pyriemann_qiskit.optimization.docplex.NaiveQAOAOptimizer¶

class pyriemann_qiskit.optimization.docplex.NaiveQAOAOptimizer(upper_bound=7, quantum_instance=None, optimizer=<qiskit_algorithms.optimizers.slsqp.SLSQP object>, initial_points=[0.0, 0.0])[source]¶

Wrapper for the quantum optimizer QAOA.

Parameters:

upper_bound (int, default=7) – The maximum integer value for matrix normalization.
quantum_instance (QuantumInstance, default=None) – A quantum backend instance. If None, AerSimulator will be used.
optimizer (SciPyOptimizer, default=SLSQP()) – An instance of a scipy optimizer to find the optimal weights for the parametric circuit (ansatz).
initial_points (Tuple[int, int], default=[0.0, 0.0].) – Starting parameters (beta and gamma) for the QAOA.

Notes

Added in version 0.0.2.

Changed in version 0.0.4: add get_weights method.

Changed in version 0.3.0: add evaluated_values_ attribute. add optimizer parameter.

evaluated_values_¶

Training curve values.

Type:: list[int]

See also

pyQiskitOptimizer

__init__(upper_bound=7, quantum_instance=None, optimizer=<qiskit_algorithms.optimizers.slsqp.SLSQP object>, initial_points=[0.0, 0.0])[source]¶

Methods

`__init__`([upper_bound, quantum_instance, ...])
`convert_spdmat`(X)	Convert a SPD matrix
`get_weights`(prob, classes)	Get weights variable
`solve`(prob[, reshape])	Solve the docplex problem.
`spdmat_var`(prob, channels, name)	Create docplex matrix variable