quantrs2_device/topological/

error_correction.rs

//! Error correction for topological quantum computing
//!
//! This module implements topological error correction schemes that leverage
//! the inherent error protection of topological quantum states.

use super::{
    topological_codes::{
        ColorCode, ErrorCorrection, SurfaceCode, SyndromeMeasurement, TopologicalCodeType,
        TopologicalDecoder,
    },
    Anyon, TopologicalCharge, TopologicalDevice, TopologicalError, TopologicalQubit,
    TopologicalResult,
};
use scirs2_core::random::prelude::*;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, SystemTime, UNIX_EPOCH};

/// Topological error correction manager
pub struct TopologicalErrorCorrector {
    /// Type of topological code being used
    code_type: TopologicalCodeType,
    /// Code distance
    code_distance: usize,
    /// Surface code instance (if applicable)
    surface_code: Option<SurfaceCode>,
    /// Color code instance (if applicable)
    color_code: Option<ColorCode>,
    /// Decoder for syndrome interpretation
    decoder: Box<dyn TopologicalDecoder + Send + Sync>,
    /// Syndrome measurement history
    syndrome_history: VecDeque<SyndromeRound>,
    /// Error correction configuration
    config: ErrorCorrectionConfig,
}

/// Configuration for topological error correction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorCorrectionConfig {
    /// Syndrome measurement frequency (Hz)
    pub syndrome_frequency: f64,
    /// Number of syndrome rounds to keep in history
    pub syndrome_history_size: usize,
    /// Threshold for syndrome correlation
    pub correlation_threshold: f64,
    /// Enable real-time error correction
    pub real_time_correction: bool,
    /// Maximum correction attempts per syndrome
    pub max_correction_attempts: usize,
    /// Minimum confidence for applying corrections
    pub min_correction_confidence: f64,
    /// Enable syndrome pattern analysis
    pub enable_pattern_analysis: bool,
}

impl Default for ErrorCorrectionConfig {
    fn default() -> Self {
        Self {
            syndrome_frequency: 1000.0, // 1 kHz
            syndrome_history_size: 100,
            correlation_threshold: 0.8,
            real_time_correction: true,
            max_correction_attempts: 3,
            min_correction_confidence: 0.9,
            enable_pattern_analysis: true,
        }
    }
}

/// A round of syndrome measurements
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SyndromeRound {
    /// Round ID
    pub round_id: usize,
    /// Timestamp of the round
    pub timestamp: f64,
    /// All syndrome measurements in this round
    pub measurements: Vec<SyndromeMeasurement>,
    /// Applied corrections (if any)
    pub corrections: Vec<ErrorCorrection>,
    /// Success of the correction
    pub correction_success: Option<bool>,
}

/// Anyon error tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnyonError {
    /// Anyon ID
    pub anyon_id: usize,
    /// Type of error
    pub error_type: AnyonErrorType,
    /// Error probability
    pub probability: f64,
    /// Detection timestamp
    pub detection_time: f64,
    /// Associated syndrome measurements
    pub syndrome_ids: Vec<usize>,
}

/// Types of errors that can affect anyons
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum AnyonErrorType {
    /// Anyon creation/annihilation error
    CreationAnnihilation,
    /// Braiding error
    BraidingError,
    /// Fusion error
    FusionError,
    /// Measurement error
    MeasurementError,
    /// Thermal fluctuation
    ThermalFluctuation,
    /// Decoherence
    Decoherence,
}

/// Error correction statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorCorrectionStats {
    /// Total syndrome rounds processed
    pub total_rounds: usize,
    /// Total errors detected
    pub errors_detected: usize,
    /// Total corrections applied
    pub corrections_applied: usize,
    /// Successful corrections
    pub successful_corrections: usize,
    /// Average correction confidence
    pub average_confidence: f64,
    /// Logical error rate
    pub logical_error_rate: f64,
    /// Physical error rate
    pub physical_error_rate: f64,
}

impl TopologicalErrorCorrector {
    /// Create a new topological error corrector
    pub fn new(
        code_type: TopologicalCodeType,
        code_distance: usize,
        config: ErrorCorrectionConfig,
    ) -> TopologicalResult<Self> {
        let (surface_code, color_code) = match code_type {
            TopologicalCodeType::SurfaceCode | TopologicalCodeType::PlanarSurfaceCode => {
                (Some(SurfaceCode::new(code_distance)?), None)
            }
            TopologicalCodeType::ColorCode => (None, Some(ColorCode::new(code_distance)?)),
            _ => (None, None),
        };

        // Create appropriate decoder
        let decoder: Box<dyn TopologicalDecoder + Send + Sync> = match code_type {
            TopologicalCodeType::SurfaceCode | TopologicalCodeType::PlanarSurfaceCode => Box::new(
                super::topological_codes::MWPMDecoder::new(code_distance, 0.01),
            ),
            _ => {
                // Default decoder
                Box::new(super::topological_codes::MWPMDecoder::new(
                    code_distance,
                    0.01,
                ))
            }
        };

        Ok(Self {
            code_type,
            code_distance,
            surface_code,
            color_code,
            decoder,
            syndrome_history: VecDeque::with_capacity(config.syndrome_history_size),
            config,
        })
    }

    /// Perform a syndrome measurement round
    pub async fn perform_syndrome_measurement(
        &mut self,
        device: &TopologicalDevice,
        round_id: usize,
    ) -> TopologicalResult<SyndromeRound> {
        let timestamp = SystemTime::now()
            .duration_since(UNIX_EPOCH)
            .expect("System time should be after UNIX epoch")
            .as_secs_f64();

        let mut measurements = Vec::new();

        // Measure stabilizers based on code type
        match &self.surface_code {
            Some(code) => {
                // Measure all stabilizers
                let all_stabilizers = code.get_all_stabilizers();

                for (idx, stabilizer) in all_stabilizers.iter().enumerate() {
                    // Simulate syndrome measurement
                    let outcome = if thread_rng().gen::<f64>() < 0.05 {
                        -1 // Error detected
                    } else {
                        1 // No error
                    };

                    measurements.push(SyndromeMeasurement {
                        stabilizer_id: stabilizer.stabilizer_id,
                        outcome,
                        timestamp,
                        fidelity: 0.99,
                    });
                }
            }
            None => {
                // For other code types, create mock measurements
                for i in 0..10 {
                    let outcome = if thread_rng().gen::<f64>() < 0.05 {
                        -1
                    } else {
                        1
                    };
                    measurements.push(SyndromeMeasurement {
                        stabilizer_id: i,
                        outcome,
                        timestamp,
                        fidelity: 0.99,
                    });
                }
            }
        }

        let syndrome_round = SyndromeRound {
            round_id,
            timestamp,
            measurements,
            corrections: Vec::new(),
            correction_success: None,
        };

        // Add to history
        self.syndrome_history.push_back(syndrome_round.clone());
        if self.syndrome_history.len() > self.config.syndrome_history_size {
            self.syndrome_history.pop_front();
        }

        Ok(syndrome_round)
    }

    /// Analyze syndrome and apply corrections
    pub async fn analyze_and_correct(
        &mut self,
        device: &mut TopologicalDevice,
        syndrome_round: &SyndromeRound,
    ) -> TopologicalResult<Vec<ErrorCorrection>> {
        // Detect errors from syndrome
        let error_syndromes: Vec<_> = syndrome_round
            .measurements
            .iter()
            .filter(|m| m.outcome == -1)
            .cloned()
            .collect();

        if error_syndromes.is_empty() {
            return Ok(Vec::new());
        }

        // Decode syndrome to find corrections
        let corrections = self
            .decoder
            .decode_syndrome(&error_syndromes, self.code_distance)?;

        // Filter corrections by confidence
        let high_confidence_corrections: Vec<_> = corrections
            .into_iter()
            .filter(|c| c.confidence >= self.config.min_correction_confidence)
            .collect();

        // Apply corrections to the device
        for correction in &high_confidence_corrections {
            self.apply_correction_to_device(device, correction).await?;
        }

        Ok(high_confidence_corrections)
    }

    /// Apply a correction to the topological device
    async fn apply_correction_to_device(
        &self,
        device: &mut TopologicalDevice,
        correction: &ErrorCorrection,
    ) -> TopologicalResult<()> {
        // For now, just simulate the correction
        // In practice, this would involve physical operations on anyons

        for (qubit_id, operator) in correction.qubits.iter().zip(&correction.corrections) {
            match operator {
                super::topological_codes::PauliOperator::X
                | super::topological_codes::PauliOperator::Z
                | super::topological_codes::PauliOperator::Y
                | super::topological_codes::PauliOperator::I => {
                    // Placeholder: Apply correction via braiding
                    // This is simplified - would require complex braiding sequences
                    // X: X correction via braiding
                    // Z: Z correction via braiding
                    // Y: Y correction (combination of X and Z)
                    // I: Identity - no correction needed
                }
            }
        }

        Ok(())
    }

    /// Analyze syndrome patterns for error prediction
    pub fn analyze_syndrome_patterns(&self) -> Vec<SyndromePattern> {
        if !self.config.enable_pattern_analysis || self.syndrome_history.len() < 3 {
            return Vec::new();
        }

        let mut patterns = Vec::new();

        // Look for repeating syndrome patterns
        for window_size in 2..=5 {
            if self.syndrome_history.len() < window_size * 2 {
                continue;
            }

            for start in 0..=(self.syndrome_history.len() - window_size * 2) {
                if start + window_size * 2 <= self.syndrome_history.len() {
                    let pattern1: Vec<_> = self
                        .syndrome_history
                        .iter()
                        .skip(start)
                        .take(window_size)
                        .cloned()
                        .collect();
                    let pattern2: Vec<_> = self
                        .syndrome_history
                        .iter()
                        .skip(start + window_size)
                        .take(window_size)
                        .cloned()
                        .collect();

                    if self.patterns_match(&pattern1, &pattern2) {
                        patterns.push(SyndromePattern {
                            pattern_id: patterns.len(),
                            rounds: pattern1.iter().map(|r| r.round_id).collect(),
                            confidence: 0.8, // Would be calculated properly
                            prediction: "Repeating error pattern detected".to_string(),
                        });
                    }
                }
            }
        }

        patterns
    }

    /// Check if two syndrome patterns match
    fn patterns_match(&self, pattern1: &[SyndromeRound], pattern2: &[SyndromeRound]) -> bool {
        if pattern1.len() != pattern2.len() {
            return false;
        }

        let threshold = self.config.correlation_threshold;
        let mut matches = 0;
        let total = pattern1.len();

        for (round1, round2) in pattern1.iter().zip(pattern2.iter()) {
            if self.rounds_similar(round1, round2, threshold) {
                matches += 1;
            }
        }

        (matches as f64 / total as f64) >= threshold
    }

    /// Check if two syndrome rounds are similar
    fn rounds_similar(
        &self,
        round1: &SyndromeRound,
        round2: &SyndromeRound,
        threshold: f64,
    ) -> bool {
        if round1.measurements.len() != round2.measurements.len() {
            return false;
        }

        let mut similar_measurements = 0;
        let total = round1.measurements.len();

        for (m1, m2) in round1.measurements.iter().zip(&round2.measurements) {
            if m1.stabilizer_id == m2.stabilizer_id && m1.outcome == m2.outcome {
                similar_measurements += 1;
            }
        }

        (similar_measurements as f64 / total as f64) >= threshold
    }

    /// Calculate error correction statistics
    pub fn calculate_statistics(&self) -> ErrorCorrectionStats {
        let total_rounds = self.syndrome_history.len();
        let mut errors_detected = 0;
        let mut corrections_applied = 0;
        let mut successful_corrections = 0;
        let mut total_confidence = 0.0;

        for round in &self.syndrome_history {
            // Count errors (syndrome violations)
            errors_detected += round
                .measurements
                .iter()
                .filter(|m| m.outcome == -1)
                .count();

            // Count corrections
            corrections_applied += round.corrections.len();

            // Count successful corrections
            if round.correction_success == Some(true) {
                successful_corrections += 1;
            }

            // Sum confidence values
            total_confidence += round.corrections.iter().map(|c| c.confidence).sum::<f64>();
        }

        let average_confidence = if corrections_applied > 0 {
            total_confidence / corrections_applied as f64
        } else {
            0.0
        };

        // Simplified error rate calculations
        let physical_error_rate = if total_rounds > 0 {
            errors_detected as f64 / (total_rounds * 10) as f64 // Assuming 10 stabilizers per round
        } else {
            0.0
        };

        let logical_error_rate = if corrections_applied > 0 {
            (corrections_applied - successful_corrections) as f64 / corrections_applied as f64
        } else {
            0.0
        };

        ErrorCorrectionStats {
            total_rounds,
            errors_detected,
            corrections_applied,
            successful_corrections,
            average_confidence,
            logical_error_rate,
            physical_error_rate,
        }
    }

    /// Get recent syndrome history
    pub fn get_recent_syndromes(&self, count: usize) -> Vec<&SyndromeRound> {
        self.syndrome_history.iter().rev().take(count).collect()
    }

    /// Clear syndrome history
    pub fn clear_history(&mut self) {
        self.syndrome_history.clear();
    }
}

/// Detected syndrome pattern
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SyndromePattern {
    pub pattern_id: usize,
    pub rounds: Vec<usize>,
    pub confidence: f64,
    pub prediction: String,
}

/// Real-time error correction monitor
pub struct RealTimeErrorMonitor {
    corrector: TopologicalErrorCorrector,
    monitoring_active: bool,
    measurement_interval: Duration,
}

impl RealTimeErrorMonitor {
    /// Create a new real-time error monitor
    pub const fn new(corrector: TopologicalErrorCorrector, measurement_interval: Duration) -> Self {
        Self {
            corrector,
            monitoring_active: false,
            measurement_interval,
        }
    }

    /// Start real-time monitoring
    pub async fn start_monitoring(
        &mut self,
        mut device: TopologicalDevice,
    ) -> TopologicalResult<()> {
        self.monitoring_active = true;
        let mut round_id = 0;

        while self.monitoring_active {
            // Perform syndrome measurement
            let syndrome_round = self
                .corrector
                .perform_syndrome_measurement(&device, round_id)
                .await?;

            // Analyze and apply corrections
            let corrections = self
                .corrector
                .analyze_and_correct(&mut device, &syndrome_round)
                .await?;

            // Log corrections if any were applied
            if !corrections.is_empty() {
                println!(
                    "Applied {} corrections in round {}",
                    corrections.len(),
                    round_id
                );
            }

            round_id += 1;
            tokio::time::sleep(self.measurement_interval).await;
        }

        Ok(())
    }

    /// Stop monitoring
    pub const fn stop_monitoring(&mut self) {
        self.monitoring_active = false;
    }

    /// Get error correction statistics
    pub fn get_statistics(&self) -> ErrorCorrectionStats {
        self.corrector.calculate_statistics()
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::topological::{
        FusionRuleSet, NonAbelianAnyonType, TopologicalCapabilities, TopologicalCharge,
        TopologicalDevice, TopologicalSystemType,
    };

    #[test]
    fn test_error_corrector_creation() {
        let config = ErrorCorrectionConfig::default();
        let corrector = TopologicalErrorCorrector::new(TopologicalCodeType::SurfaceCode, 3, config)
            .expect("Error corrector creation should succeed");

        assert_eq!(corrector.code_distance, 3);
        assert!(corrector.surface_code.is_some());
    }

    #[tokio::test]
    async fn test_syndrome_measurement() {
        let config = ErrorCorrectionConfig::default();
        let mut corrector =
            TopologicalErrorCorrector::new(TopologicalCodeType::SurfaceCode, 3, config)
                .expect("Error corrector creation should succeed");

        // Create a mock device
        let system_type = TopologicalSystemType::NonAbelian {
            anyon_type: NonAbelianAnyonType::Fibonacci,
            fusion_rules: FusionRuleSet::fibonacci(),
        };
        let capabilities = TopologicalCapabilities {
            max_anyons: 50,
            max_qubits: 5,
            supported_anyons: vec![TopologicalCharge::fibonacci_tau()],
            available_operations: vec![],
            braiding_fidelity: 0.999,
            fusion_fidelity: 0.999,
            topological_gap: 1.0,
            coherence_length: 100.0,
        };
        let device = TopologicalDevice::new(system_type, FusionRuleSet::fibonacci(), capabilities);

        let syndrome_round = corrector
            .perform_syndrome_measurement(&device, 0)
            .await
            .expect("Syndrome measurement should succeed");
        assert_eq!(syndrome_round.round_id, 0);
        assert!(!syndrome_round.measurements.is_empty());
    }

    #[test]
    fn test_statistics_calculation() {
        let config = ErrorCorrectionConfig::default();
        let corrector = TopologicalErrorCorrector::new(TopologicalCodeType::SurfaceCode, 3, config)
            .expect("Error corrector creation should succeed");

        let stats = corrector.calculate_statistics();
        assert_eq!(stats.total_rounds, 0);
        assert_eq!(stats.errors_detected, 0);
    }

    #[test]
    fn test_pattern_analysis() {
        let config = ErrorCorrectionConfig::default();
        let corrector = TopologicalErrorCorrector::new(TopologicalCodeType::SurfaceCode, 3, config)
            .expect("Error corrector creation should succeed");

        let patterns = corrector.analyze_syndrome_patterns();
        // Should be empty since there's no history
        assert!(patterns.is_empty());
    }
}