optimal_params

optimizer

Returns optimizer

Return type

Optional[Optimizer]

quantum_instance

Returns quantum instance.

Return type

Optional[QuantumInstance]

random

Return a numpy random.

var_form

Returns variational form

Return type

Union[QuantumCircuit, VariationalForm, None]