quantrs2-core 0.1.3

//! Real-time error correction with hardware integration
//!
//! This module provides interfaces and implementations for real-time quantum error correction
//! that can be integrated with quantum hardware control systems.

use super::pauli::{Pauli, PauliString};
use crate::error::{QuantRS2Error, QuantRS2Result};

// Use the SyndromeDecoder trait from parent module
use super::SyndromeDecoder;
use std::collections::VecDeque;
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant};

/// Hardware interface trait for quantum error correction
pub trait QuantumHardwareInterface: Send + Sync {
    /// Get syndrome measurements from hardware
    fn measure_syndromes(&self) -> QuantRS2Result<Vec<bool>>;

    /// Apply error correction operations to hardware
    fn apply_correction(&self, correction: &PauliString) -> QuantRS2Result<()>;

    /// Get hardware error rates and characterization data
    fn get_error_characteristics(&self) -> QuantRS2Result<HardwareErrorCharacteristics>;

    /// Check if hardware is ready for error correction
    fn is_ready(&self) -> bool;

    /// Get hardware latency statistics
    fn get_latency_stats(&self) -> QuantRS2Result<LatencyStats>;
}

/// Hardware error characteristics for adaptive error correction
#[derive(Debug, Clone)]
pub struct HardwareErrorCharacteristics {
    /// Single-qubit error rates (T1, T2, gate errors)
    pub single_qubit_error_rates: Vec<f64>,
    /// Two-qubit gate error rates
    pub two_qubit_error_rates: Vec<f64>,
    /// Measurement error rates
    pub measurement_error_rates: Vec<f64>,
    /// Correlated error patterns
    pub correlated_errors: Vec<CorrelatedErrorPattern>,
    /// Error rate temporal variation
    pub temporal_variation: f64,
}

/// Correlated error pattern for adaptive decoding
#[derive(Debug, Clone)]
pub struct CorrelatedErrorPattern {
    pub qubits: Vec<usize>,
    pub probability: f64,
    pub pauli_pattern: PauliString,
}

/// Performance and latency statistics
#[derive(Debug, Clone)]
pub struct LatencyStats {
    pub syndrome_measurement_time: Duration,
    pub decoding_time: Duration,
    pub correction_application_time: Duration,
    pub total_cycle_time: Duration,
    pub throughput_hz: f64,
}

/// Real-time syndrome stream processor
pub struct SyndromeStreamProcessor {
    buffer: Arc<Mutex<VecDeque<SyndromePacket>>>,
    decoder: Arc<dyn SyndromeDecoder + Send + Sync>,
    hardware: Arc<dyn QuantumHardwareInterface>,
    performance_monitor: Arc<RwLock<PerformanceMonitor>>,
    config: RealTimeConfig,
}

/// Syndrome packet with timing information
#[derive(Debug, Clone)]
pub struct SyndromePacket {
    pub syndrome: Vec<bool>,
    pub timestamp: Instant,
    pub sequence_number: u64,
    pub measurement_fidelity: f64,
}

/// Real-time error correction configuration
#[derive(Debug, Clone)]
pub struct RealTimeConfig {
    pub max_latency: Duration,
    pub buffer_size: usize,
    pub parallel_workers: usize,
    pub adaptive_threshold: bool,
    pub hardware_feedback: bool,
    pub performance_logging: bool,
}

impl Default for RealTimeConfig {
    fn default() -> Self {
        Self {
            max_latency: Duration::from_micros(100), // 100μs for fast correction
            buffer_size: 1000,
            parallel_workers: 4,
            adaptive_threshold: true,
            hardware_feedback: true,
            performance_logging: true,
        }
    }
}

/// Performance monitoring for real-time error correction
#[derive(Debug, Clone)]
pub struct PerformanceMonitor {
    pub cycles_processed: u64,
    pub errors_corrected: u64,
    pub false_positives: u64,
    pub latency_histogram: Vec<Duration>,
    pub throughput_samples: VecDeque<f64>,
    pub start_time: Instant,
}

impl PerformanceMonitor {
    pub fn new() -> Self {
        Self {
            cycles_processed: 0,
            errors_corrected: 0,
            false_positives: 0,
            latency_histogram: Vec::new(),
            throughput_samples: VecDeque::new(),
            start_time: Instant::now(),
        }
    }

    pub fn record_cycle(&mut self, latency: Duration, error_corrected: bool) {
        self.cycles_processed += 1;
        if error_corrected {
            self.errors_corrected += 1;
        }
        self.latency_histogram.push(latency);

        // Calculate current throughput
        let elapsed = self.start_time.elapsed();
        if elapsed.as_secs_f64() > 0.0 {
            let throughput = self.cycles_processed as f64 / elapsed.as_secs_f64();
            self.throughput_samples.push_back(throughput);

            // Keep only recent samples
            if self.throughput_samples.len() > 100 {
                self.throughput_samples.pop_front();
            }
        }
    }

    pub fn average_latency(&self) -> Duration {
        if self.latency_histogram.is_empty() {
            return Duration::from_nanos(0);
        }

        let total_nanos: u64 = self
            .latency_histogram
            .iter()
            .map(|d| d.as_nanos() as u64)
            .sum();
        Duration::from_nanos(total_nanos / self.latency_histogram.len() as u64)
    }

    pub fn current_throughput(&self) -> f64 {
        self.throughput_samples.back().copied().unwrap_or(0.0)
    }

    pub fn error_correction_rate(&self) -> f64 {
        if self.cycles_processed == 0 {
            0.0
        } else {
            self.errors_corrected as f64 / self.cycles_processed as f64
        }
    }
}

impl Default for PerformanceMonitor {
    fn default() -> Self {
        Self::new()
    }
}

impl SyndromeStreamProcessor {
    /// Create a new real-time syndrome stream processor
    pub fn new(
        decoder: Arc<dyn SyndromeDecoder + Send + Sync>,
        hardware: Arc<dyn QuantumHardwareInterface>,
        config: RealTimeConfig,
    ) -> Self {
        Self {
            buffer: Arc::new(Mutex::new(VecDeque::with_capacity(config.buffer_size))),
            decoder,
            hardware,
            performance_monitor: Arc::new(RwLock::new(PerformanceMonitor::new())),
            config,
        }
    }

    /// Start real-time error correction processing
    pub fn start_processing(&self) -> QuantRS2Result<thread::JoinHandle<()>> {
        let buffer = Arc::clone(&self.buffer);
        let decoder = Arc::clone(&self.decoder);
        let hardware = Arc::clone(&self.hardware);
        let monitor = Arc::clone(&self.performance_monitor);
        let config = self.config.clone();

        let handle = thread::spawn(move || {
            let mut sequence_number = 0u64;

            loop {
                let cycle_start = Instant::now();

                // Check if hardware is ready
                if !hardware.is_ready() {
                    thread::sleep(Duration::from_micros(10));
                    continue;
                }

                // Measure syndromes from hardware
                match hardware.measure_syndromes() {
                    Ok(syndrome) => {
                        let packet = SyndromePacket {
                            syndrome: syndrome.clone(),
                            timestamp: Instant::now(),
                            sequence_number,
                            measurement_fidelity: 0.99, // Would be measured from hardware
                        };

                        // Add to buffer
                        {
                            let mut buf = buffer.lock().expect("Syndrome buffer lock poisoned");
                            if buf.len() >= config.buffer_size {
                                buf.pop_front(); // Remove oldest if buffer full
                            }
                            buf.push_back(packet);
                        }

                        // Process syndrome if not all zeros (no error)
                        let has_error = syndrome.iter().any(|&x| x);
                        let mut error_corrected = false;

                        if has_error {
                            match decoder.decode(&syndrome) {
                                Ok(correction) => match hardware.apply_correction(&correction) {
                                    Ok(()) => {
                                        error_corrected = true;
                                    }
                                    Err(e) => {
                                        eprintln!("Failed to apply correction: {e}");
                                    }
                                },
                                Err(e) => {
                                    eprintln!("Decoding failed: {e}");
                                }
                            }
                        }

                        // Record performance metrics
                        let cycle_time = cycle_start.elapsed();
                        {
                            let mut mon =
                                monitor.write().expect("Performance monitor lock poisoned");
                            mon.record_cycle(cycle_time, error_corrected);
                        }

                        // Check latency constraint
                        if cycle_time > config.max_latency {
                            eprintln!(
                                "Warning: Error correction cycle exceeded max latency: {:?} > {:?}",
                                cycle_time, config.max_latency
                            );
                        }

                        sequence_number += 1;
                    }
                    Err(e) => {
                        eprintln!("Failed to measure syndromes: {e}");
                        thread::sleep(Duration::from_micros(10));
                    }
                }

                // Small sleep to prevent busy waiting
                thread::sleep(Duration::from_micros(1));
            }
        });

        Ok(handle)
    }

    /// Get current performance statistics
    pub fn get_performance_stats(&self) -> PerformanceMonitor {
        (*self
            .performance_monitor
            .read()
            .expect("Performance monitor lock poisoned"))
        .clone()
    }

    /// Get syndrome buffer status
    pub fn get_buffer_status(&self) -> (usize, usize) {
        let buffer = self.buffer.lock().expect("Syndrome buffer lock poisoned");
        (buffer.len(), self.config.buffer_size)
    }
}

/// Adaptive threshold decoder that learns from hardware feedback
pub struct AdaptiveThresholdDecoder {
    base_decoder: Arc<dyn SyndromeDecoder + Send + Sync>,
    error_characteristics: Arc<RwLock<HardwareErrorCharacteristics>>,
    #[allow(dead_code)]
    learning_rate: f64,
    threshold_history: VecDeque<f64>,
}

impl AdaptiveThresholdDecoder {
    pub fn new(
        base_decoder: Arc<dyn SyndromeDecoder + Send + Sync>,
        initial_characteristics: HardwareErrorCharacteristics,
    ) -> Self {
        Self {
            base_decoder,
            error_characteristics: Arc::new(RwLock::new(initial_characteristics)),
            learning_rate: 0.01,
            threshold_history: VecDeque::with_capacity(1000),
        }
    }

    /// Update error characteristics based on hardware feedback
    pub fn update_characteristics(&mut self, new_characteristics: HardwareErrorCharacteristics) {
        *self
            .error_characteristics
            .write()
            .expect("Error characteristics lock poisoned") = new_characteristics;
    }

    /// Adapt thresholds based on observed error patterns
    pub fn adapt_thresholds(&mut self, syndrome: &[bool], correction_success: bool) {
        let error_weight = syndrome.iter().filter(|&&x| x).count() as f64;

        if correction_success {
            // Increase confidence in current threshold
            self.threshold_history.push_back(error_weight);
        } else {
            // Decrease threshold to be more aggressive
            self.threshold_history.push_back(error_weight * 0.8);
        }

        if self.threshold_history.len() > 100 {
            self.threshold_history.pop_front();
        }
    }

    /// Get current adaptive threshold
    pub fn current_threshold(&self) -> f64 {
        if self.threshold_history.is_empty() {
            return 1.0; // Default threshold
        }

        let sum: f64 = self.threshold_history.iter().sum();
        sum / self.threshold_history.len() as f64
    }
}

impl SyndromeDecoder for AdaptiveThresholdDecoder {
    fn decode(&self, syndrome: &[bool]) -> QuantRS2Result<PauliString> {
        let threshold = self.current_threshold();
        let error_weight = syndrome.iter().filter(|&&x| x).count() as f64;

        // Use adaptive threshold to decide decoding strategy
        if error_weight > threshold {
            // High-confidence error: use more aggressive decoding
            self.base_decoder.decode(syndrome)
        } else {
            // Low-confidence: use conservative approach or no correction
            Ok(PauliString::new(vec![Pauli::I; syndrome.len()]))
        }
    }
}

/// Parallel syndrome decoder for high-throughput error correction
pub struct ParallelSyndromeDecoder {
    base_decoder: Arc<dyn SyndromeDecoder + Send + Sync>,
    worker_count: usize,
}

impl ParallelSyndromeDecoder {
    pub fn new(base_decoder: Arc<dyn SyndromeDecoder + Send + Sync>, worker_count: usize) -> Self {
        Self {
            base_decoder,
            worker_count,
        }
    }

    /// Decode multiple syndromes in parallel
    pub fn decode_batch(&self, syndromes: &[Vec<bool>]) -> QuantRS2Result<Vec<PauliString>> {
        let chunk_size = (syndromes.len() + self.worker_count - 1) / self.worker_count;
        let mut handles = Vec::new();

        for chunk in syndromes.chunks(chunk_size) {
            let decoder = Arc::clone(&self.base_decoder);
            let chunk_data: Vec<Vec<bool>> = chunk.to_vec();

            let handle = thread::spawn(move || {
                let mut results = Vec::new();
                for syndrome in chunk_data {
                    match decoder.decode(&syndrome) {
                        Ok(correction) => results.push(correction),
                        Err(_) => {
                            results.push(PauliString::new(vec![Pauli::I; syndrome.len()]));
                        }
                    }
                }
                results
            });

            handles.push(handle);
        }

        let mut all_results = Vec::new();
        for handle in handles {
            match handle.join() {
                Ok(chunk_results) => all_results.extend(chunk_results),
                Err(_) => {
                    return Err(QuantRS2Error::ComputationError(
                        "Parallel decoding failed".to_string(),
                    ))
                }
            }
        }

        Ok(all_results)
    }
}

impl SyndromeDecoder for ParallelSyndromeDecoder {
    fn decode(&self, syndrome: &[bool]) -> QuantRS2Result<PauliString> {
        self.base_decoder.decode(syndrome)
    }
}

/// Mock hardware interface for testing
pub struct MockQuantumHardware {
    error_rate: f64,
    latency: Duration,
    syndrome_length: usize,
}

impl MockQuantumHardware {
    pub const fn new(error_rate: f64, latency: Duration, syndrome_length: usize) -> Self {
        Self {
            error_rate,
            latency,
            syndrome_length,
        }
    }
}

impl QuantumHardwareInterface for MockQuantumHardware {
    fn measure_syndromes(&self) -> QuantRS2Result<Vec<bool>> {
        thread::sleep(self.latency);

        // Simulate random syndrome measurements
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();
        let mut syndrome = vec![false; self.syndrome_length];
        for i in 0..self.syndrome_length {
            if rng.random::<f64>() < self.error_rate {
                syndrome[i] = true;
            }
        }

        Ok(syndrome)
    }

    fn apply_correction(&self, _correction: &PauliString) -> QuantRS2Result<()> {
        thread::sleep(self.latency / 2);
        Ok(())
    }

    fn get_error_characteristics(&self) -> QuantRS2Result<HardwareErrorCharacteristics> {
        Ok(HardwareErrorCharacteristics {
            single_qubit_error_rates: vec![self.error_rate; self.syndrome_length],
            two_qubit_error_rates: vec![self.error_rate * 10.0; self.syndrome_length / 2],
            measurement_error_rates: vec![self.error_rate * 0.1; self.syndrome_length],
            correlated_errors: Vec::new(),
            temporal_variation: 0.01,
        })
    }

    fn is_ready(&self) -> bool {
        true
    }

    fn get_latency_stats(&self) -> QuantRS2Result<LatencyStats> {
        Ok(LatencyStats {
            syndrome_measurement_time: self.latency,
            decoding_time: Duration::from_micros(10),
            correction_application_time: self.latency / 2,
            total_cycle_time: self.latency + Duration::from_micros(10) + self.latency / 2,
            throughput_hz: 1.0 / self.latency.as_secs_f64().mul_add(1.5, 10e-6),
        })
    }
}