TrainableFidelityQuantumKernel¶
- class TrainableFidelityQuantumKernel(*, feature_map=None, fidelity=None, training_parameters=None, enforce_psd=True, evaluate_duplicates='off_diagonal')[sorgente]¶
An implementation of the quantum kernel that is based on the
BaseStateFidelity
algorithm and provides ability to train it.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
\[K_{\theta}(x,y) = |\langle \phi_{\theta}(x) | \phi_{\theta}(y) \rangle|^2\]- Parametri:
feature_map (QuantumCircuit | None) – 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 (BaseStateFidelity | None) – An instance of the
BaseStateFidelity
primitive to be used to compute fidelity between states. Default isComputeUncompute
which is created on top of the reference sampler defined bySampler
.training_parameters (ParameterVector | Sequence[Parameter] | None) – 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 (bool) – Project to the closest positive semidefinite matrix if
x = y
. DefaultTrue
.evaluate_duplicates (str) –
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.
Attributes
Methods
evaluate
(x_vec[, y_vec])Construct kernel matrix for given data.