pyriemann_qiskit.classification.QuanticVQC¶

class pyriemann_qiskit.classification.QuanticVQC(optimizer=<qiskit_algorithms.optimizers.spsa.SPSA object>, gen_var_form=<function gen_two_local.<locals>.<lambda>>, quantum=True, q_account_token=None, verbose=True, shots=1024, gen_feature_map=<function gen_zz_feature_map.<locals>.<lambda>>, seed=None)[source]¶

Variational quantum classifier

This class implements a variational quantum classifier (VQC). Note that there is no classical version of this algorithm. This will always run on a quantum computer (simulated or not).

Parameters:

optimizer (Optimizer, default=SPSA) – The classical optimizer to use. See [3] for details.
gen_var_form (Callable[int, QuantumCircuit | VariationalForm], default=Callable[int, TwoLocal]) – Function generating a variational form instance.
quantum (bool, default=True) –
- If true will run on local or remote backend (depending on q_account_token value).
- If false, will perform classical computing instead.
q_account_token (string | None, default=None) – If quantum is True and q_account_token provided, the classification task will be running on a IBM quantum backend. If load_account is provided, the classifier will use the previous token saved with IBMProvider.save_account().
verbose (bool, default=True) – If true, will output all intermediate results and logs
shots (int, default=1024) – Number of repetitions of each circuit, for sampling
gen_feature_map (Callable[[int, str], QuantumCircuit | FeatureMap], default=Callable[int, ZZFeatureMap]) – Function generating a feature map to encode data into a quantum state.
seed (int | None, default=None) – Random seed for the simulation.