quantrs2_anneal/

realtime_adaptive_qec.rs

//! Real-Time Adaptive Quantum Error Correction for Annealing Systems
//!
//! This module implements a sophisticated real-time adaptive quantum error correction
//! system that dynamically adjusts error correction strategies based on continuous
//! noise monitoring, performance feedback, and machine learning predictions. It provides
//! adaptive protocols that optimize error correction overhead while maintaining
//! solution quality in varying noise environments.
//!
//! Key Features:
//! - Real-time noise characterization and drift tracking
//! - Adaptive error correction protocol selection
//! - Machine learning-based noise prediction
//! - Dynamic threshold adjustment and resource allocation
//! - Multi-level error correction hierarchies
//! - Performance-aware error correction optimization
//! - Temporal noise correlation analysis
//! - Predictive error mitigation strategies

use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::thread;
use std::time::{Duration, Instant, SystemTime};

use crate::applications::{ApplicationError, ApplicationResult};
use crate::ising::{IsingModel, QuboModel};
use crate::quantum_error_correction::{
    ErrorCorrectionCode, LogicalAnnealingEncoder, MitigationTechnique, SyndromeDetector,
};

/// Real-time adaptive QEC configuration
#[derive(Debug, Clone)]
pub struct AdaptiveQecConfig {
    /// Noise monitoring interval
    pub monitoring_interval: Duration,
    /// Adaptation trigger threshold
    pub adaptation_threshold: f64,
    /// Performance window for assessment
    pub performance_window: Duration,
    /// Machine learning configuration
    pub ml_config: MLNoiseConfig,
    /// Hierarchical error correction settings
    pub hierarchy_config: HierarchyConfig,
    /// Resource management settings
    pub resource_config: ResourceManagementConfig,
    /// Prediction and forecasting settings
    pub prediction_config: PredictionConfig,
}

impl Default for AdaptiveQecConfig {
    fn default() -> Self {
        Self {
            monitoring_interval: Duration::from_millis(100),
            adaptation_threshold: 0.1,
            performance_window: Duration::from_secs(30),
            ml_config: MLNoiseConfig::default(),
            hierarchy_config: HierarchyConfig::default(),
            resource_config: ResourceManagementConfig::default(),
            prediction_config: PredictionConfig::default(),
        }
    }
}

/// Machine learning configuration for noise prediction
#[derive(Debug, Clone)]
pub struct MLNoiseConfig {
    /// Enable neural network noise prediction
    pub enable_neural_prediction: bool,
    /// Neural network architecture
    pub network_architecture: NeuralArchitecture,
    /// Training data window size
    pub training_window: usize,
    /// Prediction horizon
    pub prediction_horizon: Duration,
    /// Model update frequency
    pub update_frequency: Duration,
    /// Feature extraction settings
    pub feature_config: FeatureConfig,
}

impl Default for MLNoiseConfig {
    fn default() -> Self {
        Self {
            enable_neural_prediction: true,
            network_architecture: NeuralArchitecture::LSTM,
            training_window: 1000,
            prediction_horizon: Duration::from_secs(10),
            update_frequency: Duration::from_secs(60),
            feature_config: FeatureConfig::default(),
        }
    }
}

/// Neural network architectures for noise prediction
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NeuralArchitecture {
    /// Long Short-Term Memory networks
    LSTM,
    /// Gated Recurrent Units
    GRU,
    /// Transformer architecture
    Transformer,
    /// Convolutional Neural Network
    CNN,
    /// Hybrid CNN-LSTM
    CNNLstm,
}

/// Feature extraction configuration
#[derive(Debug, Clone)]
pub struct FeatureConfig {
    /// Enable temporal features
    pub enable_temporal: bool,
    /// Enable spectral analysis features
    pub enable_spectral: bool,
    /// Enable correlation features
    pub enable_correlation: bool,
    /// Feature normalization method
    pub normalization: FeatureNormalization,
    /// Feature selection method
    pub selection_method: FeatureSelection,
}

impl Default for FeatureConfig {
    fn default() -> Self {
        Self {
            enable_temporal: true,
            enable_spectral: true,
            enable_correlation: true,
            normalization: FeatureNormalization::ZScore,
            selection_method: FeatureSelection::Automatic,
        }
    }
}

/// Feature normalization methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum FeatureNormalization {
    /// Z-score normalization
    ZScore,
    /// Min-max normalization
    MinMax,
    /// Robust scaling
    Robust,
    /// No normalization
    None,
}

/// Feature selection methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum FeatureSelection {
    /// Automatic feature selection
    Automatic,
    /// Manual feature specification
    Manual(Vec<String>),
    /// Principal Component Analysis
    PCA,
    /// Mutual information
    MutualInformation,
}

/// Hierarchical error correction configuration
#[derive(Debug, Clone)]
pub struct HierarchyConfig {
    /// Enable multi-level hierarchy
    pub enable_hierarchy: bool,
    /// Number of hierarchy levels
    pub num_levels: usize,
    /// Level transition thresholds
    pub level_thresholds: Vec<f64>,
    /// Resource allocation per level
    pub level_resources: Vec<f64>,
    /// Inter-level communication protocol
    pub communication_protocol: HierarchyCommunication,
}

impl Default for HierarchyConfig {
    fn default() -> Self {
        Self {
            enable_hierarchy: true,
            num_levels: 3,
            level_thresholds: vec![0.01, 0.05, 0.1],
            level_resources: vec![0.1, 0.3, 0.6],
            communication_protocol: HierarchyCommunication::Cascade,
        }
    }
}

/// Hierarchy communication protocols
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum HierarchyCommunication {
    /// Cascade from low to high levels
    Cascade,
    /// Parallel processing at all levels
    Parallel,
    /// Dynamic level selection
    Dynamic,
    /// Adaptive switching
    Adaptive,
}

/// Resource management configuration
#[derive(Debug, Clone)]
pub struct ResourceManagementConfig {
    /// Maximum overhead ratio
    pub max_overhead_ratio: f64,
    /// Resource allocation strategy
    pub allocation_strategy: ResourceAllocationStrategy,
    /// Dynamic resource adjustment
    pub enable_dynamic_adjustment: bool,
    /// Resource constraint enforcement
    pub enforce_constraints: bool,
    /// Performance vs. overhead trade-off
    pub performance_weight: f64,
}

impl Default for ResourceManagementConfig {
    fn default() -> Self {
        Self {
            max_overhead_ratio: 0.3,
            allocation_strategy: ResourceAllocationStrategy::Adaptive,
            enable_dynamic_adjustment: true,
            enforce_constraints: true,
            performance_weight: 0.7,
        }
    }
}

/// Resource allocation strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ResourceAllocationStrategy {
    /// Fixed allocation
    Fixed,
    /// Performance-based allocation
    PerformanceBased,
    /// Adaptive allocation
    Adaptive,
    /// Predictive allocation
    Predictive,
}

/// Prediction and forecasting configuration
#[derive(Debug, Clone)]
pub struct PredictionConfig {
    /// Enable noise trend prediction
    pub enable_trend_prediction: bool,
    /// Enable performance forecasting
    pub enable_performance_forecasting: bool,
    /// Prediction accuracy threshold
    pub accuracy_threshold: f64,
    /// Confidence interval level
    pub confidence_level: f64,
    /// Prediction update strategy
    pub update_strategy: PredictionUpdateStrategy,
}

impl Default for PredictionConfig {
    fn default() -> Self {
        Self {
            enable_trend_prediction: true,
            enable_performance_forecasting: true,
            accuracy_threshold: 0.8,
            confidence_level: 0.95,
            update_strategy: PredictionUpdateStrategy::Continuous,
        }
    }
}

/// Prediction update strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PredictionUpdateStrategy {
    /// Continuous updates
    Continuous,
    /// Periodic updates
    Periodic(Duration),
    /// Event-driven updates
    EventDriven,
    /// Adaptive updates
    Adaptive,
}

/// Real-time noise characteristics
#[derive(Debug, Clone)]
pub struct NoiseCharacteristics {
    /// Timestamp of measurement
    pub timestamp: Instant,
    /// Overall noise level
    pub noise_level: f64,
    /// Noise type classification
    pub noise_type: NoiseType,
    /// Temporal correlation
    pub temporal_correlation: f64,
    /// Spatial correlation
    pub spatial_correlation: f64,
    /// Noise spectrum
    pub noise_spectrum: Vec<f64>,
    /// Error rates per qubit
    pub per_qubit_error_rates: Vec<f64>,
    /// Coherence times
    pub coherence_times: Vec<f64>,
    /// Gate fidelities
    pub gate_fidelities: HashMap<String, f64>,
}

/// Types of noise
#[derive(Debug, Clone, PartialEq)]
pub enum NoiseType {
    /// White noise (uncorrelated)
    White,
    /// Colored noise (correlated)
    Colored,
    /// Burst noise (intermittent)
    Burst,
    /// Drift noise (slowly varying)
    Drift,
    /// Mixed noise types
    Mixed(Vec<Self>),
}

/// Adaptive error correction protocol
#[derive(Debug, Clone)]
pub struct AdaptiveProtocol {
    /// Protocol identifier
    pub id: String,
    /// Current error correction strategy
    pub current_strategy: ErrorCorrectionStrategy,
    /// Adaptation rules
    pub adaptation_rules: Vec<AdaptationRule>,
    /// Performance metrics
    pub performance_metrics: ProtocolPerformance,
    /// Resource usage
    pub resource_usage: ResourceUsage,
    /// Last adaptation time
    pub last_adaptation: Instant,
}

/// Error correction strategies
#[derive(Debug, Clone)]
pub enum ErrorCorrectionStrategy {
    /// No error correction
    None,
    /// Basic error detection
    Detection(DetectionConfig),
    /// Full error correction
    Correction(CorrectionConfig),
    /// Hybrid approach
    Hybrid(HybridConfig),
    /// Adaptive strategy selection
    Adaptive(AdaptiveStrategyConfig),
}

/// Detection-only configuration
#[derive(Debug, Clone)]
pub struct DetectionConfig {
    /// Detection threshold
    pub threshold: f64,
    /// Detection method
    pub method: DetectionMethod,
    /// Action on detection
    pub action: DetectionAction,
}

/// Detection methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DetectionMethod {
    /// Parity check
    Parity,
    /// Syndrome measurement
    Syndrome,
    /// Statistical analysis
    Statistical,
    /// Machine learning classification
    MLClassification,
}

/// Actions on error detection
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DetectionAction {
    /// Flag error only
    Flag,
    /// Retry operation
    Retry,
    /// Switch protocol
    SwitchProtocol,
    /// Increase correction level
    IncreaseCorrectionLevel,
}

/// Full error correction configuration
#[derive(Debug, Clone)]
pub struct CorrectionConfig {
    /// Error correction code
    pub code: ErrorCorrectionCode,
    /// Correction threshold
    pub threshold: f64,
    /// Maximum correction attempts
    pub max_attempts: usize,
    /// Correction efficiency target
    pub efficiency_target: f64,
}

/// Hybrid error correction configuration
#[derive(Debug, Clone)]
pub struct HybridConfig {
    /// Detection configuration
    pub detection: DetectionConfig,
    /// Correction configuration
    pub correction: CorrectionConfig,
    /// Switching criteria
    pub switching_criteria: SwitchingCriteria,
}

/// Adaptive strategy selection configuration
#[derive(Debug, Clone)]
pub struct AdaptiveStrategyConfig {
    /// Available strategies
    pub available_strategies: Vec<ErrorCorrectionStrategy>,
    /// Selection algorithm
    pub selection_algorithm: StrategySelectionAlgorithm,
    /// Performance history
    pub performance_history: Vec<StrategyPerformance>,
}

/// Strategy selection algorithms
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum StrategySelectionAlgorithm {
    /// Greedy selection
    Greedy,
    /// Multi-armed bandit
    MultiArmedBandit,
    /// Reinforcement learning
    ReinforcementLearning,
    /// Bayesian optimization
    BayesianOptimization,
}

/// Strategy performance tracking
#[derive(Debug, Clone)]
pub struct StrategyPerformance {
    /// Strategy identifier
    pub strategy_id: String,
    /// Performance score
    pub performance_score: f64,
    /// Resource overhead
    pub resource_overhead: f64,
    /// Success rate
    pub success_rate: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Switching criteria for hybrid approaches
#[derive(Debug, Clone)]
pub struct SwitchingCriteria {
    /// Error rate threshold
    pub error_rate_threshold: f64,
    /// Performance degradation threshold
    pub performance_threshold: f64,
    /// Resource usage threshold
    pub resource_threshold: f64,
    /// Time-based switching
    pub time_based: Option<Duration>,
}

/// Adaptation rules for protocol adjustment
#[derive(Debug, Clone)]
pub struct AdaptationRule {
    /// Rule identifier
    pub id: String,
    /// Condition for triggering adaptation
    pub condition: AdaptationCondition,
    /// Action to take
    pub action: AdaptationAction,
    /// Priority level
    pub priority: u8,
    /// Cooldown period
    pub cooldown: Duration,
}

/// Conditions for triggering adaptation
#[derive(Debug, Clone)]
pub enum AdaptationCondition {
    /// Noise level exceeds threshold
    NoiseThreshold(f64),
    /// Performance drops below threshold
    PerformanceThreshold(f64),
    /// Error rate exceeds threshold
    ErrorRateThreshold(f64),
    /// Resource usage exceeds threshold
    ResourceThreshold(f64),
    /// Time-based condition
    TimeBased(Duration),
    /// Composite condition
    Composite(Vec<Self>),
}

/// Actions for adaptation
#[derive(Debug, Clone)]
pub enum AdaptationAction {
    /// Switch error correction strategy
    SwitchStrategy(ErrorCorrectionStrategy),
    /// Adjust threshold
    AdjustThreshold(f64),
    /// Increase correction level
    IncreaseCorrectionLevel,
    /// Decrease correction level
    DecreaseCorrectionLevel,
    /// Reallocate resources
    ReallocateResources(ResourceReallocation),
    /// Update prediction model
    UpdatePredictionModel,
}

/// Resource reallocation specification
#[derive(Debug, Clone)]
pub struct ResourceReallocation {
    /// New resource allocation ratios
    pub allocation_ratios: Vec<f64>,
    /// Target components
    pub target_components: Vec<String>,
    /// Reallocation priority
    pub priority: u8,
}

/// Protocol performance metrics
#[derive(Debug, Clone)]
pub struct ProtocolPerformance {
    /// Success rate
    pub success_rate: f64,
    /// Average correction time
    pub avg_correction_time: Duration,
    /// Resource efficiency
    pub resource_efficiency: f64,
    /// Quality improvement
    pub quality_improvement: f64,
    /// Overhead ratio
    pub overhead_ratio: f64,
    /// Adaptation frequency
    pub adaptation_frequency: f64,
}

/// Resource usage tracking
#[derive(Debug, Clone)]
pub struct ResourceUsage {
    /// Computational resources
    pub computational: f64,
    /// Memory usage
    pub memory: f64,
    /// Communication overhead
    pub communication: f64,
    /// Total overhead
    pub total_overhead: f64,
    /// Usage history
    pub usage_history: VecDeque<ResourceSnapshot>,
}

/// Resource usage snapshot
#[derive(Debug, Clone)]
pub struct ResourceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Resource usage values
    pub usage: HashMap<String, f64>,
    /// Performance metrics at this time
    pub performance: f64,
}

/// Noise prediction model
#[derive(Debug)]
pub struct NoisePredictionModel {
    /// Model type
    pub model_type: NeuralArchitecture,
    /// Model parameters
    pub parameters: Vec<f64>,
    /// Training data
    pub training_data: VecDeque<NoiseDataPoint>,
    /// Prediction accuracy
    pub accuracy: f64,
    /// Last update time
    pub last_update: Instant,
    /// Feature extractor
    pub feature_extractor: FeatureExtractor,
}

/// Noise data point for training
#[derive(Debug, Clone)]
pub struct NoiseDataPoint {
    /// Input features
    pub features: Vec<f64>,
    /// Target noise characteristics
    pub target: NoiseCharacteristics,
    /// Timestamp
    pub timestamp: Instant,
}

/// Feature extraction system
#[derive(Debug)]
pub struct FeatureExtractor {
    /// Extraction configuration
    pub config: FeatureConfig,
    /// Feature definitions
    pub feature_definitions: Vec<FeatureDefinition>,
    /// Normalization parameters
    pub normalization_params: HashMap<String, NormalizationParams>,
}

/// Feature definition
#[derive(Debug, Clone)]
pub struct FeatureDefinition {
    /// Feature name
    pub name: String,
    /// Feature type
    pub feature_type: FeatureType,
    /// Extraction parameters
    pub parameters: HashMap<String, f64>,
}

/// Types of features
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum FeatureType {
    /// Temporal features
    Temporal,
    /// Spectral features
    Spectral,
    /// Correlation features
    Correlation,
    /// Statistical features
    Statistical,
    /// Custom features
    Custom(String),
}

/// Normalization parameters
#[derive(Debug, Clone)]
pub struct NormalizationParams {
    /// Mean value
    pub mean: f64,
    /// Standard deviation
    pub std: f64,
    /// Minimum value
    pub min: f64,
    /// Maximum value
    pub max: f64,
}

/// Main real-time adaptive QEC system
pub struct RealTimeAdaptiveQec {
    /// Configuration
    pub config: AdaptiveQecConfig,
    /// Noise monitoring system
    pub noise_monitor: Arc<Mutex<NoiseMonitor>>,
    /// Adaptive protocol manager
    pub protocol_manager: Arc<RwLock<AdaptiveProtocolManager>>,
    /// ML prediction system
    pub prediction_system: Arc<Mutex<NoisePredictionSystem>>,
    /// Performance analyzer
    pub performance_analyzer: Arc<Mutex<PerformanceAnalyzer>>,
    /// Resource manager
    pub resource_manager: Arc<Mutex<AdaptiveResourceManager>>,
    /// Hierarchy coordinator
    pub hierarchy_coordinator: Arc<Mutex<HierarchyCoordinator>>,
}

/// Noise monitoring system
pub struct NoiseMonitor {
    /// Current noise characteristics
    pub current_noise: NoiseCharacteristics,
    /// Noise history
    pub noise_history: VecDeque<NoiseCharacteristics>,
    /// Monitoring sensors
    pub sensors: Vec<NoiseSensor>,
    /// Analysis algorithms
    pub analyzers: Vec<NoiseAnalyzer>,
}

/// Noise sensor interface
#[derive(Debug)]
pub struct NoiseSensor {
    /// Sensor identifier
    pub id: String,
    /// Sensor type
    pub sensor_type: SensorType,
    /// Measurement frequency
    pub frequency: f64,
    /// Last measurement
    pub last_measurement: Option<Instant>,
    /// Calibration data
    pub calibration: SensorCalibration,
}

/// Types of noise sensors
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SensorType {
    /// Error rate sensor
    ErrorRate,
    /// Coherence time sensor
    CoherenceTime,
    /// Gate fidelity sensor
    GateFidelity,
    /// Environmental sensor
    Environmental,
    /// Process tomography
    ProcessTomography,
}

/// Sensor calibration data
#[derive(Debug, Clone)]
pub struct SensorCalibration {
    /// Calibration timestamp
    pub timestamp: Instant,
    /// Calibration parameters
    pub parameters: HashMap<String, f64>,
    /// Accuracy estimate
    pub accuracy: f64,
}

/// Noise analysis algorithms
#[derive(Debug)]
pub struct NoiseAnalyzer {
    /// Analyzer identifier
    pub id: String,
    /// Analysis algorithm
    pub algorithm: AnalysisAlgorithm,
    /// Analysis parameters
    pub parameters: HashMap<String, f64>,
}

/// Noise analysis algorithms
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AnalysisAlgorithm {
    /// Spectral analysis
    Spectral,
    /// Correlation analysis
    Correlation,
    /// Statistical analysis
    Statistical,
    /// Machine learning classification
    MLClassification,
    /// Fourier analysis
    Fourier,
}

/// Adaptive protocol management
pub struct AdaptiveProtocolManager {
    /// Currently active protocols
    pub active_protocols: HashMap<String, AdaptiveProtocol>,
    /// Protocol history
    pub protocol_history: VecDeque<ProtocolEvent>,
    /// Adaptation engine
    pub adaptation_engine: AdaptationEngine,
}

/// Protocol events for history tracking
#[derive(Debug, Clone)]
pub struct ProtocolEvent {
    /// Event timestamp
    pub timestamp: Instant,
    /// Event type
    pub event_type: ProtocolEventType,
    /// Protocol involved
    pub protocol_id: String,
    /// Event details
    pub details: HashMap<String, String>,
}

/// Types of protocol events
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ProtocolEventType {
    /// Protocol activation
    Activation,
    /// Protocol deactivation
    Deactivation,
    /// Protocol adaptation
    Adaptation,
    /// Performance update
    PerformanceUpdate,
    /// Error detected
    ErrorDetected,
    /// Error corrected
    ErrorCorrected,
}

/// Adaptation decision engine
#[derive(Debug)]
pub struct AdaptationEngine {
    /// Decision algorithm
    pub algorithm: AdaptationAlgorithm,
    /// Decision parameters
    pub parameters: HashMap<String, f64>,
    /// Decision history
    pub decision_history: VecDeque<AdaptationDecision>,
}

/// Adaptation algorithms
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AdaptationAlgorithm {
    /// Rule-based adaptation
    RuleBased,
    /// Machine learning-based
    MachineLearning,
    /// Reinforcement learning
    ReinforcementLearning,
    /// Bayesian optimization
    BayesianOptimization,
    /// Hybrid approach
    Hybrid,
}

/// Adaptation decisions
#[derive(Debug, Clone)]
pub struct AdaptationDecision {
    /// Decision timestamp
    pub timestamp: Instant,
    /// Decision rationale
    pub rationale: String,
    /// Actions taken
    pub actions: Vec<AdaptationAction>,
    /// Expected impact
    pub expected_impact: f64,
    /// Actual impact (filled later)
    pub actual_impact: Option<f64>,
}

/// Noise prediction system
pub struct NoisePredictionSystem {
    /// Prediction models
    pub models: HashMap<String, NoisePredictionModel>,
    /// Model ensemble
    pub ensemble: ModelEnsemble,
    /// Prediction cache
    pub prediction_cache: HashMap<String, PredictionResult>,
}

/// Model ensemble for improved predictions
#[derive(Debug)]
pub struct ModelEnsemble {
    /// Ensemble method
    pub method: EnsembleMethod,
    /// Model weights
    pub weights: HashMap<String, f64>,
    /// Performance history
    pub performance_history: VecDeque<EnsemblePerformance>,
}

/// Ensemble methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum EnsembleMethod {
    /// Simple averaging
    Average,
    /// Weighted averaging
    WeightedAverage,
    /// Voting
    Voting,
    /// Stacking
    Stacking,
    /// Boosting
    Boosting,
}

/// Ensemble performance tracking
#[derive(Debug, Clone)]
pub struct EnsemblePerformance {
    /// Timestamp
    pub timestamp: Instant,
    /// Accuracy
    pub accuracy: f64,
    /// Confidence
    pub confidence: f64,
    /// Individual model contributions
    pub model_contributions: HashMap<String, f64>,
}

/// Prediction results
#[derive(Debug, Clone)]
pub struct PredictionResult {
    /// Predicted noise characteristics
    pub predicted_noise: NoiseCharacteristics,
    /// Prediction confidence
    pub confidence: f64,
    /// Prediction horizon
    pub horizon: Duration,
    /// Uncertainty bounds
    pub uncertainty_bounds: (f64, f64),
    /// Prediction timestamp
    pub timestamp: Instant,
}

/// Performance analysis system
pub struct PerformanceAnalyzer {
    /// Performance metrics
    pub metrics: PerformanceMetrics,
    /// Analysis algorithms
    pub analyzers: Vec<PerformanceAnalysisAlgorithm>,
    /// Benchmark comparisons
    pub benchmarks: HashMap<String, BenchmarkResult>,
}

/// Comprehensive performance metrics
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    /// Error correction efficiency
    pub correction_efficiency: f64,
    /// Resource utilization efficiency
    pub resource_efficiency: f64,
    /// Adaptation responsiveness
    pub adaptation_responsiveness: f64,
    /// Prediction accuracy
    pub prediction_accuracy: f64,
    /// Overall system performance
    pub overall_performance: f64,
    /// Performance history
    pub performance_history: VecDeque<PerformanceSnapshot>,
}

/// Performance snapshot for historical tracking
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Metrics at this time
    pub metrics: HashMap<String, f64>,
    /// System state
    pub system_state: SystemState,
}

/// System state representation
#[derive(Debug, Clone)]
pub struct SystemState {
    /// Active protocols
    pub active_protocols: Vec<String>,
    /// Current noise level
    pub noise_level: f64,
    /// Resource usage
    pub resource_usage: f64,
    /// Performance score
    pub performance_score: f64,
}

/// Performance analysis algorithms
#[derive(Debug)]
pub struct PerformanceAnalysisAlgorithm {
    /// Algorithm identifier
    pub id: String,
    /// Analysis type
    pub analysis_type: AnalysisType,
    /// Algorithm parameters
    pub parameters: HashMap<String, f64>,
}

/// Types of performance analysis
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AnalysisType {
    /// Trend analysis
    Trend,
    /// Anomaly detection
    AnomalyDetection,
    /// Correlation analysis
    Correlation,
    /// Comparative analysis
    Comparative,
    /// Predictive analysis
    Predictive,
}

/// Benchmark results
#[derive(Debug, Clone)]
pub struct BenchmarkResult {
    /// Benchmark name
    pub name: String,
    /// Performance score
    pub score: f64,
    /// Comparison baseline
    pub baseline: f64,
    /// Improvement factor
    pub improvement_factor: f64,
    /// Timestamp
    pub timestamp: Instant,
}

/// Adaptive resource management
pub struct AdaptiveResourceManager {
    /// Resource allocation
    pub allocation: ResourceAllocation,
    /// Resource constraints
    pub constraints: ResourceConstraints,
    /// Optimization algorithms
    pub optimizers: Vec<ResourceOptimizer>,
}

/// Current resource allocation
#[derive(Debug, Clone)]
pub struct ResourceAllocation {
    /// Allocation map
    pub allocation_map: HashMap<String, f64>,
    /// Total allocated resources
    pub total_allocated: f64,
    /// Available resources
    pub available_resources: f64,
    /// Allocation history
    pub allocation_history: VecDeque<AllocationSnapshot>,
}

/// Allocation snapshot
#[derive(Debug, Clone)]
pub struct AllocationSnapshot {
    /// Timestamp
    pub timestamp: Instant,
    /// Allocation state
    pub allocation: HashMap<String, f64>,
    /// Performance at this allocation
    pub performance: f64,
}

/// Resource constraints
#[derive(Debug, Clone)]
pub struct ResourceConstraints {
    /// Maximum total resources
    pub max_total: f64,
    /// Per-component constraints
    pub per_component: HashMap<String, f64>,
    /// Minimum performance requirement
    pub min_performance: f64,
    /// Constraint enforcement method
    pub enforcement_method: ConstraintEnforcement,
}

/// Constraint enforcement methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ConstraintEnforcement {
    /// Hard constraints (must be satisfied)
    Hard,
    /// Soft constraints (penalties)
    Soft,
    /// Adaptive constraints
    Adaptive,
}

/// Resource optimization algorithms
#[derive(Debug)]
pub struct ResourceOptimizer {
    /// Optimizer identifier
    pub id: String,
    /// Optimization algorithm
    pub algorithm: OptimizationAlgorithm,
    /// Algorithm parameters
    pub parameters: HashMap<String, f64>,
}

/// Resource optimization algorithms
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum OptimizationAlgorithm {
    /// Gradient descent
    GradientDescent,
    /// Genetic algorithm
    GeneticAlgorithm,
    /// Simulated annealing
    SimulatedAnnealing,
    /// Particle swarm optimization
    ParticleSwarm,
    /// Bayesian optimization
    BayesianOptimization,
}

/// Hierarchy coordination system
pub struct HierarchyCoordinator {
    /// Hierarchy levels
    pub levels: Vec<HierarchyLevel>,
    /// Inter-level communication
    pub communication: HierarchyCommunicationManager,
    /// Coordination algorithms
    pub coordinators: Vec<CoordinationAlgorithm>,
}

/// Hierarchy level representation
#[derive(Debug, Clone)]
pub struct HierarchyLevel {
    /// Level identifier
    pub id: usize,
    /// Level priority
    pub priority: u8,
    /// Active protocols at this level
    pub protocols: Vec<String>,
    /// Resource allocation
    pub resources: f64,
    /// Performance metrics
    pub performance: LevelPerformance,
}

/// Level-specific performance metrics
#[derive(Debug, Clone)]
pub struct LevelPerformance {
    /// Correction accuracy
    pub accuracy: f64,
    /// Response time
    pub response_time: Duration,
    /// Resource efficiency
    pub efficiency: f64,
    /// Coordination effectiveness
    pub coordination_effectiveness: f64,
}

/// Inter-level communication management
#[derive(Debug)]
pub struct HierarchyCommunicationManager {
    /// Communication channels
    pub channels: HashMap<(usize, usize), CommunicationChannel>,
    /// Message queues
    pub message_queues: HashMap<usize, VecDeque<HierarchyMessage>>,
    /// Communication statistics
    pub statistics: CommunicationStatistics,
}

/// Communication channel between levels
#[derive(Debug)]
pub struct CommunicationChannel {
    /// Source level
    pub source: usize,
    /// Target level
    pub target: usize,
    /// Channel capacity
    pub capacity: f64,
    /// Current utilization
    pub utilization: f64,
    /// Message latency
    pub latency: Duration,
}

/// Messages between hierarchy levels
#[derive(Debug, Clone)]
pub struct HierarchyMessage {
    /// Message identifier
    pub id: String,
    /// Source level
    pub source: usize,
    /// Target level
    pub target: usize,
    /// Message type
    pub message_type: MessageType,
    /// Message payload
    pub payload: MessagePayload,
    /// Timestamp
    pub timestamp: Instant,
    /// Priority
    pub priority: u8,
}

/// Types of hierarchy messages
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum MessageType {
    /// Error detection report
    ErrorReport,
    /// Correction request
    CorrectionRequest,
    /// Resource request
    ResourceRequest,
    /// Performance update
    PerformanceUpdate,
    /// Coordination signal
    CoordinationSignal,
}

/// Message payload data
#[derive(Debug, Clone)]
pub enum MessagePayload {
    /// Error information
    ErrorInfo(ErrorInfo),
    /// Resource request details
    ResourceRequest(ResourceRequestDetails),
    /// Performance data
    PerformanceData(PerformanceData),
    /// Coordination instructions
    CoordinationInstructions(CoordinationInstructions),
    /// Generic data
    Generic(Vec<u8>),
}

/// Error information payload
#[derive(Debug, Clone)]
pub struct ErrorInfo {
    /// Error type
    pub error_type: String,
    /// Error location
    pub location: Vec<usize>,
    /// Error severity
    pub severity: f64,
    /// Suggested correction
    pub suggested_correction: Option<String>,
}

/// Resource request details
#[derive(Debug, Clone)]
pub struct ResourceRequestDetails {
    /// Requested resources
    pub requested_resources: HashMap<String, f64>,
    /// Request priority
    pub priority: u8,
    /// Request deadline
    pub deadline: Option<Instant>,
    /// Justification
    pub justification: String,
}

/// Performance data payload
#[derive(Debug, Clone)]
pub struct PerformanceData {
    /// Performance metrics
    pub metrics: HashMap<String, f64>,
    /// Timestamp
    pub timestamp: Instant,
    /// Data source
    pub source: String,
}

/// Coordination instructions
#[derive(Debug, Clone)]
pub struct CoordinationInstructions {
    /// Instructions
    pub instructions: Vec<String>,
    /// Target components
    pub targets: Vec<String>,
    /// Execution priority
    pub priority: u8,
}

/// Communication statistics
#[derive(Debug, Clone)]
pub struct CommunicationStatistics {
    /// Message throughput
    pub throughput: f64,
    /// Average latency
    pub avg_latency: Duration,
    /// Message success rate
    pub success_rate: f64,
    /// Channel utilization
    pub channel_utilization: HashMap<(usize, usize), f64>,
}

/// Coordination algorithms
#[derive(Debug)]
pub struct CoordinationAlgorithm {
    /// Algorithm identifier
    pub id: String,
    /// Coordination strategy
    pub strategy: CoordinationStrategy,
    /// Algorithm parameters
    pub parameters: HashMap<String, f64>,
}

/// Coordination strategies
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum CoordinationStrategy {
    /// Centralized coordination
    Centralized,
    /// Distributed coordination
    Distributed,
    /// Hierarchical coordination
    Hierarchical,
    /// Consensus-based coordination
    Consensus,
    /// Market-based coordination
    MarketBased,
}

impl RealTimeAdaptiveQec {
    /// Create new real-time adaptive QEC system
    #[must_use]
    pub fn new(config: AdaptiveQecConfig) -> Self {
        Self {
            config: config.clone(),
            noise_monitor: Arc::new(Mutex::new(NoiseMonitor::new())),
            protocol_manager: Arc::new(RwLock::new(AdaptiveProtocolManager::new())),
            prediction_system: Arc::new(Mutex::new(NoisePredictionSystem::new(config.ml_config))),
            performance_analyzer: Arc::new(Mutex::new(PerformanceAnalyzer::new())),
            resource_manager: Arc::new(Mutex::new(AdaptiveResourceManager::new(
                config.resource_config,
            ))),
            hierarchy_coordinator: Arc::new(Mutex::new(HierarchyCoordinator::new(
                config.hierarchy_config,
            ))),
        }
    }

    /// Start real-time adaptive QEC system
    pub fn start(&self) -> ApplicationResult<()> {
        println!("Starting real-time adaptive quantum error correction system");

        // Initialize all subsystems
        self.initialize_noise_monitoring()?;
        self.initialize_prediction_system()?;
        self.initialize_protocol_management()?;
        self.initialize_performance_analysis()?;
        self.initialize_resource_management()?;
        self.initialize_hierarchy_coordination()?;

        // Start monitoring loops
        self.start_monitoring_loops()?;

        println!("Real-time adaptive QEC system started successfully");
        Ok(())
    }

    /// Apply adaptive error correction to a problem
    pub fn apply_adaptive_correction(
        &self,
        problem: &IsingModel,
    ) -> ApplicationResult<CorrectedProblem> {
        println!("Applying adaptive error correction to Ising problem");

        // Step 1: Assess current noise conditions
        let noise_assessment = self.assess_noise_conditions()?;

        // Step 2: Predict near-future noise
        let noise_prediction = self.predict_noise_evolution(&noise_assessment)?;

        // Step 3: Select optimal correction strategy
        let correction_strategy =
            self.select_correction_strategy(problem, &noise_assessment, &noise_prediction)?;

        // Step 4: Apply correction with adaptive monitoring
        let corrected_problem =
            self.apply_correction_with_monitoring(problem, &correction_strategy)?;

        // Step 5: Update system state and learn from results
        self.update_system_state(&corrected_problem)?;

        println!("Adaptive error correction applied successfully");
        Ok(corrected_problem)
    }

    /// Assess current noise conditions
    fn assess_noise_conditions(&self) -> ApplicationResult<NoiseAssessment> {
        let noise_monitor = self.noise_monitor.lock().map_err(|_| {
            ApplicationError::OptimizationError("Failed to acquire noise monitor lock".to_string())
        })?;

        let current_noise = &noise_monitor.current_noise;

        // Analyze noise characteristics
        let noise_level = current_noise.noise_level;
        let noise_type = current_noise.noise_type.clone();
        let temporal_correlation = current_noise.temporal_correlation;

        // Classify noise severity
        let severity = if noise_level < 0.01 {
            NoiseSeverity::Low
        } else if noise_level < 0.05 {
            NoiseSeverity::Medium
        } else {
            NoiseSeverity::High
        };

        Ok(NoiseAssessment {
            current_noise: current_noise.clone(),
            severity,
            trends: self.analyze_noise_trends(&noise_monitor)?,
            confidence: 0.9,
            timestamp: Instant::now(),
        })
    }

    /// Analyze noise trends from history
    fn analyze_noise_trends(&self, noise_monitor: &NoiseMonitor) -> ApplicationResult<NoiseTrends> {
        let history = &noise_monitor.noise_history;

        if history.len() < 2 {
            return Ok(NoiseTrends {
                direction: TrendDirection::Stable,
                rate: 0.0,
                confidence: 0.5,
            });
        }

        // Simple trend analysis
        let recent = &history[history.len() - 1];
        let previous = &history[history.len() - 2];

        let noise_change = recent.noise_level - previous.noise_level;
        let direction = if noise_change > 0.001 {
            TrendDirection::Increasing
        } else if noise_change < -0.001 {
            TrendDirection::Decreasing
        } else {
            TrendDirection::Stable
        };

        Ok(NoiseTrends {
            direction,
            rate: noise_change.abs(),
            confidence: 0.8,
        })
    }

    /// Predict noise evolution
    fn predict_noise_evolution(
        &self,
        assessment: &NoiseAssessment,
    ) -> ApplicationResult<NoisePrediction> {
        let prediction_system = self.prediction_system.lock().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire prediction system lock".to_string(),
            )
        })?;

        // Use ensemble prediction
        let predicted_noise_level = match assessment.trends.direction {
            TrendDirection::Increasing => assessment
                .trends
                .rate
                .mul_add(2.0, assessment.current_noise.noise_level),
            TrendDirection::Decreasing => assessment
                .trends
                .rate
                .mul_add(-2.0, assessment.current_noise.noise_level)
                .max(0.0),
            TrendDirection::Stable => assessment.current_noise.noise_level,
        };

        let predicted_noise = NoiseCharacteristics {
            timestamp: Instant::now()
                + Duration::from_millis(
                    (self.config.prediction_config.accuracy_threshold * 1000.0) as u64,
                ),
            noise_level: predicted_noise_level,
            noise_type: assessment.current_noise.noise_type.clone(),
            temporal_correlation: assessment.current_noise.temporal_correlation,
            spatial_correlation: assessment.current_noise.spatial_correlation,
            noise_spectrum: assessment.current_noise.noise_spectrum.clone(),
            per_qubit_error_rates: assessment.current_noise.per_qubit_error_rates.clone(),
            coherence_times: assessment.current_noise.coherence_times.clone(),
            gate_fidelities: assessment.current_noise.gate_fidelities.clone(),
        };

        Ok(NoisePrediction {
            predicted_noise,
            confidence: 0.85,
            horizon: Duration::from_secs(10),
            uncertainty_bounds: (predicted_noise_level * 0.9, predicted_noise_level * 1.1),
        })
    }

    /// Select optimal correction strategy
    fn select_correction_strategy(
        &self,
        problem: &IsingModel,
        noise_assessment: &NoiseAssessment,
        noise_prediction: &NoisePrediction,
    ) -> ApplicationResult<ErrorCorrectionStrategy> {
        let problem_size = problem.num_qubits;
        let noise_level = noise_assessment.current_noise.noise_level;

        // Strategy selection based on problem and noise characteristics
        let strategy = match (problem_size, noise_level) {
            (size, noise) if size <= 100 && noise < 0.01 => {
                // Small problem, low noise: minimal correction
                ErrorCorrectionStrategy::Detection(DetectionConfig {
                    threshold: 0.01,
                    method: DetectionMethod::Parity,
                    action: DetectionAction::Flag,
                })
            }
            (size, noise) if size < 500 && noise < 0.05 => {
                // Medium problem, medium noise: hybrid approach
                ErrorCorrectionStrategy::Hybrid(HybridConfig {
                    detection: DetectionConfig {
                        threshold: 0.02,
                        method: DetectionMethod::Syndrome,
                        action: DetectionAction::SwitchProtocol,
                    },
                    correction: CorrectionConfig {
                        code: ErrorCorrectionCode::SurfaceCode,
                        threshold: 0.05,
                        max_attempts: 3,
                        efficiency_target: 0.9,
                    },
                    switching_criteria: SwitchingCriteria {
                        error_rate_threshold: 0.03,
                        performance_threshold: 0.8,
                        resource_threshold: 0.5,
                        time_based: Some(Duration::from_secs(5)),
                    },
                })
            }
            _ => {
                // Large problem or high noise: full correction
                ErrorCorrectionStrategy::Correction(CorrectionConfig {
                    code: ErrorCorrectionCode::SurfaceCode,
                    threshold: 0.1,
                    max_attempts: 5,
                    efficiency_target: 0.95,
                })
            }
        };

        println!(
            "Selected error correction strategy based on problem size {problem_size} and noise level {noise_level:.4}"
        );
        Ok(strategy)
    }

    /// Apply correction with real-time monitoring
    fn apply_correction_with_monitoring(
        &self,
        problem: &IsingModel,
        strategy: &ErrorCorrectionStrategy,
    ) -> ApplicationResult<CorrectedProblem> {
        let start_time = Instant::now();

        // Apply the selected strategy
        let corrected_data = match strategy {
            ErrorCorrectionStrategy::None => {
                // No correction applied
                CorrectionResult {
                    corrected_problem: problem.clone(),
                    correction_overhead: 0.0,
                    errors_detected: 0,
                    errors_corrected: 0,
                }
            }
            ErrorCorrectionStrategy::Detection(config) => {
                self.apply_detection_only(problem, config)?
            }
            ErrorCorrectionStrategy::Correction(config) => {
                self.apply_full_correction(problem, config)?
            }
            ErrorCorrectionStrategy::Hybrid(config) => {
                self.apply_hybrid_correction(problem, config)?
            }
            ErrorCorrectionStrategy::Adaptive(config) => {
                self.apply_adaptive_strategy(problem, config)?
            }
        };

        let execution_time = start_time.elapsed();

        Ok(CorrectedProblem {
            original_problem: problem.clone(),
            corrected_problem: corrected_data.corrected_problem,
            correction_metadata: CorrectionMetadata {
                strategy_used: strategy.clone(),
                execution_time,
                correction_overhead: corrected_data.correction_overhead,
                errors_detected: corrected_data.errors_detected,
                errors_corrected: corrected_data.errors_corrected,
                confidence: 0.9,
            },
        })
    }

    /// Apply detection-only strategy
    fn apply_detection_only(
        &self,
        problem: &IsingModel,
        config: &DetectionConfig,
    ) -> ApplicationResult<CorrectionResult> {
        // Simulate error detection
        thread::sleep(Duration::from_millis(5));

        let errors_detected = (problem.num_qubits as f64 * 0.01) as usize;

        Ok(CorrectionResult {
            corrected_problem: problem.clone(),
            correction_overhead: 0.05,
            errors_detected,
            errors_corrected: 0,
        })
    }

    /// Apply full error correction
    fn apply_full_correction(
        &self,
        problem: &IsingModel,
        config: &CorrectionConfig,
    ) -> ApplicationResult<CorrectionResult> {
        // Simulate full error correction
        thread::sleep(Duration::from_millis(20));

        let errors_detected = (problem.num_qubits as f64 * 0.02) as usize;
        let errors_corrected = (errors_detected as f64 * 0.9) as usize;

        Ok(CorrectionResult {
            corrected_problem: problem.clone(),
            correction_overhead: 0.2,
            errors_detected,
            errors_corrected,
        })
    }

    /// Apply hybrid correction strategy
    fn apply_hybrid_correction(
        &self,
        problem: &IsingModel,
        config: &HybridConfig,
    ) -> ApplicationResult<CorrectionResult> {
        // Start with detection
        let detection_result = self.apply_detection_only(problem, &config.detection)?;

        // Decide whether to proceed with correction
        let should_correct = detection_result.errors_detected > 0;

        if should_correct {
            let correction_result = self.apply_full_correction(problem, &config.correction)?;
            Ok(CorrectionResult {
                corrected_problem: correction_result.corrected_problem,
                correction_overhead: detection_result.correction_overhead
                    + correction_result.correction_overhead,
                errors_detected: detection_result.errors_detected,
                errors_corrected: correction_result.errors_corrected,
            })
        } else {
            Ok(detection_result)
        }
    }

    /// Apply adaptive strategy selection
    fn apply_adaptive_strategy(
        &self,
        problem: &IsingModel,
        config: &AdaptiveStrategyConfig,
    ) -> ApplicationResult<CorrectionResult> {
        // Select best strategy from available options
        if let Some(best_strategy) = config.available_strategies.first() {
            match best_strategy {
                ErrorCorrectionStrategy::Detection(det_config) => {
                    self.apply_detection_only(problem, det_config)
                }
                ErrorCorrectionStrategy::Correction(corr_config) => {
                    self.apply_full_correction(problem, corr_config)
                }
                _ => {
                    // Default to detection
                    self.apply_detection_only(
                        problem,
                        &DetectionConfig {
                            threshold: 0.01,
                            method: DetectionMethod::Parity,
                            action: DetectionAction::Flag,
                        },
                    )
                }
            }
        } else {
            Err(ApplicationError::InvalidConfiguration(
                "No strategies available for adaptive selection".to_string(),
            ))
        }
    }

    /// Update system state based on results
    fn update_system_state(&self, corrected_problem: &CorrectedProblem) -> ApplicationResult<()> {
        // Update performance metrics
        let mut performance_analyzer = self.performance_analyzer.lock().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire performance analyzer lock".to_string(),
            )
        })?;

        performance_analyzer.update_performance(&corrected_problem.correction_metadata);

        // Update resource allocation if needed
        let mut resource_manager = self.resource_manager.lock().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire resource manager lock".to_string(),
            )
        })?;

        resource_manager
            .update_allocation_based_on_performance(&corrected_problem.correction_metadata);

        Ok(())
    }

    /// Initialize subsystems
    fn initialize_noise_monitoring(&self) -> ApplicationResult<()> {
        println!("Initializing noise monitoring subsystem");
        Ok(())
    }

    fn initialize_prediction_system(&self) -> ApplicationResult<()> {
        println!("Initializing noise prediction subsystem");
        Ok(())
    }

    fn initialize_protocol_management(&self) -> ApplicationResult<()> {
        println!("Initializing adaptive protocol management");
        Ok(())
    }

    fn initialize_performance_analysis(&self) -> ApplicationResult<()> {
        println!("Initializing performance analysis subsystem");
        Ok(())
    }

    fn initialize_resource_management(&self) -> ApplicationResult<()> {
        println!("Initializing adaptive resource management");
        Ok(())
    }

    fn initialize_hierarchy_coordination(&self) -> ApplicationResult<()> {
        println!("Initializing hierarchy coordination");
        Ok(())
    }

    fn start_monitoring_loops(&self) -> ApplicationResult<()> {
        println!("Starting real-time monitoring loops");
        // In a real implementation, this would start background threads
        Ok(())
    }

    /// Get current system performance metrics
    pub fn get_performance_metrics(&self) -> ApplicationResult<AdaptiveQecMetrics> {
        let performance_analyzer = self.performance_analyzer.lock().map_err(|_| {
            ApplicationError::OptimizationError(
                "Failed to acquire performance analyzer lock".to_string(),
            )
        })?;

        Ok(AdaptiveQecMetrics {
            correction_efficiency: performance_analyzer.metrics.correction_efficiency,
            adaptation_responsiveness: performance_analyzer.metrics.adaptation_responsiveness,
            prediction_accuracy: performance_analyzer.metrics.prediction_accuracy,
            resource_efficiency: performance_analyzer.metrics.resource_efficiency,
            overall_performance: performance_analyzer.metrics.overall_performance,
        })
    }
}

// Helper types and implementations

/// Noise assessment result
#[derive(Debug, Clone)]
pub struct NoiseAssessment {
    pub current_noise: NoiseCharacteristics,
    pub severity: NoiseSeverity,
    pub trends: NoiseTrends,
    pub confidence: f64,
    pub timestamp: Instant,
}

/// Noise severity levels
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NoiseSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Noise trend analysis
#[derive(Debug, Clone)]
pub struct NoiseTrends {
    pub direction: TrendDirection,
    pub rate: f64,
    pub confidence: f64,
}

/// Trend directions
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    Stable,
}

/// Noise prediction result
#[derive(Debug, Clone)]
pub struct NoisePrediction {
    pub predicted_noise: NoiseCharacteristics,
    pub confidence: f64,
    pub horizon: Duration,
    pub uncertainty_bounds: (f64, f64),
}

/// Correction result
#[derive(Debug, Clone)]
pub struct CorrectionResult {
    pub corrected_problem: IsingModel,
    pub correction_overhead: f64,
    pub errors_detected: usize,
    pub errors_corrected: usize,
}

/// Final corrected problem with metadata
#[derive(Debug, Clone)]
pub struct CorrectedProblem {
    pub original_problem: IsingModel,
    pub corrected_problem: IsingModel,
    pub correction_metadata: CorrectionMetadata,
}

/// Correction metadata
#[derive(Debug, Clone)]
pub struct CorrectionMetadata {
    pub strategy_used: ErrorCorrectionStrategy,
    pub execution_time: Duration,
    pub correction_overhead: f64,
    pub errors_detected: usize,
    pub errors_corrected: usize,
    pub confidence: f64,
}

/// Adaptive QEC performance metrics
#[derive(Debug, Clone)]
pub struct AdaptiveQecMetrics {
    pub correction_efficiency: f64,
    pub adaptation_responsiveness: f64,
    pub prediction_accuracy: f64,
    pub resource_efficiency: f64,
    pub overall_performance: f64,
}

// Implementation of helper structs

impl NoiseMonitor {
    fn new() -> Self {
        Self {
            current_noise: NoiseCharacteristics {
                timestamp: Instant::now(),
                noise_level: 0.01,
                noise_type: NoiseType::White,
                temporal_correlation: 0.1,
                spatial_correlation: 0.1,
                noise_spectrum: vec![0.01; 10],
                per_qubit_error_rates: vec![0.001; 100],
                coherence_times: vec![100.0; 100],
                gate_fidelities: HashMap::new(),
            },
            noise_history: VecDeque::new(),
            sensors: vec![],
            analyzers: vec![],
        }
    }
}

impl AdaptiveProtocolManager {
    fn new() -> Self {
        Self {
            active_protocols: HashMap::new(),
            protocol_history: VecDeque::new(),
            adaptation_engine: AdaptationEngine {
                algorithm: AdaptationAlgorithm::RuleBased,
                parameters: HashMap::new(),
                decision_history: VecDeque::new(),
            },
        }
    }
}

impl NoisePredictionSystem {
    fn new(ml_config: MLNoiseConfig) -> Self {
        Self {
            models: HashMap::new(),
            ensemble: ModelEnsemble {
                method: EnsembleMethod::WeightedAverage,
                weights: HashMap::new(),
                performance_history: VecDeque::new(),
            },
            prediction_cache: HashMap::new(),
        }
    }
}

impl PerformanceAnalyzer {
    fn new() -> Self {
        Self {
            metrics: PerformanceMetrics {
                correction_efficiency: 0.9,
                resource_efficiency: 0.8,
                adaptation_responsiveness: 0.85,
                prediction_accuracy: 0.8,
                overall_performance: 0.85,
                performance_history: VecDeque::new(),
            },
            analyzers: vec![],
            benchmarks: HashMap::new(),
        }
    }

    fn update_performance(&mut self, metadata: &CorrectionMetadata) {
        // Update performance metrics based on correction results
        let efficiency = metadata.errors_corrected as f64 / metadata.errors_detected.max(1) as f64;
        self.metrics.correction_efficiency = self
            .metrics
            .correction_efficiency
            .mul_add(0.9, efficiency * 0.1);

        let resource_efficiency = 1.0 / (1.0 + metadata.correction_overhead);
        self.metrics.resource_efficiency = self
            .metrics
            .resource_efficiency
            .mul_add(0.9, resource_efficiency * 0.1);

        // Update overall performance
        self.metrics.overall_performance = self.metrics.prediction_accuracy.mul_add(
            0.2,
            self.metrics.adaptation_responsiveness.mul_add(
                0.2,
                self.metrics
                    .correction_efficiency
                    .mul_add(0.3, self.metrics.resource_efficiency * 0.3),
            ),
        );
    }
}

impl AdaptiveResourceManager {
    fn new(config: ResourceManagementConfig) -> Self {
        Self {
            allocation: ResourceAllocation {
                allocation_map: HashMap::new(),
                total_allocated: 0.0,
                available_resources: 100.0,
                allocation_history: VecDeque::new(),
            },
            constraints: ResourceConstraints {
                max_total: 100.0,
                per_component: HashMap::new(),
                min_performance: 0.8,
                enforcement_method: ConstraintEnforcement::Soft,
            },
            optimizers: vec![],
        }
    }

    fn update_allocation_based_on_performance(&mut self, metadata: &CorrectionMetadata) {
        // Adjust resource allocation based on performance
        let performance_score =
            metadata.errors_corrected as f64 / metadata.errors_detected.max(1) as f64;

        if performance_score < 0.8 {
            // Increase resource allocation for error correction
            self.allocation
                .allocation_map
                .insert("error_correction".to_string(), 0.4);
        } else if performance_score > 0.95 && metadata.correction_overhead < 0.1 {
            // Reduce allocation if performance is excellent and overhead is low
            self.allocation
                .allocation_map
                .insert("error_correction".to_string(), 0.2);
        }
    }
}

impl HierarchyCoordinator {
    fn new(config: HierarchyConfig) -> Self {
        let mut levels = Vec::new();
        for i in 0..config.num_levels {
            levels.push(HierarchyLevel {
                id: i,
                priority: (config.num_levels - i) as u8,
                protocols: vec![],
                resources: config.level_resources.get(i).copied().unwrap_or(0.1),
                performance: LevelPerformance {
                    accuracy: 0.9,
                    response_time: Duration::from_millis(10 * (i + 1) as u64),
                    efficiency: 0.8,
                    coordination_effectiveness: 0.85,
                },
            });
        }

        Self {
            levels,
            communication: HierarchyCommunicationManager {
                channels: HashMap::new(),
                message_queues: HashMap::new(),
                statistics: CommunicationStatistics {
                    throughput: 100.0,
                    avg_latency: Duration::from_millis(5),
                    success_rate: 0.99,
                    channel_utilization: HashMap::new(),
                },
            },
            coordinators: vec![],
        }
    }
}

/// Create example real-time adaptive QEC system
pub fn create_example_adaptive_qec() -> ApplicationResult<RealTimeAdaptiveQec> {
    let config = AdaptiveQecConfig::default();
    let system = RealTimeAdaptiveQec::new(config);

    // Start the system
    system.start()?;

    Ok(system)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_adaptive_qec_creation() {
        let config = AdaptiveQecConfig::default();
        let system = RealTimeAdaptiveQec::new(config);

        // Basic system creation test
        assert_eq!(
            system.config.monitoring_interval,
            Duration::from_millis(100)
        );
    }

    #[test]
    fn test_noise_assessment() {
        let system = create_example_adaptive_qec().expect("Failed to create adaptive QEC system");
        let assessment = system
            .assess_noise_conditions()
            .expect("Failed to assess noise conditions");

        assert!(assessment.confidence > 0.0);
        assert!(assessment.confidence <= 1.0);
    }

    #[test]
    fn test_strategy_selection() {
        let system = create_example_adaptive_qec().expect("Failed to create adaptive QEC system");
        let problem = IsingModel::new(100);

        let noise_assessment = NoiseAssessment {
            current_noise: NoiseCharacteristics {
                timestamp: Instant::now(),
                noise_level: 0.005,
                noise_type: NoiseType::White,
                temporal_correlation: 0.1,
                spatial_correlation: 0.1,
                noise_spectrum: vec![0.005; 10],
                per_qubit_error_rates: vec![0.0005; 100],
                coherence_times: vec![50.0; 100],
                gate_fidelities: HashMap::new(),
            },
            severity: NoiseSeverity::Low,
            trends: NoiseTrends {
                direction: TrendDirection::Stable,
                rate: 0.001,
                confidence: 0.8,
            },
            confidence: 0.9,
            timestamp: Instant::now(),
        };

        let noise_prediction = NoisePrediction {
            predicted_noise: noise_assessment.current_noise.clone(),
            confidence: 0.85,
            horizon: Duration::from_secs(10),
            uncertainty_bounds: (0.003, 0.007),
        };

        let strategy = system
            .select_correction_strategy(&problem, &noise_assessment, &noise_prediction)
            .expect("Failed to select correction strategy");

        // Should select appropriate strategy for small problem with low noise
        match &strategy {
            ErrorCorrectionStrategy::Detection(_) => assert!(true),
            ErrorCorrectionStrategy::Hybrid(_) => {
                assert!(false, "Got hybrid strategy instead of detection")
            }
            ErrorCorrectionStrategy::Correction(_) => {
                assert!(false, "Got correction strategy instead of detection")
            }
            _ => assert!(
                false,
                "Expected detection strategy for low noise, got: {:?}",
                strategy
            ),
        }
    }

    #[test]
    fn test_ml_config() {
        let ml_config = MLNoiseConfig::default();
        assert_eq!(ml_config.network_architecture, NeuralArchitecture::LSTM);
        assert_eq!(ml_config.training_window, 1000);
        assert!(ml_config.enable_neural_prediction);
    }

    #[test]
    fn test_hierarchy_config() {
        let hierarchy_config = HierarchyConfig::default();
        assert_eq!(hierarchy_config.num_levels, 3);
        assert_eq!(hierarchy_config.level_thresholds.len(), 3);
        assert!(hierarchy_config.enable_hierarchy);
    }

    #[test]
    fn test_performance_metrics_update() {
        let mut analyzer = PerformanceAnalyzer::new();

        let metadata = CorrectionMetadata {
            strategy_used: ErrorCorrectionStrategy::Detection(DetectionConfig {
                threshold: 0.01,
                method: DetectionMethod::Parity,
                action: DetectionAction::Flag,
            }),
            execution_time: Duration::from_millis(10),
            correction_overhead: 0.1,
            errors_detected: 5,
            errors_corrected: 4,
            confidence: 0.9,
        };

        let initial_efficiency = analyzer.metrics.correction_efficiency;
        analyzer.update_performance(&metadata);

        // Performance should be updated
        assert!(analyzer.metrics.correction_efficiency >= 0.0);
        assert!(analyzer.metrics.correction_efficiency <= 1.0);
    }
}