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)
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,Backend,BaseBackend,None]) – Quantum Instance or Backend
Raises
ValueError – invalid parameter
__init__
__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)
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,Backend,BaseBackend,None]) – Quantum Instance or Backend
Raises
ValueError – invalid parameter
Methods
__init__(operator, var_form, optimizer, …) | type operatorLegacyBaseOperator |
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 |
aux_operators
Returns aux operators
Return type
Optional[List[Optional[OperatorBase]]]
backend
Returns backend.
Return type
Union[Backend, BaseBackend]
cleanup_parameterized_circuits
cleanup_parameterized_circuits()
set parameterized circuits to None
compute_minimum_eigenvalue
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.
Parameters
- 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
Return type
MinimumEigensolverResult
Returns
MinimumEigensolverResult
construct_circuit
construct_circuit(parameter)
Return the circuits used to compute the expectation value.
Parameters
parameter (Union[List[float], List[Parameter], ndarray]) – Parameters for the ansatz circuit.
Return type
List[QuantumCircuit]
Returns
A list of the circuits used to compute the expectation value.
construct_expectation
construct_expectation(parameter)
Generate the ansatz circuit and expectation value measurement, and return their runnable composition.
Parameters
parameter (Union[List[float], List[Parameter], ndarray]) – Parameters for the ansatz circuit.
Return type
OperatorBase
Returns
The Operator equalling the measurement of the ansatz StateFn by the Observable’s expectation StateFn.
Raises
AquaError – If no operator has been provided.
expectation
The expectation value algorithm used to construct the expectation measurement from the observable.
Return type
ExpectationBase
find_minimum
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 the circuit with the optimal parameters.
Return type
QuantumCircuit
get_optimal_cost
get_optimal_cost()
Get the minimal cost or energy found by the VQE.
Return type
float
get_optimal_vector
get_optimal_vector()
Get the simulation outcome of the optimal circuit.
Return type
Union[List[float], Dict[str, int]]
get_prob_vector_for_params
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
get_probabilities_for_counts(counts)
get probabilities for counts
initial_point
Returns initial point
Return type
Optional[ndarray]
operator
Returns operator
Return type
Optional[OperatorBase]
optimal_params
The optimal parameters for the variational form.
Return type
List[float]
optimizer
Returns optimizer
Return type
Optional[Optimizer]
print_settings
print_settings()
Preparing the setting of VQE into a string.
Returns
the formatted setting of VQE
Return type
str
quantum_instance
Returns quantum instance.
Return type
Optional[QuantumInstance]
random
Return a numpy random.
run
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
set_backend(backend, **kwargs)
Sets backend with configuration.
Return type
None
setting
Prepare the setting of VQE as a string.
supports_aux_operators
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.
Return type
bool
Returns
True if aux_operator expectations can be evaluated, False otherwise
var_form
Returns variational form
Return type
Union[QuantumCircuit, VariationalForm, None]