qiskit.chemistry.algorithms.VQEAdapt

__init__(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)

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

Returns backend.

Return type

Union[Backend, BaseBackend]

cleanup_parameterized_circuits()

set parameterized circuits to None

find_minimum(initial_point=None, var_form=None, cost_fn=None, optimizer=None, gradient_fn=None)

Optimize to find the minimum cost value.

Parameters

• initial_point (Optional[ndarray]) – If not None will be used instead of any initial point supplied via constructor. If None and None was supplied to constructor then a random point will be used if the optimizer requires an initial point.
• var_form (Union[QuantumCircuit, VariationalForm, None]) – If not None will be used instead of any variational form supplied via constructor.
• cost_fn (Optional[Callable]) – If not None will be used instead of any cost_fn supplied via constructor.
• optimizer (Optional[Optimizer]) – If not None will be used instead of any optimizer supplied via constructor.
• gradient_fn (Optional[Callable]) – Optional gradient function for optimizer

Returns

Optimized variational parameters, and corresponding minimum cost value.

Return type

dict

Raises

ValueError – invalid input

get_optimal_circuit()

get optimal circuit

get_optimal_cost()

get optimal cost

get_optimal_vector()

get optimal vector

get_prob_vector_for_params(construct_circuit_fn, params_s, quantum_instance, construct_circuit_args=None)

Helper function to get probability vectors for a set of params

get_probabilities_for_counts(counts)

get probabilities for counts

Returns initial point

Return type

Optional[ndarray]

returns optimal parameters

Returns optimizer

Return type

Optional[Optimizer]

Returns quantum instance.

Return type

Optional[QuantumInstance]

Return a numpy random.

run(quantum_instance=None, **kwargs)

Execute the algorithm with selected backend.

Parameters

• quantum_instance (Union[QuantumInstance, Backend, BaseBackend, None]) – the experimental setting.
• kwargs (dict) – kwargs

Returns

results of an algorithm.

Return type

dict

Raises

AquaError – If a quantum instance or backend has not been provided

set_backend(backend, **kwargs)

Sets backend with configuration.

Return type

None

Returns variational form

Return type

Union[QuantumCircuit, VariationalForm, None]