Struct CliffordDataRegression 

pub struct CliffordDataRegression {
    pub num_training_circuits: usize,
    pub regression_method: RegressionMethod,
    pub fit_intercept: bool,
}

Clifford Data Regression (CDR) implementation

CDR uses near-Clifford circuits where exact simulation is possible to learn a correction model for noisy circuit results.

Fields

num_training_circuits: usize

Number of near-Clifford training circuits

regression_method: RegressionMethod

Regression method

fit_intercept: bool

Include intercept in regression

Implementations

impl CliffordDataRegression

pub fn new( num_training_circuits: usize, regression_method: RegressionMethod, ) -> Self

Create a new CDR instance

pub fn train<F, G>( &self, noisy_executor: F, ideal_executor: G, ) -> Result<CDRModel>

where F: Fn(usize) -> Result<f64>, G: Fn(usize) -> Result<f64>,

Train the CDR model using Clifford circuits

pub fn mitigate(&self, model: &CDRModel, noisy_value: f64) -> Result<CDRResult>

Apply CDR to mitigate errors

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.

impl Default for CliffordDataRegression

fn default() -> Self

Returns the “default value” for a type.