scirs2-ndimage 0.4.2

//! Configuration and data structures for advanced fusion algorithms
//!
//! This module contains all the configuration types, data structures, and enums
//! used throughout the advanced fusion processing system.

use scirs2_core::ndarray::{Array1, Array2, Array3, Array4, Array5};
use scirs2_core::numeric::Complex;
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::sync::{Arc, RwLock};

use crate::neuromorphic_computing::NeuromorphicConfig;
use crate::quantum_inspired::QuantumConfig;
use crate::quantum_neuromorphic_fusion::QuantumNeuromorphicConfig;

/// Advanced Processing Configuration
#[derive(Debug, Clone)]
pub struct AdvancedConfig {
    /// Quantum computing parameters
    pub quantum: QuantumConfig,
    /// Neuromorphic computing parameters
    pub neuromorphic: NeuromorphicConfig,
    /// Quantum-neuromorphic fusion parameters
    pub quantum_neuromorphic: QuantumNeuromorphicConfig,
    /// Consciousness simulation depth
    pub consciousness_depth: usize,
    /// Meta-learning adaptation rate
    pub meta_learning_rate: f64,
    /// Advanced-dimensional processing dimensions
    pub advanced_dimensions: usize,
    /// Temporal processing window
    pub temporal_window: usize,
    /// Self-organization enabled
    pub self_organization: bool,
    /// Quantum consciousness simulation
    pub quantum_consciousness: bool,
    /// Advanced-efficiency optimization
    pub advanced_efficiency: bool,
    /// Causal inference depth
    pub causal_depth: usize,
    /// Multi-scale processing levels
    pub multi_scale_levels: usize,
    /// Adaptive resource allocation
    pub adaptive_resources: bool,
    /// Adaptive learning capability
    pub adaptive_learning: bool,
    /// Quantum coherence threshold (0.0 to 1.0)
    pub quantum_coherence_threshold: f64,
    /// Neuromorphic plasticity factor (0.0 to 1.0)
    pub neuromorphic_plasticity: f64,
    /// Advanced processing intensity (0.0 to 1.0)
    pub advanced_processing_intensity: f64,
}

impl Default for AdvancedConfig {
    fn default() -> Self {
        Self {
            quantum: QuantumConfig::default(),
            neuromorphic: NeuromorphicConfig::default(),
            quantum_neuromorphic: QuantumNeuromorphicConfig::default(),
            consciousness_depth: 8,
            meta_learning_rate: 0.01,
            advanced_dimensions: 12,
            temporal_window: 64,
            self_organization: true,
            quantum_consciousness: true,
            advanced_efficiency: true,
            causal_depth: 16,
            multi_scale_levels: 10,
            adaptive_resources: true,
            adaptive_learning: true,
            quantum_coherence_threshold: 0.85,
            neuromorphic_plasticity: 0.1,
            advanced_processing_intensity: 0.75,
        }
    }
}

/// Advanced Processing State
#[derive(Debug, Clone)]
pub struct AdvancedState {
    /// Quantum consciousness amplitudes
    pub consciousness_amplitudes: Array4<Complex<f64>>,
    /// Meta-learning parameters
    pub meta_parameters: Array2<f64>,
    /// Self-organizing network topology
    pub network_topology: Arc<RwLock<NetworkTopology>>,
    /// Temporal memory bank
    pub temporal_memory: VecDeque<Array3<f64>>,
    /// Causal relationship graph
    pub causal_graph: BTreeMap<usize, Vec<CausalRelation>>,
    /// Advanced-dimensional feature space
    pub advancedfeatures: Array5<f64>,
    /// Resource allocation state
    pub resource_allocation: ResourceState,
    /// Processing efficiency metrics
    pub efficiencymetrics: EfficiencyMetrics,
    /// Number of processing cycles
    pub processing_cycles: u64,
}

/// Self-Organizing Network Topology
#[derive(Debug, Clone)]
pub struct NetworkTopology {
    /// Node connections
    pub connections: HashMap<usize, Vec<Connection>>,
    /// Node properties
    pub nodes: Vec<NetworkNode>,
    /// Global network properties
    pub global_properties: NetworkProperties,
}

/// Network Node
#[derive(Debug, Clone)]
pub struct NetworkNode {
    /// Node ID
    pub id: usize,
    /// Quantum state
    pub quantumstate: Array1<Complex<f64>>,
    /// Classical state
    pub classicalstate: Array1<f64>,
    /// Learning parameters
    pub learning_params: Array1<f64>,
    /// Activation function type
    pub activation_type: ActivationType,
    /// Self-organization strength
    pub self_org_strength: f64,
}

/// Network Connection
#[derive(Debug, Clone)]
pub struct Connection {
    /// Target node ID
    pub target: usize,
    /// Connection weight (complex for quantum effects)
    pub weight: Complex<f64>,
    /// Connection type
    pub connection_type: ConnectionType,
    /// Plasticity parameters
    pub plasticity: PlasticityParameters,
}

/// Connection Types
#[derive(Debug, Clone)]
pub enum ConnectionType {
    Excitatory,
    Inhibitory,
    Quantum,
    QuantumEntangled,
    Modulatory,
    SelfOrganizing,
    Causal,
    Temporal,
}

/// Activation Function Types
#[derive(Debug, Clone)]
pub enum ActivationType {
    Sigmoid,
    Tanh,
    ReLU,
    Swish,
    QuantumSigmoid,
    BiologicalSpike,
    ConsciousnessGate,
    AdvancedActivation,
}

/// Plasticity Parameters
#[derive(Debug, Clone)]
pub struct PlasticityParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Decay rate
    pub decay_rate: f64,
    /// Quantum coherence factor
    pub quantum_coherence: f64,
    /// Biological time constant
    pub bio_time_constant: f64,
}

/// Network Global Properties
#[derive(Debug, Clone)]
pub struct NetworkProperties {
    /// Global coherence measure
    pub coherence: f64,
    /// Self-organization index
    pub self_organization_index: f64,
    /// Consciousness emergence measure
    pub consciousness_emergence: f64,
    /// Processing efficiency
    pub efficiency: f64,
}

/// Causal Relation
#[derive(Debug, Clone)]
pub struct CausalRelation {
    /// Source event
    pub source: usize,
    /// Target event
    pub target: usize,
    /// Causal strength
    pub strength: f64,
    /// Temporal delay
    pub delay: usize,
    /// Confidence level
    pub confidence: f64,
}

/// Resource Allocation State
#[derive(Debug, Clone)]
pub struct ResourceState {
    /// CPU allocation
    pub cpu_allocation: Vec<f64>,
    /// Memory allocation
    pub memory_allocation: f64,
    /// GPU allocation (if available)
    pub gpu_allocation: Option<f64>,
    /// Quantum processing allocation (if available)
    pub quantum_allocation: Option<f64>,
    /// Adaptive allocation history
    pub allocationhistory: VecDeque<AllocationSnapshot>,
}

/// Allocation Snapshot
#[derive(Debug, Clone)]
pub struct AllocationSnapshot {
    /// Timestamp
    pub timestamp: usize,
    /// Resource utilization
    pub utilization: HashMap<String, f64>,
    /// Performance metrics
    pub performance: f64,
    /// Efficiency score
    pub efficiency: f64,
}

/// Efficiency Metrics
#[derive(Debug, Clone)]
pub struct EfficiencyMetrics {
    /// Processing speed (operations per second)
    pub ops_per_second: f64,
    /// Memory efficiency (utilization ratio)
    pub memory_efficiency: f64,
    /// Energy efficiency (operations per watt)
    pub energy_efficiency: f64,
    /// Quality efficiency (quality per resource)
    pub quality_efficiency: f64,
    /// Temporal efficiency (real-time processing ratio)
    pub temporal_efficiency: f64,
}

/// Quantum Consciousness Evolution System
#[derive(Debug, Clone)]
pub struct QuantumConsciousnessEvolution {
    /// Consciousness evolution history
    pub evolutionhistory: VecDeque<ConsciousnessState>,
    /// Evolution rate parameters
    pub evolution_rate: f64,
    /// Consciousness complexity metrics
    pub complexitymetrics: ConsciousnessComplexity,
    /// Quantum coherence optimization engine
    pub coherence_optimizer: QuantumCoherenceOptimizer,
    /// Evolutionary selection pressure
    pub selection_pressure: f64,
    /// Consciousness emergence threshold
    pub emergence_threshold: f64,
}

/// Consciousness State
#[derive(Debug, Clone)]
pub struct ConsciousnessState {
    /// Consciousness level (0.0 to 1.0)
    pub level: f64,
    /// Quantum coherence quality
    pub coherence_quality: f64,
    /// Information integration measure
    pub phi_measure: f64,
    /// Attention focus strength
    pub attention_strength: f64,
    /// Self-awareness index
    pub self_awareness: f64,
    /// Timestamp of state
    pub timestamp: usize,
}

/// Consciousness Complexity
#[derive(Debug, Clone)]
pub struct ConsciousnessComplexity {
    /// Integrated information
    pub integrated_information: f64,
    /// Causal structure complexity
    pub causal_complexity: f64,
    /// Temporal coherence measure
    pub temporal_coherence: f64,
    /// Hierarchical organization index
    pub hierarchical_index: f64,
    /// Emergent property strength
    pub emergence_strength: f64,
}

/// Quantum Coherence Optimizer
#[derive(Debug, Clone)]
pub struct QuantumCoherenceOptimizer {
    /// Coherence maintenance strategies
    pub strategies: Vec<CoherenceStrategy>,
    /// Optimization parameters
    pub optimization_params: HashMap<String, f64>,
    /// Performance history
    pub performancehistory: VecDeque<f64>,
}

/// Coherence Strategy
#[derive(Debug, Clone)]
pub enum CoherenceStrategy {
    /// Error correction based coherence preservation
    ErrorCorrection {
        threshold: f64,
        correction_rate: f64,
    },
    /// Decoherence suppression
    DecoherenceSuppression { suppression_strength: f64 },
    /// Entanglement purification
    EntanglementPurification { purification_cycles: usize },
    /// Dynamical decoupling
    DynamicalDecoupling { pulse_frequency: f64 },
    /// Quantum Zeno effect
    QuantumZeno { measurement_frequency: f64 },
}

/// Enhanced Meta-Learning System
#[derive(Debug, Clone)]
pub struct EnhancedMetaLearningSystem {
    /// Temporal memory fusion engine
    pub temporal_memory_fusion: TemporalMemoryFusion,
    /// Hierarchical learning structure
    pub hierarchical_learner: HierarchicalLearner,
    /// Strategy evolution engine
    pub strategy_evolution: StrategyEvolution,
    /// Meta-learning performance tracker
    pub performance_tracker: MetaLearningTracker,
    /// Adaptive memory consolidation
    pub memory_consolidation: AdaptiveMemoryConsolidation,
}

/// Temporal Memory Fusion
#[derive(Debug, Clone)]
pub struct TemporalMemoryFusion {
    /// Short-term memory bank
    pub short_term_memory: VecDeque<MemoryTrace>,
    /// Long-term memory bank
    pub long_term_memory: HashMap<String, ConsolidatedMemory>,
    /// Memory fusion weights
    pub fusion_weights: Array1<f64>,
    /// Temporal decay factors
    pub decay_factors: Array1<f64>,
    /// Memory attention mechanism
    pub attention_mechanism: MemoryAttention,
}

/// Memory Trace
#[derive(Debug, Clone)]
pub struct MemoryTrace {
    /// Memory content
    pub content: Array2<f64>,
    /// Context information
    pub context: MemoryContext,
    /// Importance score
    pub importance: f64,
    /// Timestamp
    pub timestamp: usize,
    /// Access frequency
    pub access_count: usize,
}

/// Memory Context
#[derive(Debug, Clone)]
pub struct MemoryContext {
    /// Processing operation type
    pub operation_type: String,
    /// Data characteristics
    pub data_characteristics: Vec<f64>,
    /// Performance outcome
    pub performance_outcome: f64,
    /// Environmental conditions
    pub environment: HashMap<String, f64>,
}

/// Consolidated Memory
#[derive(Debug, Clone)]
pub struct ConsolidatedMemory {
    /// Consolidated representation
    pub representation: Array2<f64>,
    /// Memory strength
    pub strength: f64,
    /// Generalization scope
    pub generalization_scope: f64,
    /// Usage statistics
    pub usage_stats: MemoryUsageStats,
}

/// Memory Usage Statistics
#[derive(Debug, Clone)]
pub struct MemoryUsageStats {
    /// Total access count
    pub total_accesses: usize,
    /// Success rate
    pub success_rate: f64,
    /// Average performance improvement
    pub avg_improvement: f64,
    /// Last access timestamp
    pub last_access: usize,
}

/// Memory Attention
#[derive(Debug, Clone)]
pub struct MemoryAttention {
    /// Attention weights for different memory types
    pub attention_weights: HashMap<String, f64>,
    /// Focus threshold
    pub focus_threshold: f64,
    /// Attention adaptation rate
    pub adaptation_rate: f64,
}

/// Hierarchical Learner
#[derive(Debug, Clone)]
pub struct HierarchicalLearner {
    /// Learning hierarchy levels
    pub hierarchy_levels: Vec<LearningLevel>,
    /// Inter-level connections
    pub level_connections: Array2<f64>,
    /// Hierarchical attention
    pub hierarchical_attention: Array1<f64>,
}

/// Learning Level
#[derive(Debug, Clone)]
pub struct LearningLevel {
    /// Level identifier
    pub level_id: usize,
    /// Abstraction degree
    pub abstraction_degree: f64,
    /// Learning strategies at this level
    pub strategies: Vec<LearningStrategy>,
    /// Performance metrics
    pub performancemetrics: LevelPerformanceMetrics,
}

/// Learning Strategy
#[derive(Debug, Clone)]
pub struct LearningStrategy {
    /// Strategy name
    pub name: String,
    /// Strategy parameters
    pub parameters: HashMap<String, f64>,
    /// Success rate
    pub success_rate: f64,
    /// Adaptation history
    pub adaptationhistory: VecDeque<StrategyAdaptation>,
}

/// Strategy Adaptation
#[derive(Debug, Clone)]
pub struct StrategyAdaptation {
    /// Parameter changes
    pub parameter_changes: HashMap<String, f64>,
    /// Performance impact
    pub performance_impact: f64,
    /// Context conditions
    pub context: HashMap<String, f64>,
    /// Timestamp
    pub timestamp: usize,
}

/// Level Performance Metrics
#[derive(Debug, Clone)]
pub struct LevelPerformanceMetrics {
    /// Learning rate
    pub learning_rate: f64,
    /// Generalization ability
    pub generalization_ability: f64,
    /// Adaptation speed
    pub adaptation_speed: f64,
    /// Stability measure
    pub stability: f64,
}

/// Strategy Evolution
#[derive(Debug, Clone)]
pub struct StrategyEvolution {
    /// Strategy population
    pub strategy_population: Vec<EvolutionaryStrategy>,
    /// Selection mechanisms
    pub selection_mechanisms: Vec<SelectionMechanism>,
    /// Mutation parameters
    pub mutation_params: MutationParameters,
    /// Evolution history
    pub evolutionhistory: VecDeque<EvolutionGeneration>,
}

/// Evolutionary Strategy
#[derive(Debug, Clone)]
pub struct EvolutionaryStrategy {
    /// Strategy genome
    pub genome: Array1<f64>,
    /// Fitness score
    pub fitness: f64,
    /// Age (generations survived)
    pub age: usize,
    /// Parent lineage
    pub lineage: Vec<usize>,
}

/// Selection Mechanism
#[derive(Debug, Clone)]
pub enum SelectionMechanism {
    /// Tournament selection
    Tournament { tournament_size: usize },
    /// Roulette wheel selection
    RouletteWheel,
    /// Rank-based selection
    RankBased { selection_pressure: f64 },
    /// Elite selection
    Elite { elite_fraction: f64 },
}

/// Mutation Parameters
#[derive(Debug, Clone)]
pub struct MutationParameters {
    /// Mutation rate
    pub mutation_rate: f64,
    /// Mutation strength
    pub mutation_strength: f64,
    /// Adaptive mutation enabled
    pub adaptive_mutation: bool,
    /// Mutation distribution
    pub mutation_distribution: MutationDistribution,
}

/// Mutation Distribution
#[derive(Debug, Clone)]
pub enum MutationDistribution {
    /// Gaussian mutation
    Gaussian { sigma: f64 },
    /// Uniform mutation
    Uniform { range: f64 },
    /// Cauchy mutation
    Cauchy { scale: f64 },
    /// Adaptive distribution
    Adaptive,
}

/// Evolution Generation
#[derive(Debug, Clone)]
pub struct EvolutionGeneration {
    /// Generation number
    pub generation: usize,
    /// Best fitness achieved
    pub best_fitness: f64,
    /// Average fitness
    pub average_fitness: f64,
    /// Diversity measure
    pub diversity: f64,
    /// Notable mutations
    pub mutations: Vec<String>,
}

/// Meta-Learning Tracker
#[derive(Debug, Clone)]
pub struct MetaLearningTracker {
    /// Learning performance history
    pub performancehistory: VecDeque<MetaLearningPerformance>,
    /// Strategy effectiveness tracking
    pub strategy_effectiveness: HashMap<String, StrategyEffectiveness>,
    /// Learning curve analysis
    pub learning_curves: HashMap<String, LearningCurve>,
}

/// Meta-Learning Performance
#[derive(Debug, Clone)]
pub struct MetaLearningPerformance {
    /// Task identifier
    pub task_id: String,
    /// Performance score
    pub performance_score: f64,
    /// Learning time
    pub learning_time: f64,
    /// Generalization score
    pub generalization_score: f64,
    /// Resource usage
    pub resource_usage: f64,
}

/// Strategy Effectiveness
#[derive(Debug, Clone)]
pub struct StrategyEffectiveness {
    /// Average performance
    pub avg_performance: f64,
    /// Consistency measure
    pub consistency: f64,
    /// Robustness score
    pub robustness: f64,
    /// Efficiency rating
    pub efficiency: f64,
}

/// Learning Curve
#[derive(Debug, Clone)]
pub struct LearningCurve {
    /// Performance over time
    pub performance_timeline: Vec<f64>,
    /// Learning rate trajectory
    pub learning_rate_timeline: Vec<f64>,
    /// Convergence point
    pub convergence_point: Option<usize>,
}

/// Adaptive Memory Consolidation
#[derive(Debug, Clone)]
pub struct AdaptiveMemoryConsolidation {
    /// Consolidation strategies
    pub consolidation_strategies: Vec<ConsolidationStrategy>,
    /// Memory retention policies
    pub retention_policies: HashMap<String, RetentionPolicy>,
    /// Consolidation effectiveness metrics
    pub effectiveness_metrics: ConsolidationMetrics,
}

/// Consolidation Strategy
#[derive(Debug, Clone)]
pub enum ConsolidationStrategy {
    /// Replay-based consolidation
    ReplayBased { replay_frequency: f64 },
    /// Interference-based consolidation
    InterferenceBased { interference_threshold: f64 },
    /// Importance-weighted consolidation
    ImportanceWeighted { importance_threshold: f64 },
    /// Temporal-decay consolidation
    TemporalDecay { decay_rate: f64 },
}

/// Retention Policy
#[derive(Debug, Clone)]
pub struct RetentionPolicy {
    /// Maximum retention time
    pub max_retention_time: usize,
    /// Importance-based retention
    pub importance_based: bool,
    /// Access-frequency based retention
    pub frequency_based: bool,
    /// Recency factor
    pub recency_factor: f64,
}

/// Consolidation Metrics
#[derive(Debug, Clone)]
pub struct ConsolidationMetrics {
    /// Memory utilization efficiency
    pub memory_utilization: f64,
    /// Consolidation success rate
    pub consolidation_success_rate: f64,
    /// Average consolidation time
    pub avg_consolidation_time: f64,
    /// Memory interference reduction
    pub interference_reduction: f64,
}

/// Quantum-Aware Resource Scheduler
#[derive(Debug, Clone)]
pub struct QuantumAwareResourceScheduler {
    /// Quantum resource pool
    pub quantum_resource_pool: QuantumResourcePool,
    /// Quantum scheduling algorithms
    pub scheduling_algorithms: Vec<QuantumSchedulingAlgorithm>,
    /// Quantum load balancer
    pub quantum_load_balancer: QuantumLoadBalancer,
    /// Resource entanglement graph
    pub entanglement_graph: ResourceEntanglementGraph,
    /// Quantum optimization engine
    pub optimization_engine: QuantumOptimizationEngine,
    /// Performance monitoring
    pub performance_monitor: QuantumPerformanceMonitor,
}

/// Quantum Resource Pool
#[derive(Debug, Clone)]
pub struct QuantumResourcePool {
    /// Available quantum processing units
    pub quantum_units: Vec<QuantumProcessingUnit>,
    /// Classical processing units
    pub classical_units: Vec<ClassicalProcessingUnit>,
    /// Hybrid quantum-classical units
    pub hybrid_units: Vec<HybridProcessingUnit>,
    /// Resource allocation matrix
    pub allocation_matrix: Array2<Complex<f64>>,
    /// Quantum coherence time tracking
    pub coherence_times: HashMap<String, f64>,
}

/// Quantum Processing Unit
#[derive(Debug, Clone)]
pub struct QuantumProcessingUnit {
    /// Unit identifier
    pub id: String,
    /// Number of qubits
    pub qubit_count: usize,
    /// Coherence time
    pub coherence_time: f64,
    /// Gate fidelity
    pub gate_fidelity: f64,
    /// Current quantum state
    pub quantumstate: Array1<Complex<f64>>,
    /// Available operations
    pub available_operations: Vec<QuantumOperation>,
    /// Utilization level
    pub utilization: f64,
}

/// Classical Processing Unit
#[derive(Debug, Clone)]
pub struct ClassicalProcessingUnit {
    /// Unit identifier
    pub id: String,
    /// Processing power (FLOPS)
    pub processing_power: f64,
    /// Memory capacity
    pub memory_capacity: usize,
    /// Current load
    pub current_load: f64,
    /// Available algorithms
    pub available_algorithms: Vec<String>,
}

/// Hybrid Processing Unit
#[derive(Debug, Clone)]
pub struct HybridProcessingUnit {
    /// Unit identifier
    pub id: String,
    /// Quantum component
    pub quantum_component: QuantumProcessingUnit,
    /// Classical component
    pub classical_component: ClassicalProcessingUnit,
    /// Coupling strength
    pub coupling_strength: f64,
}

/// Quantum Operation
#[derive(Debug, Clone)]
pub enum QuantumOperation {
    /// Single qubit gate
    SingleQubit { gate_type: String, fidelity: f64 },
    /// Two qubit gate
    TwoQubit { gate_type: String, fidelity: f64 },
    /// Measurement
    Measurement { measurement_type: String },
    /// Custom operation
    Custom {
        operation: String,
        parameters: HashMap<String, f64>,
    },
}

/// Workload Characteristics
#[derive(Debug, Clone)]
pub struct WorkloadCharacteristics {
    /// Task types and their quantum requirements
    pub task_types: HashMap<String, QuantumTaskRequirements>,
    /// Workload intensity over time
    pub intensity_pattern: Vec<f64>,
    /// Data dependencies
    pub dependencies: Vec<(String, String)>,
    /// Performance requirements
    pub performance_requirements: PerformanceRequirements,
}

/// Quantum Task Requirements
#[derive(Debug, Clone)]
pub struct QuantumTaskRequirements {
    /// Required qubits
    pub qubit_requirement: usize,
    /// Coherence time requirement
    pub coherence_requirement: f64,
    /// Gate operations needed
    pub gate_operations: Vec<String>,
    /// Classical computation ratio
    pub classical_ratio: f64,
}

/// Performance Requirements
#[derive(Debug, Clone)]
pub struct PerformanceRequirements {
    /// Maximum acceptable latency
    pub max_latency: f64,
    /// Minimum throughput requirement
    pub min_throughput: f64,
    /// Accuracy requirements
    pub accuracy_requirement: f64,
    /// Energy constraints
    pub energy_budget: f64,
}

/// Resource Scheduling Decision
#[derive(Debug, Clone)]
pub struct ResourceSchedulingDecision {
    /// Optimal resource allocation
    pub resource_allocation: QuantumResourceAllocation,
    /// Load balancing decisions
    pub load_balancing: QuantumLoadBalancingDecision,
    /// Task scheduling plan
    pub task_schedule: QuantumTaskSchedule,
    /// Expected performance metrics
    pub performancemetrics: QuantumPerformanceMetrics,
    /// Quantum coherence preservation level
    pub quantum_coherence_preservation: f64,
    /// Estimated performance improvement
    pub estimated_performance_improvement: f64,
}

// Note: QuantumSchedulingAlgorithm is defined as an enum later in this file

/// Quantum Load Balancer
#[derive(Debug, Clone)]
pub struct QuantumLoadBalancer {
    pub strategies: Vec<QuantumLoadBalancingStrategy>,
    pub load_distribution: Array1<f64>,
    pub entanglement_connections: HashMap<String, Vec<String>>,
    pub load_predictor: QuantumLoadPredictor,
    // Legacy fields for compatibility
    pub strategy: String,
    pub load_metrics: HashMap<String, f64>,
}

/// Resource Entanglement Graph
#[derive(Debug, Clone)]
pub struct ResourceEntanglementGraph {
    pub adjacency_matrix: Array2<f64>,
    pub nodes: HashMap<String, usize>,
    pub entanglement_strengths: HashMap<(String, String), f64>,
    pub decoherence_tracking: HashMap<(String, String), f64>,
    // Legacy field for compatibility
    pub connections: HashMap<String, Vec<String>>,
}

/// Quantum Optimization Engine
#[derive(Debug, Clone)]
pub struct QuantumOptimizationEngine {
    pub algorithms: Vec<String>,
    pub optimizationstate: QuantumOptimizationState,
    pub optimizationhistory: VecDeque<f64>,
    pub convergence_criteria: ConvergenceCriteria,
    // Legacy field for compatibility
    pub optimization_history: VecDeque<f64>,
}

/// Quantum Performance Monitor
#[derive(Debug, Clone)]
pub struct QuantumPerformanceMonitor {
    pub metrics: QuantumPerformanceMetrics,
    pub monitoring_interval: f64,
}

/// Quantum Resource Allocation
#[derive(Debug, Clone)]
pub struct QuantumResourceAllocation {
    pub quantum_allocations: HashMap<String, f64>,
    pub classical_allocations: HashMap<String, f64>,
}

/// Quantum Load Balancing Decision
#[derive(Debug, Clone)]
pub struct QuantumLoadBalancingDecision {
    pub load_distribution: HashMap<String, f64>,
    pub balancing_strategy: String,
}

/// Quantum Task Schedule
#[derive(Debug, Clone)]
pub struct QuantumTaskSchedule {
    pub scheduled_tasks: Vec<(String, f64)>,
    pub execution_order: Vec<String>,
}

/// Quantum Performance Metrics
#[derive(Debug, Clone)]
pub struct QuantumPerformanceMetrics {
    /// Quantum speedup factor
    pub quantum_speedup: f64,
    /// Quantum advantage ratio
    pub quantum_advantage_ratio: f64,
    /// Coherence efficiency
    pub coherence_efficiency: f64,
    /// Entanglement utilization
    pub entanglement_utilization: f64,
    /// Quantum error rate
    pub quantum_error_rate: f64,
    /// Resource efficiency
    pub resource_efficiency: f64,
    /// Legacy fields for compatibility
    pub throughput: f64,
    pub latency: f64,
    pub error_rate: f64,
    pub resource_utilization: f64,
}

/// Annealing Schedule
#[derive(Debug, Clone)]
pub struct AnnealingSchedule {
    pub initial_temperature: f64,
    pub final_temperature: f64,
    pub steps: usize,
    pub cooling_rate: f64,
}

/// Optimization Target
#[derive(Debug, Clone)]
pub enum OptimizationTarget {
    MinimizeTime,
    MinimizeEnergy,
    MaximizeAccuracy,
}

/// Quantum Load Predictor
#[derive(Debug, Clone)]
pub struct QuantumLoadPredictor {
    pub quantum_nn: QuantumNeuralNetwork,
    pub prediction_horizon: usize,
    pub historical_data: VecDeque<f64>,
    pub accuracy_metrics: PredictionAccuracyMetrics,
}

/// Quantum Neural Network
#[derive(Debug, Clone)]
pub struct QuantumNeuralNetwork {
    pub layers: Vec<Array2<Complex<f64>>>,
    pub classical_layers: Vec<Array2<f64>>,
    pub training_params: QuantumTrainingParameters,
}

/// Quantum Training Parameters
#[derive(Debug, Clone)]
pub struct QuantumTrainingParameters {
    pub learning_rate: f64,
    pub batch_size: usize,
    pub epochs: usize,
    pub optimizer: QuantumOptimizer,
}

/// Quantum Optimizer
#[derive(Debug, Clone)]
pub enum QuantumOptimizer {
    QuantumAdam { beta1: f64, beta2: f64 },
    QuantumSGD { momentum: f64 },
}

/// Prediction Accuracy Metrics
#[derive(Debug, Clone)]
pub struct PredictionAccuracyMetrics {
    pub mae: f64,
    pub rmse: f64,
    pub r_squared: f64,
    pub quantum_fidelity: f64,
}

/// Quantum Optimization State
#[derive(Debug, Clone)]
pub struct QuantumOptimizationState {
    pub parameters: Array1<f64>,
    pub objective_value: f64,
    pub quantumstate: Array1<Complex<f64>>,
    pub gradient: Array1<f64>,
    pub iteration: usize,
}

/// Convergence Criteria
#[derive(Debug, Clone)]
pub struct ConvergenceCriteria {
    pub max_iterations: usize,
    pub objective_tolerance: f64,
    pub parameter_tolerance: f64,
    pub gradient_tolerance: f64,
}

/// Real Time Quantum Monitor
#[derive(Debug, Clone)]
pub struct RealTimeQuantumMonitor {
    pub monitoring_frequency: f64,
    pub currentstates: HashMap<String, f64>,
    pub alerts: Vec<String>,
    pub monitoringhistory: VecDeque<HashMap<String, f64>>,
}

/// Quantum Performance Predictor
#[derive(Debug, Clone)]
pub struct QuantumPerformancePredictor {
    pub prediction_model: QuantumPredictionModel,
    pub prediction_horizon: usize,
    pub prediction_accuracy: f64,
}

/// Quantum Prediction Model
#[derive(Debug, Clone)]
pub struct QuantumPredictionModel {
    pub model_type: String,
    pub parameters: Array2<f64>,
    pub training_data: Vec<f64>,
    pub performancemetrics: PredictionAccuracyMetrics,
}

/// Quantum Anomaly Detector
#[derive(Debug, Clone)]
pub struct QuantumAnomalyDetector {
    pub detection_algorithms: Vec<QuantumAnomalyAlgorithm>,
    pub anomaly_threshold: f64,
    pub baselines: HashMap<String, f64>,
    pub detected_anomalies: VecDeque<String>,
}

/// Quantum Anomaly Algorithm
#[derive(Debug, Clone)]
pub enum QuantumAnomalyAlgorithm {
    QuantumIsolationForest {
        tree_count: usize,
        sample_size: usize,
    },
    QuantumSVM {
        kernel: String,
        gamma: f64,
    },
}

/// Quantum Load Balancing Strategy
#[derive(Debug, Clone)]
pub enum QuantumLoadBalancingStrategy {
    QuantumSuperposition {
        superposition_weights: Array1<Complex<f64>>,
        measurement_basis: String,
    },
    QuantumEntanglement {
        entanglement_pairs: Vec<(String, String)>,
        coupling_strength: f64,
    },
}

/// Quantum Scheduling Algorithm
#[derive(Debug, Clone)]
pub enum QuantumSchedulingAlgorithm {
    QuantumAnnealing {
        annealing_schedule: AnnealingSchedule,
        optimization_target: OptimizationTarget,
    },
    QAOA {
        layers: usize,
        mixing_angles: Vec<f64>,
        cost_angles: Vec<f64>,
    },
}

impl Default for QuantumAwareResourceScheduler {
    fn default() -> Self {
        Self {
            quantum_resource_pool: QuantumResourcePool {
                quantum_units: Vec::new(),
                classical_units: Vec::new(),
                hybrid_units: Vec::new(),
                allocation_matrix: Array2::eye(4),
                coherence_times: HashMap::new(),
            },
            scheduling_algorithms: vec![
                QuantumSchedulingAlgorithm::QuantumAnnealing {
                    annealing_schedule: AnnealingSchedule {
                        initial_temperature: 1.0,
                        final_temperature: 0.01,
                        steps: 1000,
                        cooling_rate: 0.95,
                    },
                    optimization_target: OptimizationTarget::MinimizeTime,
                },
                QuantumSchedulingAlgorithm::QAOA {
                    layers: 3,
                    mixing_angles: vec![0.5, 0.7, 0.3],
                    cost_angles: vec![0.2, 0.8, 0.6],
                },
            ],
            quantum_load_balancer: QuantumLoadBalancer {
                strategies: vec![QuantumLoadBalancingStrategy::QuantumSuperposition {
                    superposition_weights: Array1::from_elem(4, Complex::new(0.5, 0.0)),
                    measurement_basis: "computational".to_string(),
                }],
                load_distribution: Array1::from_elem(4, 0.25),
                entanglement_connections: HashMap::new(),
                load_predictor: QuantumLoadPredictor {
                    quantum_nn: QuantumNeuralNetwork {
                        layers: Vec::new(),
                        classical_layers: Vec::new(),
                        training_params: QuantumTrainingParameters {
                            learning_rate: 0.01,
                            batch_size: 32,
                            epochs: 100,
                            optimizer: QuantumOptimizer::QuantumAdam {
                                beta1: 0.9,
                                beta2: 0.999,
                            },
                        },
                    },
                    prediction_horizon: 10,
                    historical_data: VecDeque::new(),
                    accuracy_metrics: PredictionAccuracyMetrics {
                        mae: 0.0,
                        rmse: 0.0,
                        r_squared: 0.0,
                        quantum_fidelity: 1.0,
                    },
                },
                strategy: "quantum_superposition".to_string(),
                load_metrics: HashMap::new(),
            },
            entanglement_graph: ResourceEntanglementGraph {
                adjacency_matrix: Array2::eye(4),
                nodes: HashMap::new(),
                entanglement_strengths: HashMap::new(),
                decoherence_tracking: HashMap::new(),
                connections: HashMap::new(),
            },
            optimization_engine: QuantumOptimizationEngine {
                algorithms: Vec::new(),
                optimizationstate: QuantumOptimizationState {
                    parameters: Array1::zeros(10),
                    objective_value: 0.0,
                    quantumstate: Array1::from_elem(4, Complex::new(0.5, 0.0)),
                    gradient: Array1::zeros(10),
                    iteration: 0,
                },
                optimizationhistory: VecDeque::new(),
                convergence_criteria: ConvergenceCriteria {
                    max_iterations: 1000,
                    objective_tolerance: 1e-6,
                    parameter_tolerance: 1e-8,
                    gradient_tolerance: 1e-6,
                },
                optimization_history: VecDeque::new(),
            },
            performance_monitor: QuantumPerformanceMonitor {
                metrics: QuantumPerformanceMetrics {
                    quantum_speedup: 1.0,
                    quantum_advantage_ratio: 1.0,
                    coherence_efficiency: 0.8,
                    entanglement_utilization: 0.5,
                    quantum_error_rate: 0.01,
                    resource_efficiency: 0.7,
                    throughput: 1000.0,
                    latency: 0.1,
                    error_rate: 0.01,
                    resource_utilization: 0.7,
                },
                monitoring_interval: 1.0,
            },
        }
    }
}