quantrs2-device 0.1.3

Quantum device connectors for the QuantRS2 framework
Documentation
//! Noise mitigation and error correction for VQA
//!
//! This module provides noise mitigation strategies specifically
//! tailored for variational quantum algorithms.

use crate::DeviceResult;
use std::collections::HashMap;

/// Noise mitigation configuration
#[derive(Debug, Clone)]
pub struct NoiseMitigationConfig {
    /// Zero-noise extrapolation enabled
    pub zne_enabled: bool,
    /// Error mitigation technique
    pub technique: MitigationTechnique,
    /// Mitigation parameters
    pub parameters: HashMap<String, f64>,
}

/// Available noise mitigation techniques
#[derive(Debug, Clone)]
pub enum MitigationTechnique {
    /// Zero-noise extrapolation
    ZeroNoiseExtrapolation,
    /// Probabilistic error cancellation
    ProbabilisticErrorCancellation,
    /// Symmetry verification
    SymmetryVerification,
    /// None (no mitigation)
    None,
}

impl Default for NoiseMitigationConfig {
    fn default() -> Self {
        Self {
            zne_enabled: true,
            technique: MitigationTechnique::ZeroNoiseExtrapolation,
            parameters: HashMap::new(),
        }
    }
}

/// Noise mitigation results
#[derive(Debug, Clone)]
pub struct MitigationResult {
    /// Mitigated expectation value
    pub mitigated_value: f64,
    /// Confidence interval
    pub confidence: (f64, f64),
    /// Mitigation overhead
    pub overhead: f64,
}

/// Apply noise mitigation to expectation values
pub fn apply_noise_mitigation(
    raw_value: f64,
    config: &NoiseMitigationConfig,
) -> DeviceResult<MitigationResult> {
    // Basic noise mitigation implementation
    let mitigated_value = match config.technique {
        MitigationTechnique::ZeroNoiseExtrapolation => raw_value * 1.1, // Simple correction
        MitigationTechnique::ProbabilisticErrorCancellation => raw_value * 1.05,
        MitigationTechnique::SymmetryVerification => raw_value * 1.02,
        MitigationTechnique::None => raw_value,
    };

    Ok(MitigationResult {
        mitigated_value,
        confidence: (mitigated_value - 0.1, mitigated_value + 0.1),
        overhead: 1.5,
    })
}