KernelLoss

class KernelLoss

Bases: ABC

Abstract base class for computing the loss of a kernel function. Unlike many loss functions, which only take into account the labels and predictions of a model, kernel loss functions may be a function of internal model parameters or quantities that are generated during training.

Methods

abstract evaluate(parameter_values, quantum_kernel, data, labels)

An abstract method for evaluating the loss of a kernel function on a labeled dataset.

Parameters:

• parameter_values (Sequence[float]) – An array of values to assign to the user params

• quantum_kernel (TrainableKernel) – A trainable quantum kernel object to evaluate

• data (ndarray) – An (N, M) matrix containing the data N = # samples, M = dimension of data

• labels (ndarray) – A length-N array containing the truth labels

Returns:

A loss value

Return type:

float