QrTrainableFidelityQuantumKernel module

class QrTrainableFidelityQuantumKernel(*, feature_map, fidelity, training_parameters, enforce_psd, evaluate_duplicates)

An implementation of the quantum kernel that is based on the BaseStateFidelity algorithm and provides ability to train it.

This class is a derivative of the Qiskit Machine Learning Package TrainableFidelityQuantumKernel. Please refer to the class for more documentation.

Finding good quantum kernels for a specific machine learning task is a big challenge in quantum machine learning. One way to choose the kernel is to add trainable parameters to the feature map, which can be used to fine-tune the kernel.

This kernel has trainable parameters \(\theta\) that can be bound using training algorithms. The kernel entries are given as

\[K_{\theta}(x,y) = |\langle \phi_{\theta}(x) | \phi_{\theta}(y) \rangle|^2\]

__init__(self, *, feature_map, fidelity, training_parameters, enforce_psd, evaluate_duplicates)

Args:

feature_map: Parameterized circuit to be used as the feature map. If None is given,

ZZFeatureMap is used with two qubits. If there’s

a mismatch in the number of qubits of the feature map and the number of features

in the dataset, then the kernel will try to adjust the feature map to reflect the

number of features.

fidelity: An instance of the

BaseStateFidelity primitive to be used

to compute fidelity between states. Default is

ComputeUncompute which is created on

top of the reference sampler defined by Sampler.

training_parameters: Iterable containing Parameter objects

which correspond to quantum gates on the feature map circuit which may be tuned.

If users intend to tune feature map parameters to find optimal values, this field

should be set.

enforce_psd: Project to the closest positive semidefinite matrix if x = y.

Default True.

evaluate_duplicates: Defines a strategy how kernel matrix elements are evaluated if

duplicate samples are found. Possible values are:

- all means that all kernel matrix elements are evaluated, even the diagonal

ones when training. This may introduce additional noise in the matrix.

- off_diagonal when training the matrix diagonal is set to 1, the rest

elements are fully evaluated, e.g., for two identical samples in the

dataset. When inferring, all elements are evaluated. This is the default

value.

- none when training the diagonal is set to 1 and if two identical samples

are found in the dataset the corresponding matrix element is set to 1.

When inferring, matrix elements for identical samples are set to 1.