QuantumKernelTrainer

class QuantumKernelTrainer(quantum_kernel, loss=None, optimizer=None, initial_point=None)

Bases : object

Quantum Kernel Trainer. This class provides utility to train quantum kernel feature map parameters.

Example

# Create 2-qubit feature map
qc = QuantumCircuit(2)

# Vectors of input and trainable user parameters
input_params = ParameterVector("x_par", 2)
training_params = ParameterVector("θ_par", 2)

# Create an initial rotation layer of trainable parameters
for i, param in enumerate(training_params):
    qc.ry(param, qc.qubits[i])

# Create a rotation layer of input parameters
for i, param in enumerate(input_params):
    qc.rz(param, qc.qubits[i])

quant_kernel = TrainableFidelityQuantumKernel(
    feature_map=qc,
    training_parameters=training_params,
)

loss_func = ...
optimizer = ...
initial_point = ...

qk_trainer = QuantumKernelTrainer(
                                quantum_kernel=quant_kernel,
                                loss=loss_func,
                                optimizer=optimizer,
                                initial_point=initial_point,
                                )
qkt_results = qk_trainer.fit(X_train, y_train)
optimized_kernel = qkt_results.quantum_kernel

Paramètres:

• quantum_kernel (TrainableKernel) – a trainable quantum kernel to be trained.

• loss (str | KernelLoss | None) – A loss function available via string is « svc_loss » which is the same as SVCLoss. If a string is passed as the loss function, then the underlying SVCLoss object will exhibit default behavior.

• optimizer (Optimizer | Minimizer | None) – An instance of Optimizer or a callable to be used in training. Refer to Minimizer for more information on the callable protocol. Since no analytical gradient is defined for kernel loss functions, gradient-based optimizers are not recommended for training kernels. When None defaults to SPSA.

• initial_point (Sequence[float] | None) – Initial point from which the optimizer will begin.

Lève:

ValueError – unknown loss function.

Attributes

initial_point

Return initial point

loss

Return the loss object.

optimizer

Return an optimizer to be used in training.

quantum_kernel

Return the quantum kernel object.

Methods

fit(data, labels)

Train the QuantumKernel by minimizing loss over the kernel parameters. The input quantum kernel will not be altered, and an optimized quantum kernel will be returned.

Paramètres:

• data (numpy.ndarray) – (N, D) array of training data, where N is the number of samples and D is the feature dimension

• labels (numpy.ndarray) – (N, 1) array of target values for the training samples

Renvoie:

the results of kernel training

Type renvoyé:

QuantumKernelTrainerResult

Lève:

ValueError – No trainable user parameters specified in quantum kernel