Struct QuantumClassifier 

pub struct QuantumClassifier { /* private fields */ }

Quantum-Classical Hybrid Classifier

A high-level classifier that wraps QuantumSVMModel with optional per-feature standardisation preprocessing. The preprocessing computes z-score normalisation (zero mean, unit variance) from training data and applies it consistently at test time.

Example

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_spatial::quantum_inspired::algorithms::quantum_machine_learning::QuantumClassifier;

let x = Array2::from_shape_vec((4, 2), vec![
    0.0, 0.0,
    1.0, 0.0,
    0.0, 1.0,
    5.0, 5.0,
])?;
let y = Array1::from_vec(vec![1.0, 1.0, 1.0, -1.0]);

let mut clf = QuantumClassifier::new(4, 1.0, true);
clf.fit(&x, &y)?;

let x_test = Array2::from_shape_vec((1, 2), vec![0.5, 0.5])?;
let preds = clf.predict(&x_test)?;
assert_eq!(preds.len(), 1);

Implementations

impl QuantumClassifier

pub fn new(n_qubits: usize, regularization: f64, standardise: bool) -> Self

Construct a new QuantumClassifier.

Arguments

• n_qubits - Passed through to QuantumSVMModel.
• regularization - SVM regularisation constant C.
• standardise - If true, z-score normalise features before fitting.

pub fn fit(&mut self, x: &Array2<f64>, y: &Array1<f64>) -> SpatialResult<()>

Train the classifier.

If standardise was set to true, this computes feature statistics from x and stores them for consistent use at predict time.

Errors

Propagates errors from QuantumSVMModel::fit.

pub fn predict(&self, x: &Array2<f64>) -> SpatialResult<Array1<f64>>

Predict labels for new samples.

Applies the same normalisation (if any) that was used during training.

Errors

Propagates errors from QuantumSVMModel::predict.

pub fn num_support_vectors(&self) -> usize

Return the number of support vectors in the underlying SVM.

Trait Implementations

impl Clone for QuantumClassifier

fn clone(&self) -> QuantumClassifier

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for QuantumClassifier

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

Formats the value using the given formatter.