qiskit.chemistry.algorithms.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)[ソース]¶

QEomVQE algorithm

パラメータ

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, Backend, BaseBackend, None]) – Quantum Instance or Backend

例外

ValueError – invalid parameter

__init__(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)[ソース]¶

パラメータ

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, Backend, BaseBackend, None]) – Quantum Instance or Backend

例外

ValueError – invalid parameter

Methods

`__init__`(operator, var_form, optimizer, …)	type operator `LegacyBaseOperator`
`cleanup_parameterized_circuits`()	set parameterized circuits to None
`compute_minimum_eigenvalue`([operator, …])	Computes minimum eigenvalue.
`construct_circuit`(parameter)	Return the circuits used to compute the expectation value.
`construct_expectation`(parameter)	Generate the ansatz circuit and expectation value measurement, and return their runnable composition.
`find_minimum`([initial_point, var_form, …])	Optimize to find the minimum cost value.
`get_optimal_circuit`()	Get the circuit with the optimal parameters.
`get_optimal_cost`()	Get the minimal cost or energy found by the VQE.
`get_optimal_vector`()	Get the simulation outcome of the optimal circuit.
`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
`print_settings`()	Preparing the setting of VQE into a string.
`run`([quantum_instance])	Execute the algorithm with selected backend.
`set_backend`(backend, **kwargs)	Sets backend with configuration.
`supports_aux_operators`()	Whether computing the expectation value of auxiliary operators is supported.

Attributes

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

property aux_operators¶

Returns aux operators

戻り値の型: Optional[List[Optional[OperatorBase]]]

property backend¶

Returns backend.

戻り値の型: Union[Backend, BaseBackend]

cleanup_parameterized_circuits()¶: set parameterized circuits to None

compute_minimum_eigenvalue(operator=None, aux_operators=None)¶

Computes minimum eigenvalue. Operator and aux_operators can be supplied here and if not None will override any already set into algorithm so it can be reused with different operators. While an operator is required by algorithms, aux_operators are optional. To 『remove』 a previous aux_operators array use an empty list here.

パラメータ

operator (Union[OperatorBase, LegacyBaseOperator, None]) – If not None replaces operator in algorithm
aux_operators (Optional[List[Union[OperatorBase, LegacyBaseOperator, None]]]) – If not None replaces aux_operators in algorithm

戻り値の型

MinimumEigensolverResult

戻り値

MinimumEigensolverResult

construct_circuit(parameter)¶

Return the circuits used to compute the expectation value.

パラメータ: parameter (Union[List[float], List[Parameter], ndarray]) – Parameters for the ansatz circuit.
戻り値の型: List[QuantumCircuit]
戻り値: A list of the circuits used to compute the expectation value.

construct_expectation(parameter)¶

Generate the ansatz circuit and expectation value measurement, and return their runnable composition.

パラメータ: parameter (Union[List[float], List[Parameter], ndarray]) – Parameters for the ansatz circuit.
戻り値の型: OperatorBase
戻り値: The Operator equalling the measurement of the ansatz StateFn by the Observable’s expectation StateFn.
例外: AquaError – If no operator has been provided.

property expectation¶

The expectation value algorithm used to construct the expectation measurement from the observable.

戻り値の型: ExpectationBase

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

Optimize to find the minimum cost value.

パラメータ

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

戻り値

Optimized variational parameters, and corresponding minimum cost value.

戻り値の型

dict

例外

ValueError – invalid input

get_optimal_circuit()¶

Get the circuit with the optimal parameters.

戻り値の型: QuantumCircuit

get_optimal_cost()¶

Get the minimal cost or energy found by the VQE.

戻り値の型: float

get_optimal_vector()¶

Get the simulation outcome of the optimal circuit.

戻り値の型: Union[List[float], Dict[str, int]]

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

戻り値の型: Optional[ndarray]

property operator¶

Returns operator

戻り値の型: Optional[OperatorBase]

property optimal_params¶

The optimal parameters for the variational form.

戻り値の型: List[float]

property optimizer¶

Returns optimizer

戻り値の型: Optional[Optimizer]

print_settings()¶

Preparing the setting of VQE into a string.

戻り値: the formatted setting of VQE
戻り値の型: str

property quantum_instance¶

Returns quantum instance.

戻り値の型: Optional[QuantumInstance]

property random¶: Return a numpy random.

run(quantum_instance=None, **kwargs)¶

Execute the algorithm with selected backend.

パラメータ

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

戻り値

results of an algorithm.

戻り値の型

dict

例外

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

set_backend(backend, **kwargs)¶

Sets backend with configuration.

戻り値の型: None

property setting¶: Prepare the setting of VQE as a string.

classmethod supports_aux_operators()¶

Whether computing the expectation value of auxiliary operators is supported.

If the minimum eigensolver computes an eigenstate of the main operator then it can compute the expectation value of the aux_operators for that state. Otherwise they will be ignored.

戻り値の型: bool
戻り値: True if aux_operator expectations can be evaluated, False otherwise

property var_form¶

Returns variational form

戻り値の型: Union[QuantumCircuit, VariationalForm, None]