qiskit.chemistry.algorithms.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)[source]¶

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

__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)[source]¶

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

`__init__`(operator, var_form_base, optimizer)	type operator `LegacyBaseOperator`
`cleanup_parameterized_circuits`()	set parameterized circuits to None
`find_minimum`([initial_point, var_form, …])	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`(counts)	get probabilities for counts
`run`([quantum_instance])	Execute the algorithm with selected backend.
`set_backend`(backend, **kwargs)	Sets backend with configuration.

Attributes

`backend`	Returns backend.
`initial_point`	Returns initial point
`optimal_params`	returns optimal parameters
`optimizer`	Returns optimizer
`quantum_instance`	Returns quantum instance.
`random`	Return a numpy random.
`var_form`	Returns variational form

property backend¶

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()[source]¶: get optimal circuit

get_optimal_cost()[source]¶: get optimal cost

get_optimal_vector()[source]¶: 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

property initial_point¶

Returns initial point

Return type: Optional[ndarray]

property optimal_params¶: returns optimal parameters

property optimizer¶

Returns optimizer

Return type: Optional[Optimizer]

property quantum_instance¶

Returns quantum instance.

Return type: Optional[QuantumInstance]

property random¶: 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

property var_form¶

Returns variational form

Return type: Union[QuantumCircuit, VariationalForm, None]