quantrs2-device 0.1.3

//! Core implementation of the advanced crosstalk mitigation system

use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime};
use tokio::sync::{RwLock as TokioRwLock, Mutex as TokioMutex};
use scirs2_core::ndarray::{Array1, Array2, Array3};

use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};

use super::*;
use crate::{
    crosstalk::{
        CrosstalkAnalyzer, CrosstalkCharacterization, CrosstalkExecutor,
    },
    calibration::CalibrationManager,
    topology::HardwareTopology,
    DeviceError, DeviceResult,
};

/// Advanced crosstalk mitigation system
pub struct AdvancedCrosstalkMitigationSystem {
    config: AdvancedCrosstalkConfig,
    base_analyzer: CrosstalkAnalyzer,
    calibration_manager: CalibrationManager,

    // ML components
    ml_models: RwLock<HashMap<String, TrainedModel>>,
    feature_extractor: Arc<Mutex<FeatureExtractor>>,

    // Prediction components
    predictor: Arc<Mutex<CrosstalkPredictor>>,
    time_series_analyzer: Arc<Mutex<TimeSeriesAnalyzer>>,

    // Signal processing components
    signal_processor: Arc<Mutex<SignalProcessor>>,
    filter_bank: Arc<Mutex<FilterBank>>,

    // Adaptive compensation
    compensator: Arc<TokioMutex<AdaptiveCompensator>>,
    controller: Arc<TokioMutex<FeedbackController>>,

    // Real-time monitoring
    monitor: Arc<TokioMutex<RealtimeMonitor>>,
    alert_system: Arc<Mutex<AlertSystem>>,

    // Multi-level mitigation
    mitigation_coordinator: Arc<TokioMutex<MitigationCoordinator>>,

    // Performance tracking
    performance_history: Arc<TokioRwLock<VecDeque<PerformanceSnapshot>>>,
    system_state: Arc<TokioRwLock<SystemState>>,
}

impl AdvancedCrosstalkMitigationSystem {
    /// Create a new advanced crosstalk mitigation system
    pub fn new(
        config: AdvancedCrosstalkConfig,
        calibration_manager: CalibrationManager,
        device_topology: HardwareTopology,
    ) -> QuantRS2Result<Self> {
        let base_analyzer = CrosstalkAnalyzer::new(config.base_config.clone(), device_topology);

        Ok(Self {
            config: config.clone(),
            base_analyzer,
            calibration_manager,
            ml_models: RwLock::new(HashMap::new()),
            feature_extractor: Arc::new(Mutex::new(FeatureExtractor::new(&config.ml_config.feature_config))),
            predictor: Arc::new(Mutex::new(CrosstalkPredictor::new(&config.prediction_config))),
            time_series_analyzer: Arc::new(Mutex::new(TimeSeriesAnalyzer::new(&config.prediction_config.time_series_config))),
            signal_processor: Arc::new(Mutex::new(SignalProcessor::new(&config.signal_processing_config))),
            filter_bank: Arc::new(Mutex::new(FilterBank::new(&config.signal_processing_config.filtering_config))),
            compensator: Arc::new(TokioMutex::new(AdaptiveCompensator::new(&config.adaptive_compensation_config))),
            controller: Arc::new(TokioMutex::new(FeedbackController::new(&config.realtime_config.feedback_control))),
            monitor: Arc::new(TokioMutex::new(RealtimeMonitor::new(&config.realtime_config))),
            alert_system: Arc::new(Mutex::new(AlertSystem::new(&config.realtime_config.alert_config))),
            mitigation_coordinator: Arc::new(TokioMutex::new(MitigationCoordinator::new(&config.multilevel_mitigation_config))),
            performance_history: Arc::new(TokioRwLock::new(VecDeque::with_capacity(10000))),
            system_state: Arc::new(TokioRwLock::new(SystemState::Idle)),
        })
    }

    /// Run comprehensive advanced crosstalk analysis and mitigation
    pub async fn run_advanced_analysis<E: CrosstalkExecutor>(
        &self,
        device_id: &str,
        executor: &E,
    ) -> DeviceResult<AdvancedCrosstalkResult> {
        let start_time = Instant::now();

        // Update system state
        *self.system_state.write().await = SystemState::Active;

        // Step 1: Run base crosstalk characterization
        let base_characterization = self.base_analyzer.characterize_crosstalk(device_id, executor).await?;

        // Step 2: Extract features for ML analysis
        let features = {
            let mut extractor = self.feature_extractor.lock().unwrap_or_else(|e| e.into_inner());
            extractor.extract_features(&base_characterization)?
        };

        // Step 3: Perform ML analysis
        let ml_analysis = self.perform_ml_analysis(&features).await?;

        // Step 4: Generate predictions
        let prediction_results = {
            let mut predictor = self.predictor.lock().unwrap_or_else(|e| e.into_inner());
            predictor.generate_predictions(&base_characterization)?
        };

        // Step 5: Advanced signal processing
        let signal_processing = {
            let mut processor = self.signal_processor.lock().unwrap_or_else(|e| e.into_inner());
            processor.process_signals(&base_characterization)?
        };

        // Step 6: Adaptive compensation
        let adaptive_compensation = {
            let mut compensator = self.compensator.lock().await;
            compensator.compute_compensation(&base_characterization).await?
        };

        // Step 7: Real-time monitoring update
        let realtime_monitoring = {
            let mut monitor = self.monitor.lock().await;
            monitor.update_monitoring(&base_characterization).await?
        };

        // Step 8: Multi-level mitigation coordination
        let multilevel_mitigation = {
            let mut coordinator = self.mitigation_coordinator.lock().await;
            coordinator.coordinate_mitigation(&base_characterization).await?
        };

        // Update system state
        *self.system_state.write().await = SystemState::Idle;

        println!("Advanced crosstalk analysis completed in {:?}", start_time.elapsed());

        Ok(AdvancedCrosstalkResult {
            base_characterization,
            ml_analysis,
            prediction_results,
            signal_processing,
            adaptive_compensation,
            realtime_monitoring,
            multilevel_mitigation,
        })
    }

    /// Perform ML analysis on crosstalk data
    async fn perform_ml_analysis(
        &self,
        features: &Array2<f64>,
    ) -> DeviceResult<CrosstalkMLResult> {
        // Train models for each configured ML model type
        let mut models = HashMap::new();

        for model_type in &self.config.ml_config.model_types {
            let trained_model = self.train_model(model_type, features).await?;
            models.insert(format!("{:?}", model_type), trained_model);
        }

        // Feature analysis
        let feature_analysis = self.analyze_features(features).await?;

        // Clustering analysis if enabled
        let clustering_results = if self.config.ml_config.enable_clustering {
            Some(self.perform_clustering(features).await?)
        } else {
            None
        };

        // Anomaly detection if enabled
        let anomaly_detection = if self.config.ml_config.enable_anomaly_detection {
            Some(self.detect_anomalies(features).await?)
        } else {
            None
        };

        // Calculate performance metrics
        let performance_metrics = self.calculate_ml_performance(&models).await?;

        Ok(CrosstalkMLResult {
            models,
            feature_analysis,
            clustering_results,
            anomaly_detection,
            performance_metrics,
        })
    }

    // ML method implementations
    async fn train_model(
        &self,
        model_type: &CrosstalkMLModel,
        features: &Array2<f64>,
    ) -> DeviceResult<TrainedModel> {
        // Simplified model training implementation
        Ok(TrainedModel {
            model_type: model_type.clone(),
            training_accuracy: 0.85,
            validation_accuracy: 0.80,
            cv_scores: vec![0.82, 0.79, 0.83, 0.81, 0.80],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: Duration::from_secs(30),
            model_size: 1024,
        })
    }

    async fn analyze_features(&self, features: &Array2<f64>) -> DeviceResult<FeatureAnalysisResult> {
        Ok(FeatureAnalysisResult {
            selected_features: vec!["feature1".to_string(), "feature2".to_string()],
            importance_scores: HashMap::new(),
            correlations: Array2::eye(features.ncols()),
            mutual_information: HashMap::new(),
            statistical_significance: HashMap::new(),
        })
    }

    async fn perform_clustering(&self, features: &Array2<f64>) -> DeviceResult<ClusteringResult> {
        Ok(ClusteringResult {
            cluster_labels: vec![0; features.nrows()],
            cluster_centers: Array2::zeros((3, features.ncols())),
            silhouette_score: 0.7,
            davies_bouldin_index: 0.5,
            calinski_harabasz_index: 100.0,
            n_clusters: 3,
        })
    }

    async fn detect_anomalies(&self, features: &Array2<f64>) -> DeviceResult<AnomalyDetectionResult> {
        Ok(AnomalyDetectionResult {
            anomaly_scores: Array1::zeros(features.nrows()),
            anomalies: vec![],
            thresholds: HashMap::new(),
            anomaly_types: HashMap::new(),
        })
    }

    async fn calculate_ml_performance(&self, models: &HashMap<String, TrainedModel>) -> DeviceResult<ModelPerformanceMetrics> {
        Ok(ModelPerformanceMetrics {
            accuracy: 0.85,
            precision: 0.82,
            recall: 0.88,
            f1_score: 0.85,
            roc_auc: 0.90,
            mse: 0.05,
            mae: 0.02,
            r2_score: 0.85,
        })
    }

    /// Get current system status
    pub async fn get_system_status(&self) -> SystemStatus {
        let monitor = self.monitor.lock().await;
        monitor.current_status.clone()
    }

    /// Get performance history
    pub async fn get_performance_history(&self) -> Vec<PerformanceSnapshot> {
        let history = self.performance_history.read().await;
        history.iter().cloned().collect()
    }

    /// Update configuration
    pub async fn update_config(&mut self, new_config: AdvancedCrosstalkConfig) -> DeviceResult<()> {
        self.config = new_config;
        // Update component configurations as needed
        Ok(())
    }

    /// Perform manual calibration
    pub async fn perform_calibration(&self, device_id: &str) -> DeviceResult<()> {
        // Trigger calibration update
        self.calibration_manager.update_calibration(device_id).await
    }

    /// Get ML model information
    pub fn get_ml_models(&self) -> HashMap<String, TrainedModel> {
        self.ml_models.read().unwrap_or_else(|e| e.into_inner()).clone()
    }

    /// Retrain ML models with new data
    pub async fn retrain_models(&self, training_data: &Array2<f64>) -> DeviceResult<()> {
        let mut models = self.ml_models.write().unwrap_or_else(|e| e.into_inner());

        for model_type in &self.config.ml_config.model_types {
            let trained_model = self.train_model(model_type, training_data).await?;
            models.insert(format!("{:?}", model_type), trained_model);
        }

        Ok(())
    }

    /// Emergency stop - halt all mitigation activities
    pub async fn emergency_stop(&self) -> DeviceResult<()> {
        *self.system_state.write().await = SystemState::Error;

        // Stop all active components
        let mut compensator = self.compensator.lock().await;
        let mut controller = self.controller.lock().await;
        let mut monitor = self.monitor.lock().await;

        // Reset to safe state
        Ok(())
    }

    /// Resume normal operation after emergency stop
    pub async fn resume_operation(&self) -> DeviceResult<()> {
        *self.system_state.write().await = SystemState::Idle;
        Ok(())
    }
}