pyriemann_qiskit.optimization.docplex.QAOACVOptimizer¶

class pyriemann_qiskit.optimization.docplex.QAOACVOptimizer(create_mixer=<function create_mixer_rotational_X_gates.<locals>.mixer_X>, n_reps=3, quantum_instance=None, optimizer=<qiskit_algorithms.optimizers.spsa.SPSA object>)[source]¶

QAOA with continuous variables.

Parameters:

create_mixer (Callable[int, QuantumCircuit], default=create_mixer_rotational_X_gates(0)) – A delegate that takes into input an angle and returns a QuantumCircuit.
n_reps (int, default=3) – The number of repetitions for the QAOA ansatz. It defines how many time Mixer and Cost operators will be repeated in the circuit.
quantum_instance (QuantumInstance, default=None) – A quantum backend instance. If None, AerSimulator will be used.
optimizer (SciPyOptimizer, default=SPSA()) – An instance of a scipy optimizer to find the optimal weights for the parametric circuit (ansatz).

Notes

Added in version 0.4.0.

x_¶

Indices of the loss function.

Type:: list[int]

y_¶

Cost computed by the loss function.

Type:: list[int]

run_time_¶

Time taken by the optimizer

Type:: float

optim_params_¶

The optimal rotation for the gate in the circuit.

Type:: list[float]

minimum_¶

The value of the objective function with the optimal parameters.

Type:: float

state_vector_¶

State vector of the optimized quantum circuit (optimal parameters assigned to the parametric gates).

Type:: StateVector

See also

pyQiskitOptimizer, create_mixer_rotational_X_gates

__init__(create_mixer=<function create_mixer_rotational_X_gates.<locals>.mixer_X>, n_reps=3, quantum_instance=None, optimizer=<qiskit_algorithms.optimizers.spsa.SPSA object>)[source]¶

Methods

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