squlearn.kernel.loss.negative_log_likelihood.NLL
- class squlearn.kernel.loss.negative_log_likelihood.NLL(sigma=0.0)
Negative log likelihood loss function. This class can be used to compute the negative log likelihood loss function for a given quantum kernel \(K_{\theta}\) with variational parameters \(\theta\). The definition of the function is taken from Equation 5.8 Chapter 5.4 of Ref. [1].
The log-likelihood function is defined as:
\[L(\theta) = -\frac{1}{2} log(|K_{\theta} + \sigmaI|)-\frac{1}{2} y^{T}(K_{\theta} + \sigmaI)^{-1}y-\frac{n}{2} log(2\pi)\]- Parameters:
sigma – (float), default=0.0: Hyperparameter for the regularization strength.
References
Methods:
- compute(parameter_values: ndarray, data: ndarray, labels: ndarray) float
Compute the negative log likelihood loss function.
- Parameters:
parameter_values (np.ndarray) – The parameter values for the variational quantum kernel parameters.
data (np.ndarray) – The training data to be used for the kernel matrix.
labels (np.ndarray) – The training labels.
- Returns:
The negative log likelihood loss value.
- Return type:
float
- set_quantum_kernel(quantum_kernel: KernelMatrixBase) None
Set the quantum kernel matrix to be used in the loss.
- Parameters:
quantum_kernel (KernelMatrixBase) – The quantum kernel matrix to be used in the loss.