scikit_quri.qsvm package

Contents

scikit_quri.qsvm package#

Submodules#

scikit_quri.qsvm.qsvc module#

scikit_quri.qsvm.qsvc.is_real_device(sampling_backend, is_sim)[source]#

Parameters:

sampling_backend (Optional[SamplingBackend]) –
is_sim (bool) –

Return type:

TypeGuard[SamplingBackend]

class scikit_quri.qsvm.qsvc.QSVC(circuit, sim=True)[source]#

Bases: object

Parameters:

circuit (LearningCircuit) –
sim (bool) –

run_circuit(x)[source]#

Parameters:: x (ndarray[tuple[int, ...], dtype[float64]]) –
Return type:: GeneralCircuitQuantumState

fit(x, y, sampling_backend=None, n_shots=1000)[source]#

Parameters:

x (ndarray[tuple[int, ...], dtype[float64]]) –
y (ndarray[tuple[int, ...], dtype[int64]]) –
sampling_backend (Optional[SamplingBackend]) –
n_shots (int) –

predict(xs)[source]#

Parameters:: xs (ndarray[tuple[int, ...], dtype[float64]]) –
Return type:: ndarray[tuple[int, …], dtype[float64]]

scikit_quri.qsvm.qsvr module#

class scikit_quri.qsvm.qsvr.QSVR(circuit)[source]#

Bases: object

Parameters:: circuit (LearningCircuit) –

run_circuit(x)[source]#

Parameters:: x (ndarray[tuple[int, ...], dtype[float64]]) –

fit(x, y)[source]#

Parameters:

x (ndarray[tuple[int, ...], dtype[float64]]) –
y (ndarray[tuple[int, ...], dtype[float64]]) –

predict(xs)[source]#

Parameters:: xs (ndarray[tuple[int, ...], dtype[float64]]) –
Return type:: ndarray[tuple[int, …], dtype[float64]]

Module contents#