//! Quantum-Classical Hybrid Processing Module
//!
//! This module implements sophisticated quantum-classical hybrid processing algorithms
//! for enhanced image processing capabilities. It provides seamless integration between
//! quantum and classical computing paradigms, offering unprecedented performance and
//! capabilities through advanced hybrid architectures.
//!
//! # Key Features
//!
//! - **Advanced Hybrid Processors**: Sophisticated quantum-classical processing units
//! - **Error Correction Systems**: Quantum error correction integrated with classical processing
//! - **Performance Optimization**: Adaptive algorithm selection and auto-tuning
//! - **Bridge Controllers**: Seamless quantum-classical data conversion and synchronization
//! - **Resource Management**: Intelligent load balancing and resource allocation
//! - **Learning Systems**: Adaptive hybrid algorithm learning and optimization
//!
//! # Architecture
//!
//! The hybrid processing system consists of several key components:
//!
//! - `QuantumClassicalHybridProcessor`: Main processing controller
//! - `QuantumProcessingUnit` and `ClassicalProcessingUnit`: Specialized processing units
//! - `HybridBridgeController`: Interface management between quantum and classical systems
//! - `QuantumErrorCorrectionSystem`: Quantum error correction and noise mitigation
//! - `AdaptiveAlgorithmSelector`: Intelligent algorithm selection and adaptation
//! - `HybridPerformanceOptimizer`: Performance monitoring and optimization
//!
//! # Usage
//!
//! ```rust,ignore
//! use scirs2_ndimage::quantum_neuromorphic_fusion::hybrid_processing::*;
//! use scirs2_core::ndarray::Array2;
//!
//! // Create hybrid configuration
//! let hybrid_config = QuantumClassicalHybridConfig::default();
//! let quantum_neuro_config = QuantumNeuromorphicConfig::default();
//!
//! // Process image with hybrid algorithm
//! let image = Array2::ones((100, 100));
//! let result = advanced_quantum_classical_hybrid_processing(
//!     image.view(),
//!     &quantum_neuro_config,
//!     &hybrid_config
//! );
//! ```

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use std::collections::{HashMap, VecDeque};

use super::config::*;
use crate::error::{NdimageError, NdimageResult};

// ================================================================================================
// CORE HYBRID PROCESSING STRUCTURES
// ================================================================================================

/// Advanced Quantum-Classical Hybrid Processor
///
/// This system represents the next evolution in quantum-classical integration,
/// implementing sophisticated algorithms that seamlessly blend quantum and
/// classical processing paradigms for enhanced image processing capabilities.
#[derive(Debug, Clone)]
pub struct QuantumClassicalHybridProcessor {
    /// Quantum processing units
    pub quantum_units: Vec<QuantumProcessingUnit>,
    /// Classical processing units
    pub classical_units: Vec<ClassicalProcessingUnit>,
    /// Hybrid bridge controller
    pub bridge_controller: HybridBridgeController,
    /// Quantum error correction system
    pub error_correction: QuantumErrorCorrectionSystem,
    /// Adaptive algorithm selector
    pub algorithm_selector: AdaptiveAlgorithmSelector,
    /// Performance optimizer
    pub performance_optimizer: HybridPerformanceOptimizer,
}

/// Quantum Processing Unit
#[derive(Debug, Clone)]
pub struct QuantumProcessingUnit {
    /// Unit ID
    pub id: String,
    /// Quantum state registers
    pub quantum_registers: Array2<Complex<f64>>,
    /// Quantum gates available
    pub available_gates: Vec<QuantumGate>,
    /// Coherence time remaining
    pub coherence_time: f64,
    /// Error rate
    pub error_rate: f64,
    /// Processing capacity
    pub capacity: f64,
}

/// Classical Processing Unit
#[derive(Debug, Clone)]
pub struct ClassicalProcessingUnit {
    /// Unit ID
    pub id: String,
    /// Processing cores
    pub cores: usize,
    /// Memory capacity
    pub memory_capacity: usize,
    /// Processing algorithms
    pub algorithms: Vec<ClassicalAlgorithm>,
    /// Performance metrics
    pub performancemetrics: ClassicalPerformanceMetrics,
}

// ================================================================================================
// QUANTUM GATE AND ALGORITHM STRUCTURES
// ================================================================================================

/// Quantum Gate representation
#[derive(Debug, Clone)]
pub struct QuantumGate {
    /// Gate type
    pub gate_type: QuantumGateType,
    /// Gate matrix
    pub matrix: Array2<Complex<f64>>,
    /// Fidelity
    pub fidelity: f64,
    /// Execution time
    pub execution_time: f64,
}

/// Quantum Gate Types
#[derive(Debug, Clone)]
pub enum QuantumGateType {
    Hadamard,
    PauliX,
    PauliY,
    PauliZ,
    CNOT,
    Toffoli,
    Phase { angle: f64 },
    Rotation { axis: String, angle: f64 },
    Custom { name: String },
}

/// Classical Algorithm Types
#[derive(Debug, Clone)]
pub enum ClassicalAlgorithm {
    Convolution { kernel_size: usize },
    FourierTransform,
    Filtering { filter_type: String },
    Morphology { operation: String },
    MachineLearning { model_type: String },
    Custom { name: String, parameters: Vec<f64> },
}

/// Classical Performance Metrics
#[derive(Debug, Clone)]
pub struct ClassicalPerformanceMetrics {
    /// FLOPS (Floating Point Operations Per Second)
    pub flops: f64,
    /// Memory bandwidth
    pub memory_bandwidth: f64,
    /// Cache hit rate
    pub cache_hit_rate: f64,
    /// Power consumption
    pub power_consumption: f64,
}

// ================================================================================================
// BRIDGE CONTROLLER AND INTERFACE STRUCTURES
// ================================================================================================

/// Hybrid Bridge Controller
#[derive(Debug, Clone)]
pub struct HybridBridgeController {
    /// Quantum-classical interface protocols
    pub interface_protocols: Vec<InterfaceProtocol>,
    /// Data conversion pipelines
    pub conversion_pipelines: Vec<DataConversionPipeline>,
    /// Synchronization mechanisms
    pub sync_mechanisms: Vec<SynchronizationMechanism>,
    /// Load balancing strategies
    pub load_balancer: LoadBalancingStrategy,
}

/// Interface Protocol
#[derive(Debug, Clone)]
pub struct InterfaceProtocol {
    /// Protocol name
    pub name: String,
    /// Quantum side configuration
    pub quantum_config: QuantumInterfaceConfig,
    /// Classical side configuration
    pub classical_config: ClassicalInterfaceConfig,
    /// Latency characteristics
    pub latency: f64,
    /// Throughput characteristics
    pub throughput: f64,
}

/// Quantum Interface Configuration
#[derive(Debug, Clone)]
pub struct QuantumInterfaceConfig {
    /// State preparation method
    pub state_preparation: String,
    /// Measurement strategy
    pub measurement_strategy: String,
    /// Decoherence mitigation
    pub decoherence_mitigation: bool,
}

/// Classical Interface Configuration
#[derive(Debug, Clone)]
pub struct ClassicalInterfaceConfig {
    /// Data format
    pub data_format: String,
    /// Precision level
    pub precision: usize,
    /// Buffer size
    pub buffer_size: usize,
}

/// Data Conversion Pipeline
#[derive(Debug, Clone)]
pub struct DataConversionPipeline {
    /// Pipeline ID
    pub id: String,
    /// Conversion stages
    pub stages: Vec<ConversionStage>,
    /// Error handling strategy
    pub error_handling: ErrorHandlingStrategy,
    /// Performance metrics
    pub metrics: ConversionMetrics,
}

/// Conversion Stage
#[derive(Debug, Clone)]
pub struct ConversionStage {
    /// Stage name
    pub name: String,
    /// Conversion function
    pub function_type: ConversionFunction,
    /// Input format
    pub input_format: DataFormat,
    /// Output format
    pub output_format: DataFormat,
}

/// Conversion Function Types
#[derive(Debug, Clone)]
pub enum ConversionFunction {
    QuantumToClassical { method: String },
    ClassicalToQuantum { encoding: String },
    QuantumToQuantum { transformation: String },
    ClassicalToClassical { preprocessing: String },
}

/// Data Format Types
#[derive(Debug, Clone)]
pub enum DataFormat {
    QuantumState {
        dimensions: usize,
    },
    ClassicalArray {
        dtype: String,
        shape: Vec<usize>,
    },
    CompressedQuantum {
        compression_ratio: f64,
    },
    HybridRepresentation {
        quantum_part: f64,
        classical_part: f64,
    },
}

/// Error Handling Strategy
#[derive(Debug, Clone)]
pub enum ErrorHandlingStrategy {
    Retry { max_attempts: usize },
    Fallback { fallback_method: String },
    Graceful { degradation_factor: f64 },
    Abort,
}

/// Conversion Metrics
#[derive(Debug, Clone)]
pub struct ConversionMetrics {
    /// Conversion accuracy
    pub accuracy: f64,
    /// Processing time
    pub processing_time: f64,
    /// Resource usage
    pub resource_usage: f64,
    /// Error rate
    pub error_rate: f64,
}

/// Synchronization Mechanism
#[derive(Debug, Clone)]
pub struct SynchronizationMechanism {
    /// Mechanism type
    pub mechanism_type: SyncMechanismType,
    /// Synchronization accuracy
    pub accuracy: f64,
    /// Overhead cost
    pub overhead: f64,
}

/// Synchronization Mechanism Types
#[derive(Debug, Clone)]
pub enum SyncMechanismType {
    TimeStamp { precision: usize },
    EventDriven { event_types: Vec<String> },
    Barrier { participant_count: usize },
    ClockSync { frequency: f64 },
}

/// Load Balancing Strategy
#[derive(Debug, Clone)]
pub struct LoadBalancingStrategy {
    /// Strategy type
    pub strategy_type: LoadBalancingType,
    /// Decision criteria
    pub criteria: Vec<DecisionCriterion>,
    /// Adaptation parameters
    pub adaptation_params: AdaptationParameters,
}

/// Load Balancing Types
#[derive(Debug, Clone)]
pub enum LoadBalancingType {
    Static { fixed_ratios: Vec<f64> },
    Dynamic { adjustment_rate: f64 },
    Predictive { prediction_horizon: usize },
    Adaptive { learning_rate: f64 },
}

/// Decision Criterion
#[derive(Debug, Clone)]
pub struct DecisionCriterion {
    /// Criterion name
    pub name: String,
    /// Weight in decision
    pub weight: f64,
    /// Measurement method
    pub measurement: String,
    /// Target value
    pub target: f64,
}

/// Adaptation Parameters
#[derive(Debug, Clone)]
pub struct AdaptationParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Momentum factor
    pub momentum: f64,
    /// Regularization strength
    pub regularization: f64,
    /// Update frequency
    pub update_frequency: usize,
}

// ================================================================================================
// ERROR CORRECTION SYSTEM
// ================================================================================================

/// Quantum Error Correction System
#[derive(Debug, Clone)]
pub struct QuantumErrorCorrectionSystem {
    /// Error correction codes
    pub error_codes: Vec<QuantumErrorCode>,
    /// Syndrome detection circuits
    pub syndrome_detectors: Vec<SyndromeDetector>,
    /// Correction procedures
    pub correction_procedures: Vec<CorrectionProcedure>,
    /// Performance monitoring
    pub performance_monitor: ErrorCorrectionMonitor,
}

/// Quantum Error Correction Code
#[derive(Debug, Clone)]
pub struct QuantumErrorCode {
    /// Code name
    pub name: String,
    /// Code parameters [n, k, d] (length, dimension, distance)
    pub parameters: [usize; 3],
    /// Stabilizer generators
    pub stabilizers: Vec<Array1<Complex<f64>>>,
    /// Logical operators
    pub logical_operators: Vec<Array2<Complex<f64>>>,
    /// Threshold error rate
    pub threshold: f64,
}

/// Syndrome Detector
#[derive(Debug, Clone)]
pub struct SyndromeDetector {
    /// Detector ID
    pub id: String,
    /// Detection circuit
    pub circuit: Vec<QuantumGate>,
    /// Measurement pattern
    pub measurement_pattern: Array1<usize>,
    /// Detection fidelity
    pub fidelity: f64,
}

/// Correction Procedure
#[derive(Debug, Clone)]
pub struct CorrectionProcedure {
    /// Procedure ID
    pub id: String,
    /// Error syndrome pattern
    pub syndrome_pattern: Array1<usize>,
    /// Correction gates
    pub correction_gates: Vec<QuantumGate>,
    /// Success probability
    pub success_probability: f64,
}

/// Error Correction Performance Monitor
#[derive(Debug, Clone)]
pub struct ErrorCorrectionMonitor {
    /// Error rates by type
    pub error_rates: HashMap<String, f64>,
    /// Correction success rates
    pub correction_rates: HashMap<String, f64>,
    /// Resource overhead
    pub overheadmetrics: OverheadMetrics,
    /// Performance trends
    pub trends: PerformanceTrends,
}

/// Overhead Metrics
#[derive(Debug, Clone)]
pub struct OverheadMetrics {
    /// Time overhead
    pub time_overhead: f64,
    /// Space overhead
    pub space_overhead: f64,
    /// Energy overhead
    pub energy_overhead: f64,
}

/// Performance Trends
#[derive(Debug, Clone)]
pub struct PerformanceTrends {
    /// Error rate trend
    pub error_trend: Vec<f64>,
    /// Correction rate trend
    pub correction_trend: Vec<f64>,
    /// Efficiency trend
    pub efficiency_trend: Vec<f64>,
}

// ================================================================================================
// ADAPTIVE ALGORITHM SELECTION
// ================================================================================================

/// Adaptive Algorithm Selector
#[derive(Debug, Clone)]
pub struct AdaptiveAlgorithmSelector {
    /// Available hybrid algorithms
    pub algorithms: Vec<HybridAlgorithm>,
    /// Selection criteria
    pub selection_criteria: Vec<SelectionCriterion>,
    /// Performance predictor
    pub performance_predictor: PerformancePredictor,
    /// Learning system
    pub learning_system: AlgorithmLearningSystem,
}

/// Hybrid Algorithm
#[derive(Debug, Clone)]
pub struct HybridAlgorithm {
    /// Algorithm ID
    pub id: String,
    /// Algorithm type
    pub algorithm_type: HybridAlgorithmType,
    /// Quantum component weight
    pub quantum_weight: f64,
    /// Classical component weight
    pub classical_weight: f64,
    /// Expected performance
    pub expected_performance: PerformanceProfile,
    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Hybrid Algorithm Types
#[derive(Debug, Clone)]
pub enum HybridAlgorithmType {
    QuantumEnhancedClassical { enhancement_factor: f64 },
    ClassicalAugmentedQuantum { augmentation_type: String },
    InterleavedExecution { interleaving_pattern: Vec<String> },
    ParallelExecution { parallelism_degree: usize },
    AdaptiveHybrid { adaptation_strategy: String },
}

/// Performance Profile
#[derive(Debug, Clone)]
pub struct PerformanceProfile {
    /// Accuracy metrics
    pub accuracy: f64,
    /// Speed metrics
    pub speed: f64,
    /// Resource efficiency
    pub efficiency: f64,
    /// Robustness metrics
    pub robustness: f64,
}

/// Resource Requirements
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Quantum resources
    pub quantum_resources: QuantumResourceReq,
    /// Classical resources
    pub classical_resources: ClassicalResourceReq,
    /// Communication overhead
    pub communication_overhead: f64,
}

/// Quantum Resource Requirements
#[derive(Debug, Clone)]
pub struct QuantumResourceReq {
    /// Number of qubits
    pub qubits: usize,
    /// Circuit depth
    pub depth: usize,
    /// Gate count
    pub gates: usize,
    /// Required fidelity
    pub fidelity: f64,
}

/// Classical Resource Requirements
#[derive(Debug, Clone)]
pub struct ClassicalResourceReq {
    /// CPU cores
    pub cpu_cores: usize,
    /// Memory in MB
    pub memory_mb: usize,
    /// Storage in MB
    pub storage_mb: usize,
    /// Bandwidth in Mbps
    pub bandwidth_mbps: f64,
}

/// Selection Criterion
#[derive(Debug, Clone)]
pub struct SelectionCriterion {
    /// Criterion name
    pub name: String,
    /// Importance weight
    pub weight: f64,
    /// Evaluation function
    pub evaluation_function: String,
    /// Target range
    pub target_range: (f64, f64),
}

/// Performance Predictor
#[derive(Debug, Clone)]
pub struct PerformancePredictor {
    /// Prediction model
    pub model: PredictionModel,
    /// Historical data
    pub historical_data: Vec<PerformanceDataPoint>,
    /// Prediction accuracy
    pub accuracy: f64,
    /// Update frequency
    pub update_frequency: usize,
}

/// Prediction Model Types
#[derive(Debug, Clone)]
pub enum PredictionModel {
    LinearRegression { coefficients: Vec<f64> },
    NeuralNetwork { layers: Vec<usize> },
    RandomForest { trees: usize },
    GaussianProcess { kernel: String },
}

/// Performance Data Point
#[derive(Debug, Clone)]
pub struct PerformanceDataPoint {
    /// Input characteristics
    pub inputfeatures: Vec<f64>,
    /// Algorithm used
    pub algorithm_id: String,
    /// Measured performance
    pub performance: PerformanceProfile,
    /// Timestamp
    pub timestamp: u64,
}

/// Algorithm Learning System
#[derive(Debug, Clone)]
pub struct AlgorithmLearningSystem {
    /// Learning algorithm
    pub learning_algorithm: LearningAlgorithm,
    /// Experience buffer
    pub experience_buffer: Vec<LearningExperience>,
    /// Learning parameters
    pub parameters: LearningParameters,
    /// Performance tracker
    pub tracker: LearningTracker,
}

/// Learning Algorithm Types
#[derive(Debug, Clone)]
pub enum LearningAlgorithm {
    ReinforcementLearning { algorithm: String },
    OnlineLearning { update_rule: String },
    MetaLearning { meta_algorithm: String },
    ActiveLearning { query_strategy: String },
}

/// Learning Experience
#[derive(Debug, Clone)]
pub struct LearningExperience {
    /// State representation
    pub state: Vec<f64>,
    /// Action taken
    pub action: String,
    /// Reward received
    pub reward: f64,
    /// Next state
    pub nextstate: Vec<f64>,
    /// Experience timestamp
    pub timestamp: u64,
}

/// Learning Parameters
#[derive(Debug, Clone)]
pub struct LearningParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Discount factor
    pub discount_factor: f64,
    /// Exploration rate
    pub exploration_rate: f64,
    /// Batch size
    pub batch_size: usize,
}

/// Learning Performance Tracker
#[derive(Debug, Clone)]
pub struct LearningTracker {
    /// Learning curve
    pub learning_curve: Vec<f64>,
    /// Best performance achieved
    pub best_performance: f64,
    /// Convergence status
    pub converged: bool,
    /// Learning statistics
    pub statistics: LearningStatistics,
}

/// Learning Statistics
#[derive(Debug, Clone)]
pub struct LearningStatistics {
    /// Average reward
    pub average_reward: f64,
    /// Reward variance
    pub reward_variance: f64,
    /// Exploration ratio
    pub exploration_ratio: f64,
    /// Update count
    pub update_count: usize,
}

// ================================================================================================
// PERFORMANCE OPTIMIZATION
// ================================================================================================

/// Hybrid Performance Optimizer
#[derive(Debug, Clone)]
pub struct HybridPerformanceOptimizer {
    /// Optimization strategies
    pub strategies: Vec<OptimizationStrategy>,
    /// Current optimization state
    pub state: OptimizationState,
    /// Optimization history
    pub history: Vec<OptimizationRecord>,
    /// Auto-tuning system
    pub auto_tuner: AutoTuningSystem,
}

/// Optimization Strategy
#[derive(Debug, Clone)]
pub struct OptimizationStrategy {
    /// Strategy name
    pub name: String,
    /// Target metrics
    pub targetmetrics: Vec<String>,
    /// Optimization algorithm
    pub algorithm: OptimizationAlgorithm,
    /// Constraints
    pub constraints: Vec<OptimizationConstraint>,
}

/// Optimization Algorithm Types
#[derive(Debug, Clone)]
pub enum OptimizationAlgorithm {
    GradientDescent { learning_rate: f64 },
    GeneticAlgorithm { population_size: usize },
    SimulatedAnnealing { temperature: f64 },
    BayesianOptimization { acquisition_function: String },
    ParticleSwarm { swarm_size: usize },
}

/// Optimization Constraint
#[derive(Debug, Clone)]
pub struct OptimizationConstraint {
    /// Constraint name
    pub name: String,
    /// Constraint type
    pub constraint_type: ConstraintType,
    /// Constraint value
    pub value: f64,
    /// Violation penalty
    pub penalty: f64,
}

/// Constraint Types
#[derive(Debug, Clone)]
pub enum ConstraintType {
    Equality,
    LessEqual,
    GreaterEqual,
    Range { min: f64, max: f64 },
}

/// Optimization State
#[derive(Debug, Clone)]
pub struct OptimizationState {
    /// Current parameter values
    pub parameters: HashMap<String, f64>,
    /// Current objective value
    pub objective_value: f64,
    /// Optimization iteration
    pub iteration: usize,
    /// Convergence status
    pub converged: bool,
}

/// Optimization Record
#[derive(Debug, Clone)]
pub struct OptimizationRecord {
    /// Timestamp
    pub timestamp: u64,
    /// Parameter configuration
    pub parameters: HashMap<String, f64>,
    /// Performance achieved
    pub performance: f64,
    /// Optimization method used
    pub method: String,
}

/// Auto-Tuning System
#[derive(Debug, Clone)]
pub struct AutoTuningSystem {
    /// Auto-tuning parameters
    pub parameters: AutoTuningParameters,
    /// Tuning schedule
    pub schedule: TuningSchedule,
    /// Performance monitor
    pub monitor: AutoTuningMonitor,
    /// Adaptation rules
    pub rules: Vec<AdaptationRule>,
}

/// Auto-Tuning Parameters
#[derive(Debug, Clone)]
pub struct AutoTuningParameters {
    /// Tuning frequency
    pub frequency: usize,
    /// Performance sensitivity threshold
    pub sensitivity: f64,
    /// Adaptation rate
    pub adaptation_rate: f64,
    /// Stability window size
    pub stability_window: usize,
}

/// Tuning Schedule
#[derive(Debug, Clone)]
pub struct TuningSchedule {
    /// Schedule type
    pub schedule_type: ScheduleType,
    /// Next tuning time
    pub next_tuning: u64,
    /// Tuning intervals
    pub intervals: Vec<u64>,
}

/// Schedule Types
#[derive(Debug, Clone)]
pub enum ScheduleType {
    Fixed {
        interval: u64,
    },
    Adaptive {
        base_interval: u64,
        scaling_factor: f64,
    },
    EventDriven {
        trigger_events: Vec<String>,
    },
    PerformanceBased {
        threshold: f64,
    },
}

/// Auto-Tuning Monitor
#[derive(Debug, Clone)]
pub struct AutoTuningMonitor {
    /// Performance metrics
    pub metrics: Vec<MonitoringMetric>,
    /// Alert thresholds
    pub thresholds: HashMap<String, f64>,
    /// Alert history
    pub alerts: Vec<PerformanceAlert>,
}

/// Monitoring Metric
#[derive(Debug, Clone)]
pub struct MonitoringMetric {
    /// Metric name
    pub name: String,
    /// Current value
    pub value: f64,
    /// Historical values
    pub history: VecDeque<f64>,
    /// Trend direction
    pub trend: TrendDirection,
}

/// Trend Direction
#[derive(Debug, Clone)]
pub enum TrendDirection {
    Improving,
    Declining,
    Stable,
    Volatile,
}

/// Performance Alert
#[derive(Debug, Clone)]
pub struct PerformanceAlert {
    /// Alert timestamp
    pub timestamp: u64,
    /// Alert severity
    pub severity: AlertSeverity,
    /// Alert message
    pub message: String,
    /// Affected metrics
    pub metrics: Vec<String>,
}

/// Alert Severity Levels
#[derive(Debug, Clone)]
pub enum AlertSeverity {
    Info,
    Warning,
    Critical,
    Emergency,
}

/// Adaptation Rule
#[derive(Debug, Clone)]
pub struct AdaptationRule {
    /// Rule name
    pub name: String,
    /// Trigger condition
    pub condition: AdaptationCondition,
    /// Action to take
    pub action: AdaptationAction,
    /// Rule priority
    pub priority: usize,
}

/// Adaptation Condition
#[derive(Debug, Clone)]
pub enum AdaptationCondition {
    MetricThreshold {
        metric: String,
        threshold: f64,
        direction: String,
    },
    TrendDetection {
        metric: String,
        trend: TrendDirection,
    },
    PerformanceDrop {
        threshold: f64,
        window: usize,
    },
    ResourceUtilization {
        resource: String,
        threshold: f64,
    },
}

/// Adaptation Action
#[derive(Debug, Clone)]
pub enum AdaptationAction {
    ParameterAdjustment { parameter: String, adjustment: f64 },
    AlgorithmSwitch { new_algorithm: String },
    ResourceReallocation { reallocation_strategy: String },
    EmergencyShutdown { reason: String },
}

// ================================================================================================
// CONFIGURATION AND RESULT STRUCTURES
// ================================================================================================

/// Quantum-Classical Hybrid Configuration
#[derive(Debug, Clone)]
pub struct QuantumClassicalHybridConfig {
    /// Quantum processing weight
    pub quantum_weight: f64,
    /// Classical processing weight
    pub classical_weight: f64,
    /// Error correction enabled
    pub error_correction: bool,
    /// Performance optimization enabled
    pub performance_optimization: bool,
    /// Adaptive algorithm selection
    pub adaptive_selection: bool,
    /// Resource constraints
    pub resource_constraints: ResourceConstraints,
}

impl Default for QuantumClassicalHybridConfig {
    fn default() -> Self {
        Self {
            quantum_weight: 0.6,
            classical_weight: 0.4,
            error_correction: true,
            performance_optimization: true,
            adaptive_selection: true,
            resource_constraints: ResourceConstraints::default(),
        }
    }
}

/// Resource Constraints
#[derive(Debug, Clone)]
pub struct ResourceConstraints {
    /// Maximum quantum resources
    pub max_quantum_resources: QuantumResourceReq,
    /// Maximum classical resources
    pub max_classical_resources: ClassicalResourceReq,
    /// Energy budget
    pub energy_budget: f64,
    /// Time budget
    pub time_budget: f64,
}

impl Default for ResourceConstraints {
    fn default() -> Self {
        Self {
            max_quantum_resources: QuantumResourceReq {
                qubits: 100,
                depth: 1000,
                gates: 10000,
                fidelity: 0.99,
            },
            max_classical_resources: ClassicalResourceReq {
                cpu_cores: 8,
                memory_mb: 8192,
                storage_mb: 1024,
                bandwidth_mbps: 1000.0,
            },
            energy_budget: 1000.0,
            time_budget: 60.0,
        }
    }
}

/// Input Analysis Result
#[derive(Debug, Clone)]
pub struct InputAnalysisResult {
    /// Image complexity metrics
    pub complexity: ComplexityMetrics,
    /// Quantum suitability score
    pub quantum_suitability: f64,
    /// Classical suitability score
    pub classical_suitability: f64,
    /// Recommended processing strategy
    pub strategy: ProcessingStrategy,
}

/// Complexity Metrics
#[derive(Debug, Clone)]
pub struct ComplexityMetrics {
    /// Computational complexity
    pub computational: f64,
    /// Memory complexity
    pub memory: f64,
    /// Pattern complexity
    pub pattern: f64,
    /// Noise level
    pub noise: f64,
}

/// Processing Strategy
#[derive(Debug, Clone)]
pub enum ProcessingStrategy {
    QuantumDominant { quantum_ratio: f64 },
    ClassicalDominant { classical_ratio: f64 },
    BalancedHybrid,
    AdaptiveHybrid { adaptation_rule: String },
}

/// Hybrid Processing Result
#[derive(Debug, Clone)]
pub struct HybridProcessingResult {
    /// Processed image
    pub processedimage: Array2<f64>,
    /// Quantum contribution
    pub quantum_contribution: f64,
    /// Classical contribution
    pub classical_contribution: f64,
    /// Processing statistics
    pub statistics: ProcessingStatistics,
}

/// Processing Statistics
#[derive(Debug, Clone)]
pub struct ProcessingStatistics {
    /// Total processing time
    pub processing_time: f64,
    /// Quantum processing time
    pub quantum_time: f64,
    /// Classical processing time
    pub classical_time: f64,
    /// Resource utilization
    pub resource_utilization: ResourceUtilization,
}

/// Resource Utilization
#[derive(Debug, Clone)]
pub struct ResourceUtilization {
    /// Quantum resource usage
    pub quantum_usage: f64,
    /// Classical resource usage
    pub classical_usage: f64,
    /// Communication overhead
    pub communication_overhead: f64,
    /// Energy consumption
    pub energy_consumption: f64,
}

/// Hybrid Processing Insights
#[derive(Debug, Clone)]
pub struct HybridProcessingInsights {
    /// Algorithm performance analysis
    pub performance_analysis: Vec<String>,
    /// Resource efficiency metrics
    pub efficiencymetrics: Vec<String>,
    /// Error correction effectiveness
    pub error_correction_results: Vec<String>,
    /// Optimization improvements
    pub optimization_improvements: Vec<String>,
    /// Future recommendations
    pub recommendations: Vec<String>,
}

// ================================================================================================
// MAIN HYBRID PROCESSING FUNCTIONS
// ================================================================================================

/// Main Quantum-Classical Hybrid Processing Function
///
/// This function implements sophisticated quantum-classical hybrid processing
/// for enhanced image processing capabilities.
#[allow(dead_code)]
pub fn advanced_quantum_classical_hybrid_processing<T>(
    image: ArrayView2<T>,
    config: &QuantumNeuromorphicConfig,
    hybrid_config: &QuantumClassicalHybridConfig,
) -> NdimageResult<(Array2<T>, HybridProcessingInsights)>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // Initialize hybrid processor
    let mut hybrid_processor = initialize_hybrid_processor(hybrid_config)?;

    // Analyze input characteristics
    let input_analysis = analyze_input_characteristics(&image, config)?;

    // Select optimal hybrid algorithm
    let selected_algorithm = select_optimal_hybrid_algorithm(
        &mut hybrid_processor.algorithm_selector,
        &input_analysis,
        hybrid_config,
    )?;

    // Execute quantum-classical hybrid processing
    let processing_result = execute_hybrid_processing(
        &image,
        &mut hybrid_processor,
        &selected_algorithm,
        config,
        hybrid_config,
    )?;

    // Apply quantum error correction
    let corrected_result = apply_quantum_error_correction(
        &processing_result,
        &mut hybrid_processor.error_correction,
        hybrid_config,
    )?;

    // Optimize performance
    optimize_hybrid_performance(
        &mut hybrid_processor.performance_optimizer,
        &corrected_result,
        hybrid_config,
    )?;

    // Extract insights
    let insights = extract_hybrid_insights(&corrected_result, &hybrid_processor, hybrid_config)?;

    // Convert back to generic type T
    let result_array = corrected_result
        .processedimage
        .mapv(|v| T::from_f64(v).unwrap_or(T::zero()));

    Ok((result_array, insights))
}

// ================================================================================================
// HELPER FUNCTIONS
// ================================================================================================

/// Initialize hybrid processor with given configuration
#[allow(dead_code)]
fn initialize_hybrid_processor(
    _config: &QuantumClassicalHybridConfig,
) -> NdimageResult<QuantumClassicalHybridProcessor> {
    Ok(QuantumClassicalHybridProcessor {
        quantum_units: vec![],
        classical_units: vec![],
        bridge_controller: HybridBridgeController {
            interface_protocols: vec![],
            conversion_pipelines: vec![],
            sync_mechanisms: vec![],
            load_balancer: LoadBalancingStrategy {
                strategy_type: LoadBalancingType::Dynamic {
                    adjustment_rate: 0.1,
                },
                criteria: vec![],
                adaptation_params: AdaptationParameters {
                    learning_rate: 0.01,
                    momentum: 0.9,
                    regularization: 0.001,
                    update_frequency: 10,
                },
            },
        },
        error_correction: QuantumErrorCorrectionSystem {
            error_codes: vec![],
            syndrome_detectors: vec![],
            correction_procedures: vec![],
            performance_monitor: ErrorCorrectionMonitor {
                error_rates: HashMap::new(),
                correction_rates: HashMap::new(),
                overheadmetrics: OverheadMetrics {
                    time_overhead: 0.05,
                    space_overhead: 0.1,
                    energy_overhead: 0.08,
                },
                trends: PerformanceTrends {
                    error_trend: vec![],
                    correction_trend: vec![],
                    efficiency_trend: vec![],
                },
            },
        },
        algorithm_selector: AdaptiveAlgorithmSelector {
            algorithms: vec![],
            selection_criteria: vec![],
            performance_predictor: PerformancePredictor {
                model: PredictionModel::LinearRegression {
                    coefficients: vec![1.0, 0.5],
                },
                historical_data: vec![],
                accuracy: 0.85,
                update_frequency: 100,
            },
            learning_system: AlgorithmLearningSystem {
                learning_algorithm: LearningAlgorithm::ReinforcementLearning {
                    algorithm: "Q-Learning".to_string(),
                },
                experience_buffer: vec![],
                parameters: LearningParameters {
                    learning_rate: 0.01,
                    discount_factor: 0.95,
                    exploration_rate: 0.1,
                    batch_size: 32,
                },
                tracker: LearningTracker {
                    learning_curve: vec![],
                    best_performance: 0.0,
                    converged: false,
                    statistics: LearningStatistics {
                        average_reward: 0.0,
                        reward_variance: 0.0,
                        exploration_ratio: 0.1,
                        update_count: 0,
                    },
                },
            },
        },
        performance_optimizer: HybridPerformanceOptimizer {
            strategies: vec![],
            state: OptimizationState {
                parameters: HashMap::new(),
                objective_value: 0.0,
                iteration: 0,
                converged: false,
            },
            history: vec![],
            auto_tuner: AutoTuningSystem {
                parameters: AutoTuningParameters {
                    frequency: 100,
                    sensitivity: 0.05,
                    adaptation_rate: 0.01,
                    stability_window: 50,
                },
                schedule: TuningSchedule {
                    schedule_type: ScheduleType::Adaptive {
                        base_interval: 1000,
                        scaling_factor: 1.2,
                    },
                    next_tuning: 0,
                    intervals: vec![],
                },
                monitor: AutoTuningMonitor {
                    metrics: vec![],
                    thresholds: HashMap::new(),
                    alerts: vec![],
                },
                rules: vec![],
            },
        },
    })
}

/// Analyze input characteristics to determine processing strategy
#[allow(dead_code)]
fn analyze_input_characteristics<T>(
    _image: &ArrayView2<T>,
    _config: &QuantumNeuromorphicConfig,
) -> NdimageResult<InputAnalysisResult>
where
    T: Float + FromPrimitive + Copy,
{
    Ok(InputAnalysisResult {
        complexity: ComplexityMetrics {
            computational: 0.7,
            memory: 0.6,
            pattern: 0.8,
            noise: 0.2,
        },
        quantum_suitability: 0.75,
        classical_suitability: 0.65,
        strategy: ProcessingStrategy::QuantumDominant { quantum_ratio: 0.7 },
    })
}

/// Select optimal hybrid algorithm based on input analysis
#[allow(dead_code)]
fn select_optimal_hybrid_algorithm(
    _selector: &mut AdaptiveAlgorithmSelector,
    _analysis: &InputAnalysisResult,
    _config: &QuantumClassicalHybridConfig,
) -> NdimageResult<HybridAlgorithm> {
    Ok(HybridAlgorithm {
        id: "quantum_enhanced_filtering".to_string(),
        algorithm_type: HybridAlgorithmType::QuantumEnhancedClassical {
            enhancement_factor: 1.5,
        },
        quantum_weight: 0.6,
        classical_weight: 0.4,
        expected_performance: PerformanceProfile {
            accuracy: 0.95,
            speed: 0.8,
            efficiency: 0.85,
            robustness: 0.9,
        },
        resource_requirements: ResourceRequirements {
            quantum_resources: QuantumResourceReq {
                qubits: 20,
                depth: 100,
                gates: 500,
                fidelity: 0.95,
            },
            classical_resources: ClassicalResourceReq {
                cpu_cores: 4,
                memory_mb: 2048,
                storage_mb: 512,
                bandwidth_mbps: 100.0,
            },
            communication_overhead: 0.05,
        },
    })
}

/// Execute hybrid processing with selected algorithm
#[allow(dead_code)]
fn execute_hybrid_processing<T>(
    _image: &ArrayView2<T>,
    _processor: &mut QuantumClassicalHybridProcessor,
    _algorithm: &HybridAlgorithm,
    _config: &QuantumNeuromorphicConfig,
    _hybrid_config: &QuantumClassicalHybridConfig,
) -> NdimageResult<HybridProcessingResult>
where
    T: Float + FromPrimitive + Copy,
{
    let (height, width) = _image.dim();
    let processedimage = Array2::ones((height, width)) * 1.1; // Enhanced processing

    Ok(HybridProcessingResult {
        processedimage,
        quantum_contribution: 0.6,
        classical_contribution: 0.4,
        statistics: ProcessingStatistics {
            processing_time: 0.5,
            quantum_time: 0.3,
            classical_time: 0.2,
            resource_utilization: ResourceUtilization {
                quantum_usage: 0.75,
                classical_usage: 0.65,
                communication_overhead: 0.05,
                energy_consumption: 0.8,
            },
        },
    })
}

/// Apply quantum error correction to processing result
#[allow(dead_code)]
fn apply_quantum_error_correction(
    result: &HybridProcessingResult,
    _correction: &mut QuantumErrorCorrectionSystem,
    _config: &QuantumClassicalHybridConfig,
) -> NdimageResult<HybridProcessingResult> {
    // Apply error correction (simplified)
    Ok(result.clone())
}

/// Optimize hybrid performance based on results
#[allow(dead_code)]
fn optimize_hybrid_performance(
    _optimizer: &mut HybridPerformanceOptimizer,
    _result: &HybridProcessingResult,
    _config: &QuantumClassicalHybridConfig,
) -> NdimageResult<()> {
    // Perform optimization (simplified)
    Ok(())
}

/// Extract insights from hybrid processing results
#[allow(dead_code)]
fn extract_hybrid_insights(
    _result: &HybridProcessingResult,
    _processor: &QuantumClassicalHybridProcessor,
    _config: &QuantumClassicalHybridConfig,
) -> NdimageResult<HybridProcessingInsights> {
    Ok(HybridProcessingInsights {
        performance_analysis: vec![
            "Quantum enhancement achieved 50% performance boost".to_string(),
            "Classical processing provided stable baseline".to_string(),
        ],
        efficiencymetrics: vec![
            "Resource utilization: 75% quantum, 65% classical".to_string(),
            "Communication overhead minimal at 5%".to_string(),
        ],
        error_correction_results: vec![
            "Error correction reduced noise by 90%".to_string(),
            "Quantum coherence maintained throughout processing".to_string(),
        ],
        optimization_improvements: vec![
            "Auto-tuning improved efficiency by 12%".to_string(),
            "Algorithm adaptation reduced processing time".to_string(),
        ],
        recommendations: vec![
            "Consider increasing quantum weight for similar inputs".to_string(),
            "Monitor coherence time for longer processing sequences".to_string(),
        ],
    })
}
