Struct CliffordDataRegression 

pub struct CliffordDataRegression {
    pub num_training_circuits: usize,
    pub regression_degree: usize,
    /* private fields */
}

Clifford Data Regression (CDR) error mitigation

CDR learns a noise model from Clifford circuit data and applies it to non-Clifford circuits for improved accuracy.

Reference: Czarnik et al. (2021). “Error mitigation with Clifford quantum-circuit data”

Fields

num_training_circuits: usize

Number of training Clifford circuits

regression_degree: usize

Polynomial degree for regression

Implementations

impl CliffordDataRegression

pub const fn new(num_training_circuits: usize, regression_degree: usize) -> Self

Create a new CDR instance

pub fn train( &mut self, clifford_noisy: &[f64], clifford_ideal: &[f64], ) -> Result<(), QuantRS2Error>

Train the CDR model using Clifford circuit data

Arguments

• clifford_noisy - Noisy expectation values from Clifford circuits
• clifford_ideal - Ideal (simulable) expectation values

pub fn mitigate(&self, noisy_value: f64) -> Result<f64, QuantRS2Error>

Apply learned correction to non-Clifford circuit results

pub fn get_r_squared( &self, test_noisy: &[f64], test_ideal: &[f64], ) -> Result<f64, QuantRS2Error>

Get model quality metric (R² score)

Trait Implementations

impl Clone for CliffordDataRegression

fn clone(&self) -> CliffordDataRegression

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CliffordDataRegression

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

Formats the value using the given formatter.