quantrs2_ml/

quantum_mixture_of_experts.rs

//! Quantum Mixture of Experts
//!
//! This module implements cutting-edge Quantum Mixture of Experts (QMoE) that leverages
//! quantum superposition and entanglement for scalable conditional computation. Unlike
//! classical MoE, QMoE can route information through multiple experts simultaneously
//! using quantum parallelism, enabling unprecedented computational efficiency and
//! novel expert interaction patterns.
//!
//! Key Features:
//! - Quantum superposition-based expert routing
//! - Entanglement-enhanced expert interactions
//! - Dynamic quantum gating mechanisms
//! - Sparse activation with quantum efficiency
//! - Multi-scale expert hierarchies
//! - Quantum load balancing and fairness

use crate::error::{MLError, Result};
use ndarray::{Array1, Array2, Array3, ArrayView1, Axis};
use num_complex::Complex64;
use rand::{Rng, SeedableRng};
use rand_chacha::ChaCha20Rng;
use std::collections::HashMap;
use std::f64::consts::PI;

/// Configuration for Quantum Mixture of Experts
#[derive(Debug, Clone)]
pub struct QuantumMixtureOfExpertsConfig {
    pub input_dim: usize,
    pub output_dim: usize,
    pub num_experts: usize,
    pub num_qubits: usize,
    pub expert_capacity: usize,
    pub routing_strategy: QuantumRoutingStrategy,
    pub expert_architecture: ExpertArchitecture,
    pub gating_mechanism: QuantumGatingMechanism,
    pub load_balancing: LoadBalancingStrategy,
    pub sparsity_config: SparsityConfig,
    pub entanglement_config: EntanglementConfig,
    pub quantum_enhancement_level: f64,
    pub enable_hierarchical_experts: bool,
    pub enable_dynamic_experts: bool,
    pub enable_quantum_communication: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumRoutingStrategy {
    /// Superposition-based routing with quantum parallelism
    QuantumSuperposition {
        superposition_strength: f64,
        interference_pattern: InterferencePattern,
    },

    /// Entanglement-based routing for correlated experts
    EntanglementRouting {
        entanglement_strength: f64,
        coupling_topology: CouplingTopology,
    },

    /// Quantum attention-based routing
    QuantumAttentionRouting {
        attention_heads: usize,
        attention_mechanism: QuantumAttentionMechanism,
    },

    /// Hierarchical quantum routing
    HierarchicalRouting {
        hierarchy_levels: usize,
        routing_per_level: RoutingType,
    },

    /// Adaptive quantum routing that learns optimal patterns
    AdaptiveQuantumRouting {
        adaptation_rate: f64,
        exploration_strategy: ExplorationStrategy,
    },

    /// Topological routing based on quantum graph structures
    TopologicalRouting {
        graph_structure: QuantumGraphStructure,
        propagation_method: PropagationMethod,
    },
}

#[derive(Debug, Clone)]
pub enum InterferencePattern {
    Constructive,
    Destructive,
    Mixed,
    Adaptive { adaptation_parameter: f64 },
    Custom { pattern_function: String },
}

#[derive(Debug, Clone)]
pub enum CouplingTopology {
    Linear,
    Circular,
    AllToAll,
    Random { connectivity: f64 },
    Hierarchical { branching_factor: usize },
    CustomGraph { adjacency_matrix: Array2<f64> },
}

#[derive(Debug, Clone)]
pub enum QuantumAttentionMechanism {
    QuantumSelfAttention,
    QuantumCrossAttention,
    QuantumMultiHeadAttention { num_heads: usize },
    EntanglementBasedAttention,
    QuantumFourierAttention,
}

#[derive(Debug, Clone)]
pub enum RoutingType {
    Standard,
    Quantum,
    Hybrid,
    Adaptive,
}

#[derive(Debug, Clone)]
pub enum ExplorationStrategy {
    EpsilonGreedy { epsilon: f64 },
    UCB { confidence_parameter: f64 },
    ThompsonSampling,
    QuantumAnnealing { temperature_schedule: Array1<f64> },
    EntanglementBased { entanglement_threshold: f64 },
}

#[derive(Debug, Clone)]
pub enum QuantumGraphStructure {
    SmallWorld { rewiring_probability: f64 },
    ScaleFree { preferential_attachment: f64 },
    Lattice { dimensions: Vec<usize> },
    Random { edge_probability: f64 },
    Community { num_communities: usize },
}

#[derive(Debug, Clone)]
pub enum PropagationMethod {
    QuantumWalk,
    DiffusionProcess,
    WaveFunction,
    MessagePassing,
}

#[derive(Debug, Clone)]
pub enum ExpertArchitecture {
    /// Standard feed-forward experts
    FeedForward {
        hidden_layers: Vec<usize>,
        activation: ActivationFunction,
    },

    /// Convolutional experts for spatial data
    Convolutional {
        channels: Vec<usize>,
        kernel_sizes: Vec<usize>,
        strides: Vec<usize>,
    },

    /// Attention-based experts
    AttentionBased {
        attention_type: AttentionType,
        attention_heads: usize,
        key_dim: usize,
    },

    /// Recurrent experts for sequential data
    Recurrent {
        cell_type: RecurrentCellType,
        hidden_size: usize,
        num_layers: usize,
    },

    /// Quantum experts with quantum gates
    QuantumExperts {
        quantum_layers: Vec<QuantumExpertLayer>,
        measurement_strategy: MeasurementStrategy,
    },

    /// Hybrid quantum-classical experts
    HybridExperts {
        quantum_component: QuantumComponent,
        classical_component: ClassicalComponent,
        interaction_method: InteractionMethod,
    },

    /// Specialized experts for specific modalities
    SpecializedExperts {
        expert_specializations: Vec<ExpertSpecialization>,
        specialization_strength: f64,
    },
}

#[derive(Debug, Clone)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Swish,
    Tanh,
    Sigmoid,
    Mish,
    QuantumActivation {
        activation_type: QuantumActivationType,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumActivationType {
    QuantumReLU,
    QuantumSigmoid,
    QuantumTanh,
    EntanglementActivation,
    PhaseActivation,
}

#[derive(Debug, Clone)]
pub enum AttentionType {
    SelfAttention,
    CrossAttention,
    MultiHeadAttention,
    QuantumAttention,
}

#[derive(Debug, Clone)]
pub enum RecurrentCellType {
    LSTM,
    GRU,
    QuantumLSTM,
    QuantumGRU,
}

#[derive(Debug, Clone)]
pub struct QuantumExpertLayer {
    layer_type: QuantumLayerType,
    num_qubits: usize,
    parameters: Array1<f64>,
    quantum_gates: Vec<QuantumGate>,
}

#[derive(Debug, Clone)]
pub enum QuantumLayerType {
    VariationalLayer,
    EntanglementLayer,
    RotationLayer,
    MeasurementLayer,
    ConditionalLayer,
}

#[derive(Debug, Clone)]
pub struct QuantumGate {
    gate_type: GateType,
    target_qubits: Vec<usize>,
    control_qubits: Vec<usize>,
    parameters: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum GateType {
    Rotation { axis: RotationAxis },
    Controlled { base_gate: String },
    Entangling { coupling_strength: f64 },
    Measurement { basis: MeasurementBasis },
    Custom { gate_matrix: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub enum RotationAxis {
    X,
    Y,
    Z,
    Custom { direction: Array1<f64> },
}

#[derive(Debug, Clone)]
pub enum MeasurementBasis {
    Computational,
    PauliX,
    PauliY,
    PauliZ,
    Bell,
    Custom { basis_vectors: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub enum MeasurementStrategy {
    ExpectationValues,
    ProbabilityDistributions,
    QuantumStateVector,
    PartialMeasurements,
}

#[derive(Debug, Clone)]
pub struct QuantumComponent {
    component_type: QuantumComponentType,
    num_qubits: usize,
    quantum_circuit: QuantumCircuit,
    entanglement_structure: EntanglementStructure,
}

#[derive(Debug, Clone)]
pub enum QuantumComponentType {
    VariationalQuantumCircuit,
    QuantumConvolutional,
    QuantumAttention,
    QuantumRecurrent,
}

#[derive(Debug, Clone)]
pub struct QuantumCircuit {
    gates: Vec<QuantumGate>,
    depth: usize,
    parameters: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct EntanglementStructure {
    entanglement_map: Array2<f64>,
    entanglement_strength: f64,
    entanglement_pattern: EntanglementPattern,
}

#[derive(Debug, Clone)]
pub enum EntanglementPattern {
    Linear,
    Circular,
    AllToAll,
    Hierarchical { levels: usize },
    Random { probability: f64 },
    Adaptive { adaptation_rate: f64 },
}

#[derive(Debug, Clone)]
pub struct ClassicalComponent {
    layers: Vec<ClassicalLayer>,
    architecture: ClassicalArchitecture,
}

#[derive(Debug, Clone)]
pub struct ClassicalLayer {
    layer_type: ClassicalLayerType,
    parameters: Array2<f64>,
    activation: ActivationFunction,
}

#[derive(Debug, Clone)]
pub enum ClassicalLayerType {
    Dense {
        input_dim: usize,
        output_dim: usize,
    },
    Convolutional {
        channels: usize,
        kernel_size: usize,
    },
    Attention {
        attention_dim: usize,
    },
    Normalization {
        normalization_type: NormalizationType,
    },
}

#[derive(Debug, Clone)]
pub enum ClassicalArchitecture {
    FeedForward,
    Convolutional,
    Recurrent,
    Transformer,
}

#[derive(Debug, Clone)]
pub enum NormalizationType {
    BatchNorm,
    LayerNorm,
    InstanceNorm,
    GroupNorm,
}

#[derive(Debug, Clone)]
pub enum InteractionMethod {
    TensorProduct,
    DirectSum,
    ConditionalCoupling,
    AttentionCoupling,
    QuantumClassicalHybrid,
}

#[derive(Debug, Clone)]
pub enum ExpertSpecialization {
    TextProcessing,
    ImageProcessing,
    AudioProcessing,
    VideoProcessing,
    GraphProcessing,
    TimeSeriesProcessing,
    MultiModal,
    Domain { domain_name: String },
}

#[derive(Debug, Clone)]
pub enum QuantumGatingMechanism {
    /// Quantum superposition gating
    SuperpositionGating { coherence_preservation: f64 },

    /// Measurement-based gating
    MeasurementGating {
        measurement_basis: MeasurementBasis,
        post_selection: bool,
    },

    /// Entanglement-based gating
    EntanglementGating {
        entanglement_threshold: f64,
        gating_strength: f64,
    },

    /// Quantum attention gating
    QuantumAttentionGating {
        attention_mechanism: QuantumAttentionMechanism,
        temperature: f64,
    },

    /// Adaptive quantum gating
    AdaptiveGating {
        adaptation_strategy: AdaptationStrategy,
        learning_rate: f64,
    },

    /// Hierarchical gating with quantum circuits
    HierarchicalGating { gating_hierarchy: GatingHierarchy },
}

#[derive(Debug, Clone)]
pub enum AdaptationStrategy {
    GradientBased,
    EvolutionaryStrategy,
    QuantumAnnealing,
    ReinforcementLearning,
    BayesianOptimization,
}

#[derive(Debug, Clone)]
pub struct GatingHierarchy {
    levels: Vec<GatingLevel>,
    level_interactions: Array2<f64>,
}

#[derive(Debug, Clone)]
pub struct GatingLevel {
    level_id: usize,
    gating_type: QuantumGatingMechanism,
    expert_groups: Vec<ExpertGroup>,
}

#[derive(Debug, Clone)]
pub struct ExpertGroup {
    group_id: usize,
    expert_indices: Vec<usize>,
    group_specialization: Option<ExpertSpecialization>,
    internal_routing: RoutingType,
}

#[derive(Debug, Clone)]
pub enum LoadBalancingStrategy {
    /// No load balancing
    None,

    /// Uniform load balancing
    Uniform,

    /// Capacity-aware balancing
    CapacityAware { capacity_factors: Array1<f64> },

    /// Performance-based balancing
    PerformanceBased { performance_weights: Array1<f64> },

    /// Quantum fairness balancing
    QuantumFairness {
        fairness_metric: FairnessMetric,
        balancing_strength: f64,
    },

    /// Dynamic balancing with adaptation
    DynamicBalancing {
        adaptation_rate: f64,
        balancing_history: usize,
    },

    /// Entropy-based balancing
    EntropyBalancing {
        target_entropy: f64,
        entropy_weight: f64,
    },
}

#[derive(Debug, Clone)]
pub enum FairnessMetric {
    Equal,
    Proportional,
    QuantumEntropy,
    InformationTheoretic,
}

#[derive(Debug, Clone)]
pub struct SparsityConfig {
    pub target_sparsity: f64,
    pub sparsity_method: SparsityMethod,
    pub sparsity_schedule: SparsitySchedule,
    pub quantum_sparsity_enhancement: f64,
}

#[derive(Debug, Clone)]
pub enum SparsityMethod {
    TopK { k: usize },
    Threshold { threshold: f64 },
    QuantumSelection { selection_probability: f64 },
    AdaptiveSparsity { adaptation_rate: f64 },
    EntanglementBased { entanglement_threshold: f64 },
}

#[derive(Debug, Clone)]
pub enum SparsitySchedule {
    Constant,
    Linear { start: f64, end: f64 },
    Exponential { decay_rate: f64 },
    Adaptive { target_performance: f64 },
    QuantumAnnealed { temperature_schedule: Array1<f64> },
}

#[derive(Debug, Clone)]
pub struct EntanglementConfig {
    pub enable_expert_entanglement: bool,
    pub entanglement_strength: f64,
    pub entanglement_decay: f64,
    pub entanglement_restoration: f64,
    pub max_entanglement_range: usize,
    pub entanglement_pattern: EntanglementPattern,
}

impl Default for EntanglementConfig {
    fn default() -> Self {
        Self {
            enable_expert_entanglement: false,
            entanglement_strength: 0.5,
            entanglement_decay: 0.01,
            entanglement_restoration: 0.05,
            max_entanglement_range: 4,
            entanglement_pattern: EntanglementPattern::Linear,
        }
    }
}

/// Main Quantum Mixture of Experts model
pub struct QuantumMixtureOfExperts {
    config: QuantumMixtureOfExpertsConfig,

    // Core components
    experts: Vec<QuantumExpert>,
    quantum_router: QuantumRouter,
    quantum_gate_network: QuantumGateNetwork,

    // Load balancing and fairness
    load_balancer: LoadBalancer,
    expert_statistics: ExpertStatistics,

    // Training and optimization
    training_history: Vec<MoETrainingMetrics>,
    routing_optimizer: RoutingOptimizer,
    expert_optimizer: ExpertOptimizer,

    // Quantum state management
    quantum_state_tracker: QuantumStateTracker,
    entanglement_manager: EntanglementManager,

    // Performance monitoring
    performance_monitor: PerformanceMonitor,
    capacity_manager: CapacityManager,
}

#[derive(Debug, Clone)]
pub struct QuantumExpert {
    expert_id: usize,
    architecture: ExpertArchitecture,
    quantum_parameters: Array1<f64>,
    classical_parameters: Array2<f64>,
    specialization: Option<ExpertSpecialization>,
    capacity: usize,
    current_load: usize,
    performance_history: Vec<f64>,
    quantum_state: QuantumExpertState,
}

#[derive(Debug, Clone)]
pub struct QuantumExpertState {
    quantum_amplitudes: Array1<Complex64>,
    entanglement_connections: Vec<usize>,
    coherence_time: f64,
    fidelity: f64,
    quantum_volume: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumRouter {
    routing_strategy: QuantumRoutingStrategy,
    routing_network: QuantumRoutingNetwork,
    routing_parameters: Array1<f64>,
    routing_history: Vec<RoutingDecision>,
    quantum_routing_state: QuantumRoutingState,
    num_experts: usize,
}

#[derive(Debug, Clone)]
pub struct QuantumRoutingNetwork {
    routing_layers: Vec<QuantumRoutingLayer>,
    attention_mechanisms: Vec<QuantumAttentionHead>,
    entanglement_couplings: Vec<EntanglementCoupling>,
}

#[derive(Debug, Clone)]
pub struct QuantumRoutingLayer {
    layer_type: RoutingLayerType,
    quantum_gates: Vec<QuantumGate>,
    routing_weights: Array2<f64>,
    activation_function: ActivationFunction,
}

#[derive(Debug, Clone)]
pub enum RoutingLayerType {
    QuantumLinear,
    QuantumAttention,
    QuantumConvolutional,
    QuantumRecurrent,
    HybridLayer,
}

#[derive(Debug, Clone)]
pub struct QuantumAttentionHead {
    head_id: usize,
    query_projection: QuantumProjection,
    key_projection: QuantumProjection,
    value_projection: QuantumProjection,
    attention_weights: Array2<f64>,
    entanglement_strength: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumProjection {
    projection_type: ProjectionType,
    quantum_circuit: QuantumCircuit,
    parameters: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum ProjectionType {
    Linear,
    Nonlinear,
    Quantum,
    Hybrid,
}

#[derive(Debug, Clone)]
pub struct EntanglementCoupling {
    coupling_qubits: Vec<usize>,
    coupling_strength: f64,
    coupling_type: CouplingType,
    time_evolution: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum CouplingType {
    CNOT,
    CZ,
    SWAP,
    Custom { coupling_matrix: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub struct RoutingDecision {
    decision_id: usize,
    expert_weights: Array1<f64>,
    routing_confidence: f64,
    quantum_coherence: f64,
    entanglement_measure: f64,
    decision_quality: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumRoutingState {
    routing_amplitudes: Array1<Complex64>,
    routing_entanglement: f64,
    routing_coherence: f64,
    routing_fidelity: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumGateNetwork {
    gating_mechanism: QuantumGatingMechanism,
    gate_parameters: Array1<f64>,
    gating_history: Vec<GatingDecision>,
    quantum_gate_state: QuantumGateState,
}

#[derive(Debug, Clone)]
pub struct GatingDecision {
    gate_weights: Array1<f64>,
    gate_confidence: f64,
    sparsity_level: f64,
    quantum_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumGateState {
    gate_amplitudes: Array1<Complex64>,
    gate_entanglement: f64,
    gate_coherence: f64,
}

#[derive(Debug, Clone)]
pub struct LoadBalancer {
    strategy: LoadBalancingStrategy,
    balancing_parameters: Array1<f64>,
    load_history: Vec<LoadBalancingState>,
    fairness_metrics: FairnessMetrics,
}

#[derive(Debug, Clone)]
pub struct LoadBalancingState {
    expert_loads: Array1<f64>,
    load_variance: f64,
    utilization_efficiency: f64,
    fairness_score: f64,
}

#[derive(Debug, Clone)]
pub struct FairnessMetrics {
    gini_coefficient: f64,
    entropy_measure: f64,
    quantum_fairness: f64,
    balance_score: f64,
}

#[derive(Debug, Clone)]
pub struct ExpertStatistics {
    expert_utilizations: Array1<f64>,
    expert_performances: Array1<f64>,
    expert_specializations: Array1<f64>,
    expert_interactions: Array2<f64>,
    quantum_correlations: Array2<f64>,
}

#[derive(Debug, Clone)]
pub struct RoutingOptimizer {
    optimizer_type: OptimizerType,
    learning_rate: f64,
    optimization_history: Vec<RoutingOptimizationStep>,
    gradient_estimator: GradientEstimator,
}

#[derive(Debug, Clone)]
pub enum OptimizerType {
    Adam { beta1: f64, beta2: f64 },
    SGD { momentum: f64 },
    RMSprop { decay: f64 },
    QuantumNaturalGradient,
    ParameterShiftRule,
}

#[derive(Debug, Clone)]
pub struct RoutingOptimizationStep {
    step_id: usize,
    gradient_norm: f64,
    learning_rate_used: f64,
    optimization_objective: f64,
    convergence_metric: f64,
}

#[derive(Debug, Clone)]
pub enum GradientEstimator {
    ExactGradient,
    FiniteDifference { epsilon: f64 },
    ParameterShift,
    QuantumNaturalGradient,
    StochasticEstimation { num_samples: usize },
}

#[derive(Debug, Clone)]
pub struct ExpertOptimizer {
    optimizer_type: OptimizerType,
    expert_learning_rates: Array1<f64>,
    expert_optimization_history: Vec<ExpertOptimizationStep>,
}

#[derive(Debug, Clone)]
pub struct ExpertOptimizationStep {
    expert_id: usize,
    gradient_norm: f64,
    parameter_update_norm: f64,
    performance_change: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumStateTracker {
    state_history: Vec<QuantumSystemState>,
    coherence_tracking: CoherenceTracker,
    entanglement_tracking: EntanglementTracker,
    fidelity_tracking: FidelityTracker,
}

#[derive(Debug, Clone)]
pub struct QuantumSystemState {
    timestamp: usize,
    total_entanglement: f64,
    system_coherence: f64,
    quantum_volume_utilization: f64,
    expert_quantum_states: Vec<QuantumExpertState>,
}

#[derive(Debug, Clone)]
pub struct CoherenceTracker {
    coherence_history: Vec<f64>,
    decoherence_rate: f64,
    coherence_preservation_strategies: Vec<CoherenceStrategy>,
}

#[derive(Debug, Clone)]
pub enum CoherenceStrategy {
    DynamicalDecoupling,
    ErrorCorrection,
    DecoherenceSupression,
    QuantumZeno,
}

#[derive(Debug, Clone)]
pub struct EntanglementTracker {
    entanglement_history: Vec<EntanglementMeasurement>,
    entanglement_budget: f64,
    entanglement_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct EntanglementMeasurement {
    timestamp: usize,
    concurrence: f64,
    negativity: f64,
    entanglement_entropy: f64,
    quantum_discord: f64,
}

#[derive(Debug, Clone)]
pub struct FidelityTracker {
    fidelity_history: Vec<f64>,
    target_fidelity: f64,
    fidelity_optimization: FidelityOptimization,
}

#[derive(Debug, Clone)]
pub enum FidelityOptimization {
    ProcessTomography,
    StateTomography,
    DirectFidelityEstimation,
    QuantumBenchmarking,
}

#[derive(Debug, Clone)]
pub struct EntanglementManager {
    entanglement_config: EntanglementConfig,
    entanglement_operations: Vec<EntanglementOperation>,
    entanglement_scheduler: EntanglementScheduler,
}

#[derive(Debug, Clone)]
pub struct EntanglementOperation {
    operation_type: EntanglementOperationType,
    target_experts: Vec<usize>,
    entanglement_strength: f64,
    operation_fidelity: f64,
}

#[derive(Debug, Clone)]
pub enum EntanglementOperationType {
    CreateEntanglement,
    BreakEntanglement,
    ModifyEntanglement,
    MeasureEntanglement,
}

#[derive(Debug, Clone)]
pub struct EntanglementScheduler {
    scheduling_strategy: SchedulingStrategy,
    entanglement_budget: f64,
    operation_queue: Vec<EntanglementOperation>,
}

#[derive(Debug, Clone)]
pub enum SchedulingStrategy {
    GreedyScheduling,
    OptimalScheduling,
    HeuristicScheduling,
    AdaptiveScheduling,
}

#[derive(Debug, Clone)]
pub struct PerformanceMonitor {
    performance_metrics: PerformanceMetrics,
    monitoring_config: MonitoringConfig,
    alert_system: AlertSystem,
}

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    throughput: f64,
    latency: f64,
    accuracy: f64,
    expert_utilization: Array1<f64>,
    quantum_efficiency: f64,
    resource_utilization: f64,
}

#[derive(Debug, Clone)]
pub struct MonitoringConfig {
    monitoring_frequency: usize,
    metrics_to_track: Vec<MetricType>,
    alert_thresholds: HashMap<String, f64>,
}

#[derive(Debug, Clone)]
pub enum MetricType {
    Performance,
    ResourceUtilization,
    QuantumCoherence,
    ExpertLoad,
    RoutingEfficiency,
}

#[derive(Debug, Clone)]
pub struct AlertSystem {
    alert_rules: Vec<AlertRule>,
    alert_history: Vec<Alert>,
}

#[derive(Debug, Clone)]
pub struct AlertRule {
    rule_id: String,
    metric_name: String,
    threshold: f64,
    comparison: ComparisonType,
    action: AlertAction,
}

#[derive(Debug, Clone)]
pub enum ComparisonType {
    GreaterThan,
    LessThan,
    Equal,
    NotEqual,
}

#[derive(Debug, Clone)]
pub enum AlertAction {
    Log,
    Rebalance,
    OptimizeRouting,
    RestoreCoherence,
}

#[derive(Debug, Clone)]
pub struct Alert {
    alert_id: String,
    timestamp: usize,
    severity: AlertSeverity,
    message: String,
    affected_components: Vec<String>,
}

#[derive(Debug, Clone)]
pub enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

#[derive(Debug, Clone)]
pub struct CapacityManager {
    total_capacity: usize,
    available_capacity: usize,
    capacity_allocation: Array1<f64>,
    capacity_optimization: CapacityOptimization,
}

#[derive(Debug, Clone)]
pub enum CapacityOptimization {
    StaticAllocation,
    DynamicAllocation,
    PredictiveAllocation,
    QuantumOptimizedAllocation,
}

// Training and evaluation structures
#[derive(Debug, Clone)]
pub struct MoETrainingMetrics {
    pub epoch: usize,
    pub loss: f64,
    pub routing_efficiency: f64,
    pub expert_utilization: f64,
    pub load_balance_score: f64,
    pub quantum_coherence: f64,
    pub entanglement_utilization: f64,
    pub sparsity_achieved: f64,
    pub throughput: f64,
    pub quantum_advantage: f64,
}

// Main implementation
impl QuantumMixtureOfExperts {
    /// Create a new Quantum Mixture of Experts
    pub fn new(config: QuantumMixtureOfExpertsConfig) -> Result<Self> {
        println!("🧠 Initializing Quantum Mixture of Experts in UltraThink Mode");

        // Initialize experts
        let experts = Self::create_experts(&config)?;

        // Initialize quantum router
        let quantum_router = QuantumRouter::new(&config)?;

        // Initialize quantum gate network
        let quantum_gate_network = QuantumGateNetwork::new(&config)?;

        // Initialize load balancer
        let load_balancer = LoadBalancer::new(&config)?;

        // Initialize statistics and monitoring
        let expert_statistics = ExpertStatistics::new(config.num_experts);
        let performance_monitor = PerformanceMonitor::new(&config)?;
        let capacity_manager = CapacityManager::new(&config)?;

        // Initialize optimizers
        let routing_optimizer = RoutingOptimizer::new(&config)?;
        let expert_optimizer = ExpertOptimizer::new(&config)?;

        // Initialize quantum state management
        let quantum_state_tracker = QuantumStateTracker::new(&config)?;
        let entanglement_manager = EntanglementManager::new(&config)?;

        Ok(Self {
            config,
            experts,
            quantum_router,
            quantum_gate_network,
            load_balancer,
            expert_statistics,
            training_history: Vec::new(),
            routing_optimizer,
            expert_optimizer,
            quantum_state_tracker,
            entanglement_manager,
            performance_monitor,
            capacity_manager,
        })
    }

    /// Forward pass through the quantum mixture of experts
    pub fn forward(&mut self, input: &Array1<f64>) -> Result<MoEOutput> {
        // Quantum routing to determine expert weights
        let routing_result = self.quantum_router.route(input)?;

        // Apply quantum gating mechanism
        let gating_result = self.quantum_gate_network.gate(&routing_result)?;

        // Apply load balancing
        let balanced_weights = self
            .load_balancer
            .balance_loads(&gating_result.expert_weights)?;

        // Process through selected experts in quantum superposition
        let expert_outputs = self.process_through_experts(input, &balanced_weights)?;

        // Combine expert outputs using quantum interference
        let combined_output = self.combine_expert_outputs(&expert_outputs, &balanced_weights)?;

        // Update quantum states and entanglement
        self.update_quantum_states(&routing_result, &gating_result)?;

        // Update statistics
        self.update_expert_statistics(&balanced_weights, &expert_outputs)?;

        // Monitor performance
        self.performance_monitor
            .update(&combined_output, &balanced_weights)?;

        Ok(MoEOutput {
            output: combined_output.prediction,
            expert_weights: balanced_weights,
            routing_decision: routing_result,
            gating_decision: gating_result,
            expert_outputs,
            quantum_metrics: combined_output.quantum_metrics,
        })
    }

    /// Create experts based on configuration
    fn create_experts(config: &QuantumMixtureOfExpertsConfig) -> Result<Vec<QuantumExpert>> {
        let mut experts = Vec::new();

        for expert_id in 0..config.num_experts {
            let expert = QuantumExpert::new(expert_id, config)?;
            experts.push(expert);
        }

        Ok(experts)
    }

    /// Process input through selected experts
    fn process_through_experts(
        &mut self,
        input: &Array1<f64>,
        expert_weights: &Array1<f64>,
    ) -> Result<Vec<ExpertOutput>> {
        let mut expert_outputs = Vec::new();

        for (expert_id, expert) in self.experts.iter_mut().enumerate() {
            let weight = expert_weights[expert_id];

            // Skip experts with negligible weights for efficiency
            if weight < 1e-6 {
                expert_outputs.push(ExpertOutput::default());
                continue;
            }

            // Process through expert
            let output = expert.process(input, weight, &self.config)?;
            expert_outputs.push(output);
        }

        Ok(expert_outputs)
    }

    /// Combine expert outputs using quantum interference
    fn combine_expert_outputs(
        &self,
        expert_outputs: &[ExpertOutput],
        weights: &Array1<f64>,
    ) -> Result<CombinedOutput> {
        let output_dim = self.config.output_dim;
        let mut combined_prediction = Array1::zeros(output_dim);
        let mut total_weight = 0.0;
        let mut quantum_metrics = QuantumCombinationMetrics::default();

        // Weighted combination with quantum interference effects
        for (expert_id, output) in expert_outputs.iter().enumerate() {
            let weight = weights[expert_id];
            if weight > 1e-6 {
                // Apply quantum interference pattern
                let interference_factor = self.compute_interference_factor(expert_id, &weights)?;
                let effective_weight = weight * interference_factor;

                combined_prediction =
                    &combined_prediction + &(effective_weight * &output.prediction);
                total_weight += effective_weight;

                // Accumulate quantum metrics
                quantum_metrics.accumulate(&output.quantum_metrics, effective_weight);
            }
        }

        // Normalize
        if total_weight > 1e-10 {
            combined_prediction = combined_prediction / total_weight;
        }

        // Finalize quantum metrics
        quantum_metrics.finalize(total_weight);

        Ok(CombinedOutput {
            prediction: combined_prediction,
            quantum_metrics,
        })
    }

    /// Compute quantum interference factor between experts
    fn compute_interference_factor(&self, expert_id: usize, weights: &Array1<f64>) -> Result<f64> {
        let mut interference_factor = 1.0;

        match &self.config.routing_strategy {
            QuantumRoutingStrategy::QuantumSuperposition {
                interference_pattern,
                ..
            } => {
                match interference_pattern {
                    InterferencePattern::Constructive => {
                        // Enhance contributions from highly weighted experts
                        interference_factor = 1.0 + 0.1 * weights[expert_id];
                    }
                    InterferencePattern::Destructive => {
                        // Reduce contributions based on other expert weights
                        let other_weights_sum: f64 = weights
                            .iter()
                            .enumerate()
                            .filter(|(i, _)| *i != expert_id)
                            .map(|(_, w)| *w)
                            .sum();
                        interference_factor = 1.0 - 0.05 * other_weights_sum;
                    }
                    InterferencePattern::Mixed => {
                        // Combine constructive and destructive effects
                        let constructive = 1.0 + 0.05 * weights[expert_id];
                        let destructive = 1.0 - 0.025 * (weights.sum() - weights[expert_id]);
                        interference_factor = 0.5 * (constructive + destructive);
                    }
                    _ => {
                        interference_factor = 1.0;
                    }
                }
            }
            _ => {
                interference_factor = 1.0;
            }
        }

        Ok(interference_factor.max(0.1)) // Ensure positive interference
    }

    /// Update quantum states after processing
    fn update_quantum_states(
        &mut self,
        routing_result: &RoutingResult,
        gating_result: &GatingResult,
    ) -> Result<()> {
        // Update entanglement between experts
        self.entanglement_manager
            .update_entanglement(&routing_result.expert_weights)?;

        // Track quantum coherence
        self.quantum_state_tracker
            .update_coherence(routing_result.quantum_coherence)?;

        // Update expert quantum states
        for (expert_id, expert) in self.experts.iter_mut().enumerate() {
            expert.update_quantum_state(
                routing_result.expert_weights[expert_id],
                gating_result.quantum_efficiency,
            )?;
        }

        Ok(())
    }

    /// Update expert utilization statistics
    fn update_expert_statistics(
        &mut self,
        weights: &Array1<f64>,
        outputs: &[ExpertOutput],
    ) -> Result<()> {
        // Update utilization statistics
        for (expert_id, &weight) in weights.iter().enumerate() {
            if expert_id < self.expert_statistics.expert_utilizations.len() {
                self.expert_statistics.expert_utilizations[expert_id] =
                    0.9 * self.expert_statistics.expert_utilizations[expert_id] + 0.1 * weight;
            }

            if let Some(output) = outputs.get(expert_id) {
                if expert_id < self.expert_statistics.expert_performances.len() {
                    self.expert_statistics.expert_performances[expert_id] = 0.9
                        * self.expert_statistics.expert_performances[expert_id]
                        + 0.1 * output.quality_score;
                }
            }
        }

        // Update expert interaction matrix
        for i in 0..self.config.num_experts {
            for j in i + 1..self.config.num_experts {
                let interaction = weights[i] * weights[j];
                self.expert_statistics.expert_interactions[[i, j]] =
                    0.9 * self.expert_statistics.expert_interactions[[i, j]] + 0.1 * interaction;
                self.expert_statistics.expert_interactions[[j, i]] =
                    self.expert_statistics.expert_interactions[[i, j]];
            }
        }

        Ok(())
    }

    /// Train the quantum mixture of experts
    pub fn train(
        &mut self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        training_config: &MoETrainingConfig,
    ) -> Result<MoETrainingOutput> {
        let mut training_losses = Vec::new();
        let mut routing_efficiency_history = Vec::new();
        let mut quantum_metrics_history = Vec::new();

        println!("🚀 Starting Quantum Mixture of Experts Training in UltraThink Mode");

        for epoch in 0..training_config.epochs {
            let epoch_metrics = self.train_epoch(data, targets, training_config, epoch)?;

            training_losses.push(epoch_metrics.loss);
            routing_efficiency_history.push(epoch_metrics.routing_efficiency);

            // Update learning rates and strategies
            self.update_training_strategies(&epoch_metrics)?;

            // Apply load balancing adjustments
            self.load_balancer.adapt_strategy(&epoch_metrics)?;

            // Optimize quantum parameters
            self.optimize_quantum_parameters(&epoch_metrics)?;

            self.training_history.push(epoch_metrics.clone());
            quantum_metrics_history.push(QuantumMoEMetrics {
                quantum_coherence: epoch_metrics.quantum_coherence,
                entanglement_utilization: epoch_metrics.entanglement_utilization,
                quantum_advantage: epoch_metrics.quantum_advantage,
                routing_efficiency: epoch_metrics.routing_efficiency,
            });

            // Logging
            if epoch % training_config.log_interval == 0 {
                println!(
                    "Epoch {}: Loss = {:.6}, Routing Efficiency = {:.4}, Expert Utilization = {:.4}, Quantum Advantage = {:.2}x",
                    epoch,
                    epoch_metrics.loss,
                    epoch_metrics.routing_efficiency,
                    epoch_metrics.expert_utilization,
                    epoch_metrics.quantum_advantage,
                );
            }
        }

        let convergence_analysis = self.analyze_convergence(&training_losses)?;

        Ok(MoETrainingOutput {
            training_losses,
            routing_efficiency_history,
            quantum_metrics_history,
            final_expert_statistics: self.expert_statistics.clone(),
            convergence_analysis,
        })
    }

    /// Train single epoch
    fn train_epoch(
        &mut self,
        data: &Array2<f64>,
        targets: &Array2<f64>,
        config: &MoETrainingConfig,
        epoch: usize,
    ) -> Result<MoETrainingMetrics> {
        let mut epoch_loss = 0.0;
        let mut routing_efficiency_sum = 0.0;
        let mut expert_utilization_sum = 0.0;
        let mut quantum_coherence_sum = 0.0;
        let mut entanglement_sum = 0.0;
        let mut num_batches = 0;

        let num_samples = data.nrows();

        for batch_start in (0..num_samples).step_by(config.batch_size) {
            let batch_end = (batch_start + config.batch_size).min(num_samples);
            let batch_data = data.slice(ndarray::s![batch_start..batch_end, ..]);
            let batch_targets = targets.slice(ndarray::s![batch_start..batch_end, ..]);

            let batch_metrics = self.train_batch(&batch_data, &batch_targets, config)?;

            epoch_loss += batch_metrics.loss;
            routing_efficiency_sum += batch_metrics.routing_efficiency;
            expert_utilization_sum += batch_metrics.expert_utilization;
            quantum_coherence_sum += batch_metrics.quantum_coherence;
            entanglement_sum += batch_metrics.entanglement_utilization;
            num_batches += 1;
        }

        let num_batches_f = num_batches as f64;
        Ok(MoETrainingMetrics {
            epoch,
            loss: epoch_loss / num_batches_f,
            routing_efficiency: routing_efficiency_sum / num_batches_f,
            expert_utilization: expert_utilization_sum / num_batches_f,
            load_balance_score: self.compute_load_balance_score()?,
            quantum_coherence: quantum_coherence_sum / num_batches_f,
            entanglement_utilization: entanglement_sum / num_batches_f,
            sparsity_achieved: self.compute_sparsity_achieved()?,
            throughput: num_samples as f64 / 1.0, // Simplified throughput
            quantum_advantage: self.estimate_quantum_advantage()?,
        })
    }

    /// Train single batch
    fn train_batch(
        &mut self,
        batch_data: &ndarray::ArrayView2<f64>,
        batch_targets: &ndarray::ArrayView2<f64>,
        config: &MoETrainingConfig,
    ) -> Result<MoETrainingMetrics> {
        let mut batch_loss = 0.0;
        let mut routing_efficiency_sum = 0.0;
        let mut expert_utilization_sum = 0.0;
        let mut quantum_coherence_sum = 0.0;
        let mut entanglement_sum = 0.0;

        for (sample_idx, (input, target)) in batch_data
            .rows()
            .into_iter()
            .zip(batch_targets.rows())
            .enumerate()
        {
            let input_array = input.to_owned();
            let target_array = target.to_owned();

            // Forward pass
            let output = self.forward(&input_array)?;

            // Compute loss
            let loss = self.compute_loss(&output.output, &target_array, &output)?;
            batch_loss += loss;

            // Accumulate metrics
            routing_efficiency_sum += output.routing_decision.routing_confidence;
            expert_utilization_sum += output.expert_weights.sum() / self.config.num_experts as f64;
            quantum_coherence_sum += output.quantum_metrics.coherence;
            entanglement_sum += output.quantum_metrics.entanglement;

            // Backward pass and parameter updates
            self.update_parameters(&output, &target_array, config)?;
        }

        let num_samples = batch_data.nrows() as f64;
        Ok(MoETrainingMetrics {
            epoch: 0, // Will be set by caller
            loss: batch_loss / num_samples,
            routing_efficiency: routing_efficiency_sum / num_samples,
            expert_utilization: expert_utilization_sum / num_samples,
            load_balance_score: self.compute_load_balance_score()?,
            quantum_coherence: quantum_coherence_sum / num_samples,
            entanglement_utilization: entanglement_sum / num_samples,
            sparsity_achieved: self.compute_sparsity_achieved()?,
            throughput: num_samples,
            quantum_advantage: self.estimate_quantum_advantage()?,
        })
    }

    /// Compute loss function
    fn compute_loss(
        &self,
        prediction: &Array1<f64>,
        target: &Array1<f64>,
        output: &MoEOutput,
    ) -> Result<f64> {
        // Base prediction loss (MSE)
        let mse_loss = (prediction - target).mapv(|x| x * x).sum() / prediction.len() as f64;

        // Load balancing loss
        let load_balance_loss = self.compute_load_balance_loss(&output.expert_weights)?;

        // Sparsity loss
        let sparsity_loss = self.compute_sparsity_loss(&output.expert_weights)?;

        // Quantum coherence preservation loss
        let coherence_loss = 1.0 - output.quantum_metrics.coherence;

        // Total loss
        let total_loss =
            mse_loss + 0.01 * load_balance_loss + 0.001 * sparsity_loss + 0.1 * coherence_loss;

        Ok(total_loss)
    }

    /// Update model parameters
    fn update_parameters(
        &mut self,
        output: &MoEOutput,
        target: &Array1<f64>,
        config: &MoETrainingConfig,
    ) -> Result<()> {
        // Update routing parameters
        // Convert RoutingResult to RoutingDecision for compatibility
        let routing_decision = RoutingDecision {
            decision_id: 0,
            expert_weights: output.routing_decision.expert_weights.clone(),
            routing_confidence: output.routing_decision.routing_confidence,
            quantum_coherence: output.routing_decision.quantum_coherence,
            entanglement_measure: 0.0, // Default value
            decision_quality: output.routing_decision.routing_confidence,
        };

        self.routing_optimizer.update_routing_parameters(
            &routing_decision,
            target,
            config.routing_learning_rate,
        )?;

        // Update expert parameters
        self.expert_optimizer.update_expert_parameters(
            &self.experts,
            &output.expert_outputs,
            &output.expert_weights,
            target,
            config.expert_learning_rate,
        )?;

        // Update quantum parameters
        self.update_quantum_parameters_from_loss(output, target)?;

        Ok(())
    }

    /// Get current model statistics
    pub fn get_statistics(&self) -> MoEStatistics {
        MoEStatistics {
            expert_utilizations: self.expert_statistics.expert_utilizations.clone(),
            expert_performances: self.expert_statistics.expert_performances.clone(),
            load_balance_score: self.compute_load_balance_score().unwrap_or(0.0),
            routing_efficiency: self.compute_routing_efficiency(),
            quantum_coherence: self.quantum_state_tracker.get_current_coherence(),
            entanglement_utilization: self.entanglement_manager.get_utilization(),
            total_parameters: self.count_total_parameters(),
            memory_usage: self.estimate_memory_usage(),
        }
    }

    // Helper methods (implementation details)
    fn compute_load_balance_score(&self) -> Result<f64> {
        let utilizations = &self.expert_statistics.expert_utilizations;
        let mean_util = utilizations.sum() / utilizations.len() as f64;
        let variance = utilizations
            .iter()
            .map(|&x| (x - mean_util).powi(2))
            .sum::<f64>()
            / utilizations.len() as f64;
        Ok(1.0 / (1.0 + variance))
    }

    fn compute_sparsity_achieved(&self) -> Result<f64> {
        // Compute average sparsity across recent routing decisions
        let recent_decisions = 10.min(self.quantum_router.routing_history.len());
        if recent_decisions == 0 {
            return Ok(0.0);
        }

        let total_sparsity = self
            .quantum_router
            .routing_history
            .iter()
            .rev()
            .take(recent_decisions)
            .map(|decision| {
                let active_experts = decision
                    .expert_weights
                    .iter()
                    .filter(|&&w| w > 1e-6)
                    .count();
                1.0 - (active_experts as f64 / self.config.num_experts as f64)
            })
            .sum::<f64>();

        Ok(total_sparsity / recent_decisions as f64)
    }

    fn estimate_quantum_advantage(&self) -> Result<f64> {
        let quantum_contribution = self.quantum_state_tracker.get_current_coherence()
            * self.entanglement_manager.get_utilization();
        Ok(1.0 + quantum_contribution * 2.0)
    }

    fn compute_load_balance_loss(&self, expert_weights: &Array1<f64>) -> Result<f64> {
        let ideal_weight = 1.0 / self.config.num_experts as f64;
        let balance_loss = expert_weights
            .iter()
            .map(|&w| (w - ideal_weight).powi(2))
            .sum::<f64>();
        Ok(balance_loss)
    }

    fn compute_sparsity_loss(&self, expert_weights: &Array1<f64>) -> Result<f64> {
        let target_sparsity = self.config.sparsity_config.target_sparsity;
        let current_sparsity = 1.0
            - expert_weights.iter().filter(|&&w| w > 1e-6).count() as f64
                / expert_weights.len() as f64;
        Ok((current_sparsity - target_sparsity).powi(2))
    }

    fn update_training_strategies(&mut self, metrics: &MoETrainingMetrics) -> Result<()> {
        // Adaptive learning rate adjustment
        if metrics.routing_efficiency < 0.7 {
            self.routing_optimizer.learning_rate *= 1.1;
        } else if metrics.routing_efficiency > 0.9 {
            self.routing_optimizer.learning_rate *= 0.95;
        }

        // Sparsity adjustment
        if metrics.sparsity_achieved < self.config.sparsity_config.target_sparsity {
            // Increase sparsity pressure
        }

        Ok(())
    }

    fn optimize_quantum_parameters(&mut self, metrics: &MoETrainingMetrics) -> Result<()> {
        // Optimize entanglement parameters
        if metrics.entanglement_utilization < 0.5 {
            self.entanglement_manager.increase_entanglement_strength()?;
        }

        // Optimize coherence preservation
        if metrics.quantum_coherence < 0.8 {
            self.quantum_state_tracker
                .enhance_coherence_preservation()?;
        }

        Ok(())
    }

    fn update_quantum_parameters_from_loss(
        &mut self,
        output: &MoEOutput,
        target: &Array1<f64>,
    ) -> Result<()> {
        // Placeholder for quantum parameter updates based on loss
        Ok(())
    }

    fn analyze_convergence(&self, losses: &[f64]) -> Result<ConvergenceAnalysis> {
        if losses.len() < 10 {
            return Ok(ConvergenceAnalysis::default());
        }

        let recent_losses = &losses[losses.len() - 10..];
        let early_losses = &losses[0..10];

        let recent_avg = recent_losses.iter().sum::<f64>() / recent_losses.len() as f64;
        let early_avg = early_losses.iter().sum::<f64>() / early_losses.len() as f64;

        let convergence_rate = (early_avg - recent_avg) / early_avg;

        let variance = recent_losses
            .iter()
            .map(|&x| (x - recent_avg).powi(2))
            .sum::<f64>()
            / recent_losses.len() as f64;

        Ok(ConvergenceAnalysis {
            convergence_rate,
            is_converged: variance < 1e-6,
            final_loss: recent_avg,
            loss_variance: variance,
        })
    }

    fn compute_routing_efficiency(&self) -> f64 {
        if self.quantum_router.routing_history.is_empty() {
            return 0.0;
        }

        let recent_efficiency = self
            .quantum_router
            .routing_history
            .iter()
            .rev()
            .take(10)
            .map(|decision| decision.routing_confidence)
            .sum::<f64>()
            / 10.0_f64.min(self.quantum_router.routing_history.len() as f64);

        recent_efficiency
    }

    fn count_total_parameters(&self) -> usize {
        let mut total = 0;

        // Count expert parameters
        for expert in &self.experts {
            total += expert.quantum_parameters.len();
            total += expert.classical_parameters.len();
        }

        // Count routing parameters
        total += self.quantum_router.routing_parameters.len();

        // Count gating parameters
        total += self.quantum_gate_network.gate_parameters.len();

        total
    }

    fn estimate_memory_usage(&self) -> usize {
        // Simplified memory usage estimation
        let expert_memory = self.experts.len() * 1000; // Placeholder
        let routing_memory = self.quantum_router.routing_parameters.len() * 8;
        let state_memory = self.quantum_state_tracker.state_history.len() * 100;

        expert_memory + routing_memory + state_memory
    }
}

// Component implementations (abbreviated for space)

impl QuantumExpert {
    pub fn new(expert_id: usize, config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            expert_id,
            architecture: config.expert_architecture.clone(),
            quantum_parameters: Array1::zeros(config.num_qubits * 3),
            classical_parameters: Array2::zeros((64, 64)), // Default size
            specialization: None,
            capacity: config.expert_capacity,
            current_load: 0,
            performance_history: Vec::new(),
            quantum_state: QuantumExpertState {
                quantum_amplitudes: Array1::<Complex64>::ones(
                    2_usize.pow(config.num_qubits as u32),
                )
                .mapv(|_| Complex64::new(1.0, 0.0)),
                entanglement_connections: Vec::new(),
                coherence_time: 1.0,
                fidelity: 1.0,
                quantum_volume: config.num_qubits as f64,
            },
        })
    }

    pub fn process(
        &mut self,
        input: &Array1<f64>,
        weight: f64,
        config: &QuantumMixtureOfExpertsConfig,
    ) -> Result<ExpertOutput> {
        // Transform input to output dimension using quantum transformation
        let prediction = if config.output_dim != input.len() {
            // Simple linear transformation for dimension matching
            let mut output = Array1::zeros(config.output_dim);
            for i in 0..config.output_dim {
                let input_idx = i % input.len();
                output[i] = input[input_idx] * (1.0 + self.expert_id as f64 * 0.1);
                // Expert-specific transformation
            }
            output
        } else {
            input.clone()
        };
        let quality_score = 0.8; // Placeholder

        self.current_load += 1;
        self.performance_history.push(quality_score);

        Ok(ExpertOutput {
            prediction,
            quality_score,
            confidence: weight,
            quantum_metrics: ExpertQuantumMetrics {
                coherence: self.quantum_state.coherence_time,
                entanglement: 0.5,
                fidelity: self.quantum_state.fidelity,
                quantum_volume: self.quantum_state.quantum_volume,
            },
        })
    }

    pub fn update_quantum_state(&mut self, weight: f64, efficiency: f64) -> Result<()> {
        // Update quantum state based on usage
        self.quantum_state.coherence_time *= 0.99; // Gradual decoherence
        self.quantum_state.fidelity = (self.quantum_state.fidelity + efficiency * weight) / 2.0;
        Ok(())
    }
}

impl QuantumRouter {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            routing_strategy: config.routing_strategy.clone(),
            routing_network: QuantumRoutingNetwork::new(config)?,
            routing_parameters: Array1::zeros(config.num_experts * 10),
            routing_history: Vec::new(),
            quantum_routing_state: QuantumRoutingState {
                routing_amplitudes: Array1::<Complex64>::ones(config.num_experts)
                    .mapv(|_| Complex64::new(1.0, 0.0)),
                routing_entanglement: 0.0,
                routing_coherence: 1.0,
                routing_fidelity: 1.0,
            },
            num_experts: config.num_experts,
        })
    }

    pub fn route(&mut self, input: &Array1<f64>) -> Result<RoutingResult> {
        // Simplified routing implementation
        let num_experts = self.num_experts;
        let mut expert_weights = Array1::zeros(num_experts);

        // Simple softmax routing
        for i in 0..num_experts {
            expert_weights[i] = (input[i % input.len()]).exp();
        }
        let sum_weights = expert_weights.sum();
        if sum_weights > 0.0 {
            expert_weights = expert_weights / sum_weights;
        }

        let routing_decision = RoutingDecision {
            decision_id: self.routing_history.len(),
            expert_weights: expert_weights.clone(),
            routing_confidence: 0.8,
            quantum_coherence: self.quantum_routing_state.routing_coherence,
            entanglement_measure: self.quantum_routing_state.routing_entanglement,
            decision_quality: 0.8,
        };

        self.routing_history.push(routing_decision.clone());

        Ok(RoutingResult {
            expert_weights: expert_weights.clone(),
            routing_confidence: 0.8,
            quantum_coherence: self.quantum_routing_state.routing_coherence,
            routing_entropy: self.compute_routing_entropy(&expert_weights)?,
        })
    }

    fn compute_routing_entropy(&self, weights: &Array1<f64>) -> Result<f64> {
        let entropy = -weights
            .iter()
            .filter(|&&w| w > 1e-10)
            .map(|&w| w * w.ln())
            .sum::<f64>();
        Ok(entropy)
    }
}

impl QuantumRoutingNetwork {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            routing_layers: Vec::new(),
            attention_mechanisms: Vec::new(),
            entanglement_couplings: Vec::new(),
        })
    }
}

impl QuantumGateNetwork {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            gating_mechanism: config.gating_mechanism.clone(),
            gate_parameters: Array1::zeros(config.num_experts),
            gating_history: Vec::new(),
            quantum_gate_state: QuantumGateState {
                gate_amplitudes: Array1::<Complex64>::ones(config.num_experts)
                    .mapv(|_| Complex64::new(1.0, 0.0)),
                gate_entanglement: 0.0,
                gate_coherence: 1.0,
            },
        })
    }

    pub fn gate(&mut self, routing_result: &RoutingResult) -> Result<GatingResult> {
        // Apply gating mechanism to routing weights
        let gated_weights = match &self.gating_mechanism {
            QuantumGatingMechanism::SuperpositionGating {
                coherence_preservation,
            } => routing_result
                .expert_weights
                .mapv(|w| w * coherence_preservation),
            _ => routing_result.expert_weights.clone(),
        };

        let gating_decision = GatingDecision {
            gate_weights: gated_weights.clone(),
            gate_confidence: routing_result.routing_confidence,
            sparsity_level: self.compute_sparsity(&gated_weights)?,
            quantum_efficiency: 0.9,
        };

        self.gating_history.push(gating_decision.clone());

        Ok(GatingResult {
            expert_weights: gated_weights,
            sparsity_achieved: gating_decision.sparsity_level,
            quantum_efficiency: gating_decision.quantum_efficiency,
        })
    }

    fn compute_sparsity(&self, weights: &Array1<f64>) -> Result<f64> {
        let active_count = weights.iter().filter(|&&w| w > 1e-6).count();
        Ok(1.0 - active_count as f64 / weights.len() as f64)
    }
}

impl LoadBalancer {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            strategy: config.load_balancing.clone(),
            balancing_parameters: Array1::zeros(config.num_experts),
            load_history: Vec::new(),
            fairness_metrics: FairnessMetrics {
                gini_coefficient: 0.0,
                entropy_measure: 0.0,
                quantum_fairness: 0.0,
                balance_score: 1.0,
            },
        })
    }

    pub fn balance_loads(&mut self, weights: &Array1<f64>) -> Result<Array1<f64>> {
        match &self.strategy {
            LoadBalancingStrategy::Uniform => {
                // Apply uniform balancing
                let mean_weight = weights.sum() / weights.len() as f64;
                let balanced = weights.mapv(|w| 0.8 * w + 0.2 * mean_weight);
                Ok(balanced)
            }
            LoadBalancingStrategy::CapacityAware { capacity_factors } => {
                // Apply capacity-aware balancing
                let mut balanced = weights.clone();
                for i in 0..balanced.len() {
                    balanced[i] *= capacity_factors[i.min(capacity_factors.len() - 1)];
                }
                Ok(balanced)
            }
            _ => Ok(weights.clone()),
        }
    }

    pub fn adapt_strategy(&mut self, metrics: &MoETrainingMetrics) -> Result<()> {
        // Adapt balancing strategy based on performance
        if metrics.load_balance_score < 0.7 {
            // Increase balancing strength
        }
        Ok(())
    }
}

impl ExpertStatistics {
    pub fn new(num_experts: usize) -> Self {
        Self {
            expert_utilizations: Array1::zeros(num_experts),
            expert_performances: Array1::zeros(num_experts),
            expert_specializations: Array1::zeros(num_experts),
            expert_interactions: Array2::zeros((num_experts, num_experts)),
            quantum_correlations: Array2::zeros((num_experts, num_experts)),
        }
    }
}

impl PerformanceMonitor {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            performance_metrics: PerformanceMetrics {
                throughput: 0.0,
                latency: 0.0,
                accuracy: 0.0,
                expert_utilization: Array1::zeros(config.num_experts),
                quantum_efficiency: 0.0,
                resource_utilization: 0.0,
            },
            monitoring_config: MonitoringConfig {
                monitoring_frequency: 100,
                metrics_to_track: vec![MetricType::Performance, MetricType::ResourceUtilization],
                alert_thresholds: HashMap::new(),
            },
            alert_system: AlertSystem {
                alert_rules: Vec::new(),
                alert_history: Vec::new(),
            },
        })
    }

    pub fn update(&mut self, output: &CombinedOutput, weights: &Array1<f64>) -> Result<()> {
        // Update performance metrics
        self.performance_metrics.quantum_efficiency = output.quantum_metrics.coherence;
        self.performance_metrics.expert_utilization = weights.clone();
        Ok(())
    }
}

impl CapacityManager {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            total_capacity: config.num_experts * config.expert_capacity,
            available_capacity: config.num_experts * config.expert_capacity,
            capacity_allocation: Array1::ones(config.num_experts) / config.num_experts as f64,
            capacity_optimization: CapacityOptimization::DynamicAllocation,
        })
    }
}

impl RoutingOptimizer {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            optimizer_type: OptimizerType::Adam {
                beta1: 0.9,
                beta2: 0.999,
            },
            learning_rate: 0.001,
            optimization_history: Vec::new(),
            gradient_estimator: GradientEstimator::ParameterShift,
        })
    }

    pub fn update_routing_parameters(
        &mut self,
        routing_decision: &RoutingDecision,
        target: &Array1<f64>,
        learning_rate: f64,
    ) -> Result<()> {
        // Placeholder for routing parameter updates
        Ok(())
    }
}

impl ExpertOptimizer {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            optimizer_type: OptimizerType::Adam {
                beta1: 0.9,
                beta2: 0.999,
            },
            expert_learning_rates: Array1::ones(config.num_experts) * 0.001,
            expert_optimization_history: Vec::new(),
        })
    }

    pub fn update_expert_parameters(
        &mut self,
        experts: &[QuantumExpert],
        expert_outputs: &[ExpertOutput],
        expert_weights: &Array1<f64>,
        target: &Array1<f64>,
        learning_rate: f64,
    ) -> Result<()> {
        // Placeholder for expert parameter updates
        Ok(())
    }
}

impl QuantumStateTracker {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            state_history: Vec::new(),
            coherence_tracking: CoherenceTracker {
                coherence_history: Vec::new(),
                decoherence_rate: 0.01,
                coherence_preservation_strategies: Vec::new(),
            },
            entanglement_tracking: EntanglementTracker {
                entanglement_history: Vec::new(),
                entanglement_budget: 1.0,
                entanglement_efficiency: 1.0,
            },
            fidelity_tracking: FidelityTracker {
                fidelity_history: Vec::new(),
                target_fidelity: 0.95,
                fidelity_optimization: FidelityOptimization::DirectFidelityEstimation,
            },
        })
    }

    pub fn update_coherence(&mut self, coherence: f64) -> Result<()> {
        self.coherence_tracking.coherence_history.push(coherence);
        Ok(())
    }

    pub fn get_current_coherence(&self) -> f64 {
        self.coherence_tracking
            .coherence_history
            .last()
            .copied()
            .unwrap_or(1.0)
    }

    pub fn enhance_coherence_preservation(&mut self) -> Result<()> {
        // Implement coherence enhancement strategies
        Ok(())
    }
}

impl EntanglementManager {
    pub fn new(config: &QuantumMixtureOfExpertsConfig) -> Result<Self> {
        Ok(Self {
            entanglement_config: config.entanglement_config.clone(),
            entanglement_operations: Vec::new(),
            entanglement_scheduler: EntanglementScheduler {
                scheduling_strategy: SchedulingStrategy::AdaptiveScheduling,
                entanglement_budget: 1.0,
                operation_queue: Vec::new(),
            },
        })
    }

    pub fn update_entanglement(&mut self, expert_weights: &Array1<f64>) -> Result<()> {
        // Update entanglement based on expert interactions
        for i in 0..expert_weights.len() {
            for j in i + 1..expert_weights.len() {
                if expert_weights[i] * expert_weights[j] > 0.1 {
                    // Create entanglement between interacting experts
                    let operation = EntanglementOperation {
                        operation_type: EntanglementOperationType::CreateEntanglement,
                        target_experts: vec![i, j],
                        entanglement_strength: expert_weights[i] * expert_weights[j],
                        operation_fidelity: 0.95,
                    };
                    self.entanglement_operations.push(operation);
                }
            }
        }
        Ok(())
    }

    pub fn get_utilization(&self) -> f64 {
        if self.entanglement_operations.is_empty() {
            0.0
        } else {
            let avg_strength = self
                .entanglement_operations
                .iter()
                .map(|op| op.entanglement_strength)
                .sum::<f64>()
                / self.entanglement_operations.len() as f64;
            avg_strength
        }
    }

    pub fn increase_entanglement_strength(&mut self) -> Result<()> {
        // Increase entanglement strength across the system
        Ok(())
    }
}

// Output structures
#[derive(Debug, Clone)]
pub struct MoEOutput {
    pub output: Array1<f64>,
    pub expert_weights: Array1<f64>,
    pub routing_decision: RoutingResult,
    pub gating_decision: GatingResult,
    pub expert_outputs: Vec<ExpertOutput>,
    pub quantum_metrics: QuantumCombinationMetrics,
}

#[derive(Debug, Clone)]
pub struct RoutingResult {
    pub expert_weights: Array1<f64>,
    pub routing_confidence: f64,
    pub quantum_coherence: f64,
    pub routing_entropy: f64,
}

#[derive(Debug, Clone)]
pub struct GatingResult {
    pub expert_weights: Array1<f64>,
    pub sparsity_achieved: f64,
    pub quantum_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct ExpertOutput {
    pub prediction: Array1<f64>,
    pub quality_score: f64,
    pub confidence: f64,
    pub quantum_metrics: ExpertQuantumMetrics,
}

#[derive(Debug, Clone)]
pub struct ExpertQuantumMetrics {
    pub coherence: f64,
    pub entanglement: f64,
    pub fidelity: f64,
    pub quantum_volume: f64,
}

#[derive(Debug, Clone)]
pub struct CombinedOutput {
    pub prediction: Array1<f64>,
    pub quantum_metrics: QuantumCombinationMetrics,
}

#[derive(Debug, Clone, Default)]
pub struct QuantumCombinationMetrics {
    pub coherence: f64,
    pub entanglement: f64,
    pub fidelity: f64,
    pub quantum_volume: f64,
    pub interference_factor: f64,
}

impl QuantumCombinationMetrics {
    pub fn accumulate(&mut self, expert_metrics: &ExpertQuantumMetrics, weight: f64) {
        self.coherence += weight * expert_metrics.coherence;
        self.entanglement += weight * expert_metrics.entanglement;
        self.fidelity += weight * expert_metrics.fidelity;
        self.quantum_volume += weight * expert_metrics.quantum_volume;
    }

    pub fn finalize(&mut self, total_weight: f64) {
        if total_weight > 1e-10 {
            self.coherence /= total_weight;
            self.entanglement /= total_weight;
            self.fidelity /= total_weight;
            self.quantum_volume /= total_weight;
        }
    }
}

// Training structures
#[derive(Debug, Clone)]
pub struct MoETrainingConfig {
    pub epochs: usize,
    pub batch_size: usize,
    pub routing_learning_rate: f64,
    pub expert_learning_rate: f64,
    pub load_balance_weight: f64,
    pub sparsity_weight: f64,
    pub quantum_coherence_weight: f64,
    pub log_interval: usize,
}

impl Default for MoETrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 100,
            batch_size: 32,
            routing_learning_rate: 0.001,
            expert_learning_rate: 0.001,
            load_balance_weight: 0.01,
            sparsity_weight: 0.001,
            quantum_coherence_weight: 0.1,
            log_interval: 10,
        }
    }
}

#[derive(Debug, Clone)]
pub struct MoETrainingOutput {
    pub training_losses: Vec<f64>,
    pub routing_efficiency_history: Vec<f64>,
    pub quantum_metrics_history: Vec<QuantumMoEMetrics>,
    pub final_expert_statistics: ExpertStatistics,
    pub convergence_analysis: ConvergenceAnalysis,
}

#[derive(Debug, Clone)]
pub struct QuantumMoEMetrics {
    pub quantum_coherence: f64,
    pub entanglement_utilization: f64,
    pub quantum_advantage: f64,
    pub routing_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct MoEStatistics {
    pub expert_utilizations: Array1<f64>,
    pub expert_performances: Array1<f64>,
    pub load_balance_score: f64,
    pub routing_efficiency: f64,
    pub quantum_coherence: f64,
    pub entanglement_utilization: f64,
    pub total_parameters: usize,
    pub memory_usage: usize,
}

#[derive(Debug, Clone, Default)]
pub struct ConvergenceAnalysis {
    pub convergence_rate: f64,
    pub is_converged: bool,
    pub final_loss: f64,
    pub loss_variance: f64,
}

// Default implementations
impl Default for QuantumMixtureOfExpertsConfig {
    fn default() -> Self {
        Self {
            input_dim: 64,
            output_dim: 32,
            num_experts: 8,
            num_qubits: 6,
            expert_capacity: 100,
            routing_strategy: QuantumRoutingStrategy::QuantumSuperposition {
                superposition_strength: 0.8,
                interference_pattern: InterferencePattern::Constructive,
            },
            expert_architecture: ExpertArchitecture::FeedForward {
                hidden_layers: vec![128, 64],
                activation: ActivationFunction::ReLU,
            },
            gating_mechanism: QuantumGatingMechanism::SuperpositionGating {
                coherence_preservation: 0.9,
            },
            load_balancing: LoadBalancingStrategy::Uniform,
            sparsity_config: SparsityConfig {
                target_sparsity: 0.7,
                sparsity_method: SparsityMethod::TopK { k: 3 },
                sparsity_schedule: SparsitySchedule::Constant,
                quantum_sparsity_enhancement: 0.1,
            },
            entanglement_config: EntanglementConfig {
                enable_expert_entanglement: true,
                entanglement_strength: 0.5,
                entanglement_decay: 0.01,
                entanglement_restoration: 0.1,
                max_entanglement_range: 4,
                entanglement_pattern: EntanglementPattern::Circular,
            },
            quantum_enhancement_level: 0.6,
            enable_hierarchical_experts: false,
            enable_dynamic_experts: true,
            enable_quantum_communication: true,
        }
    }
}

impl Default for ExpertOutput {
    fn default() -> Self {
        Self {
            prediction: Array1::zeros(1),
            quality_score: 0.5,
            confidence: 0.5,
            quantum_metrics: ExpertQuantumMetrics::default(),
        }
    }
}

impl Default for ExpertQuantumMetrics {
    fn default() -> Self {
        Self {
            coherence: 1.0,
            entanglement: 0.0,
            fidelity: 1.0,
            quantum_volume: 0.0,
        }
    }
}

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

    #[test]
    fn test_quantum_mixture_of_experts_creation() {
        let config = QuantumMixtureOfExpertsConfig::default();
        let moe = QuantumMixtureOfExperts::new(config);
        assert!(moe.is_ok());
    }

    #[test]
    fn test_expert_creation() {
        let config = QuantumMixtureOfExpertsConfig::default();
        let expert = QuantumExpert::new(0, &config);
        assert!(expert.is_ok());
    }

    #[test]
    fn test_quantum_routing() {
        let config = QuantumMixtureOfExpertsConfig::default();
        let mut router = QuantumRouter::new(&config).unwrap();
        let input = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);

        let result = router.route(&input);
        assert!(result.is_ok());

        let routing_result = result.unwrap();
        assert_eq!(routing_result.expert_weights.len(), 8);
        assert!(routing_result.routing_confidence >= 0.0);
        assert!(routing_result.routing_confidence <= 1.0);
    }

    #[test]
    fn test_forward_pass() {
        let config = QuantumMixtureOfExpertsConfig {
            input_dim: 4,
            output_dim: 2,
            num_experts: 3,
            ..Default::default()
        };
        let mut moe = QuantumMixtureOfExperts::new(config).unwrap();
        let input = Array1::from_vec(vec![0.1, 0.2, 0.3, 0.4]);

        let result = moe.forward(&input);
        assert!(result.is_ok());

        let output = result.unwrap();
        assert_eq!(output.expert_weights.len(), 3);
        assert!(output.routing_decision.routing_confidence >= 0.0);
    }

    #[test]
    fn test_load_balancing() {
        let config = QuantumMixtureOfExpertsConfig {
            load_balancing: LoadBalancingStrategy::Uniform,
            ..Default::default()
        };
        let mut balancer = LoadBalancer::new(&config).unwrap();
        let weights = Array1::from_vec(vec![0.8, 0.1, 0.1]);

        let balanced = balancer.balance_loads(&weights);
        assert!(balanced.is_ok());

        let balanced_weights = balanced.unwrap();
        assert_eq!(balanced_weights.len(), 3);
    }

    #[test]
    fn test_sparsity_computation() {
        let config = QuantumMixtureOfExpertsConfig::default();
        let gate_network = QuantumGateNetwork::new(&config).unwrap();
        let weights = Array1::from_vec(vec![0.8, 0.0, 0.2, 0.0]);

        let sparsity = gate_network.compute_sparsity(&weights);
        assert!(sparsity.is_ok());
        assert_eq!(sparsity.unwrap(), 0.5); // 2 out of 4 are zero
    }

    #[test]
    fn test_quantum_interference() {
        let config = QuantumMixtureOfExpertsConfig {
            routing_strategy: QuantumRoutingStrategy::QuantumSuperposition {
                superposition_strength: 0.8,
                interference_pattern: InterferencePattern::Constructive,
            },
            ..Default::default()
        };
        let moe = QuantumMixtureOfExperts::new(config).unwrap();
        let weights = Array1::from_vec(vec![0.5, 0.3, 0.2]);

        let interference = moe.compute_interference_factor(0, &weights);
        assert!(interference.is_ok());
        assert!(interference.unwrap() > 0.0);
    }

    #[test]
    fn test_entanglement_management() {
        let config = QuantumMixtureOfExpertsConfig {
            entanglement_config: EntanglementConfig {
                enable_expert_entanglement: true,
                entanglement_strength: 0.7,
                ..Default::default()
            },
            ..Default::default()
        };
        let mut manager = EntanglementManager::new(&config).unwrap();
        let expert_weights = Array1::from_vec(vec![0.4, 0.6, 0.0]);

        let result = manager.update_entanglement(&expert_weights);
        assert!(result.is_ok());

        let utilization = manager.get_utilization();
        assert!(utilization >= 0.0);
    }

    #[test]
    fn test_expert_specialization() {
        let config = QuantumMixtureOfExpertsConfig {
            expert_architecture: ExpertArchitecture::SpecializedExperts {
                expert_specializations: vec![
                    ExpertSpecialization::TextProcessing,
                    ExpertSpecialization::ImageProcessing,
                ],
                specialization_strength: 0.8,
            },
            ..Default::default()
        };

        let moe = QuantumMixtureOfExperts::new(config);
        assert!(moe.is_ok());
    }

    #[test]
    fn test_hierarchical_routing() {
        let config = QuantumMixtureOfExpertsConfig {
            routing_strategy: QuantumRoutingStrategy::HierarchicalRouting {
                hierarchy_levels: 2,
                routing_per_level: RoutingType::Quantum,
            },
            ..Default::default()
        };

        let moe = QuantumMixtureOfExperts::new(config);
        assert!(moe.is_ok());
    }
}