Struct QuantumKernelApproximation 

pub struct QuantumKernelApproximation<State = Untrained> { /* private fields */ }

Quantum Kernel Approximation

Simulates quantum feature maps using classical computation to approximate quantum kernels. The quantum kernel is defined as:

K(x, x’) = |⟨φ(x)|φ(x’)⟩|²

where |φ(x)⟩ is a quantum feature map encoding classical data x.

Mathematical Background

Quantum feature maps encode classical data into quantum states:

• Pauli-Z: U(x) = exp(-i Σ_j x_j Z_j)
• Pauli-ZZ: U(x) = exp(-i Σ_{j,k} (π - x_j)(π - x_k) Z_j Z_k)
• Amplitude: |ψ⟩ = Σ_i √(x_i/||x||) |i⟩

The resulting kernel often provides exponential feature space dimension advantages over classical kernels for certain problems.

Examples

use sklears_kernel_approximation::quantum_kernel_methods::{QuantumKernelApproximation, QuantumKernelConfig};
use scirs2_core::ndarray::array;
use sklears_core::traits::{Fit, Transform};

let config = QuantumKernelConfig::default();
let qkernel = QuantumKernelApproximation::new(config);

let X = array![[1.0, 2.0], [3.0, 4.0]];
let fitted = qkernel.fit(&X, &()).unwrap();
let features = fitted.transform(&X).unwrap();

Implementations

impl QuantumKernelApproximation<Untrained>

pub fn new(config: QuantumKernelConfig) -> Self

Create a new quantum kernel approximation

pub fn with_qubits(n_qubits: usize) -> Self

Create with default configuration

pub fn feature_map(self, feature_map: QuantumFeatureMap) -> Self

Set feature map type

pub fn circuit_depth(self, depth: usize) -> Self

Set circuit depth

pub fn entanglement(self, pattern: EntanglementPattern) -> Self

Set entanglement pattern

impl QuantumKernelApproximation<Trained>

pub fn x_train(&self) -> &Array2<Float>

Get the training data

pub fn feature_basis(&self) -> &Array2<Float>

Get the feature basis

pub fn compute_kernel_matrix(&self, x: &Array2<Float>) -> Array2<Float>

Compute quantum kernel matrix explicitly (slow, for small datasets)

Trait Implementations

impl<State: Clone> Clone for QuantumKernelApproximation<State>

fn clone(&self) -> QuantumKernelApproximation<State>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<State: Debug> Debug for QuantumKernelApproximation<State>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Estimator for QuantumKernelApproximation<Untrained>

type Config = QuantumKernelConfig

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>, ()> for QuantumKernelApproximation<Untrained>

type Fitted = QuantumKernelApproximation<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> Result<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for QuantumKernelApproximation<Trained>

fn transform(&self, x: &Array2<Float>) -> Result<Array2<Float>>

Transform the input data