quantrs2_ml/

quantum_implicit_neural_representations.rs

//! Quantum Implicit Neural Representations
//!
//! This module implements cutting-edge Quantum Implicit Neural Representations (QINRs) that leverage
//! quantum computing principles to represent continuous signals with unprecedented efficiency and quality.
//! Unlike classical INRs, QINRs can achieve exponential compression ratios and superior approximation
//! capabilities through quantum superposition and entanglement.
//!
//! Key Features:
//! - Quantum coordinate networks with quantum positional encoding
//! - Quantum SIREN with quantum sinusoidal activations
//! - Multi-modal quantum neural fields (images, audio, 3D shapes, video)
//! - Quantum meta-learning for rapid adaptation to new signals
//! - Quantum compression with exponential storage savings
//! - Advanced quantum optimization techniques

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2, Array3, Array4, ArrayView1, Axis};
use scirs2_core::random::ChaCha20Rng;
use scirs2_core::random::{Rng, SeedableRng};
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::f64::consts::PI;

/// Configuration for Quantum Implicit Neural Representations
#[derive(Debug, Clone)]
pub struct QuantumINRConfig {
    pub signal_type: SignalType,
    pub coordinate_dim: usize,
    pub output_dim: usize,
    pub num_qubits: usize,
    pub network_depth: usize,
    pub hidden_dim: usize,
    pub quantum_enhancement_level: f64,
    pub representation_method: RepresentationMethod,
    pub positional_encoding: QuantumPositionalEncoding,
    pub activation_config: QuantumActivationConfig,
    pub compression_config: CompressionConfig,
    pub meta_learning_config: MetaLearningConfig,
    pub optimization_config: OptimizationConfig,
}

#[derive(Debug, Clone)]
pub enum SignalType {
    Image2D {
        height: usize,
        width: usize,
        channels: usize,
    },
    Image3D {
        depth: usize,
        height: usize,
        width: usize,
        channels: usize,
    },
    Audio {
        sample_rate: usize,
        channels: usize,
    },
    Video {
        frames: usize,
        height: usize,
        width: usize,
        channels: usize,
    },
    Shape3D {
        vertices: usize,
        faces: usize,
    },
    VolumetricData {
        resolution: Vec<usize>,
    },
    SignedDistanceField {
        bounds: Array2<f64>,
    },
    Occupancy {
        resolution: Vec<usize>,
    },
    Radiance {
        viewing_directions: bool,
    },
    LightField {
        angular_resolution: usize,
    },
    CustomSignal {
        input_dim: usize,
        output_dim: usize,
    },
}

#[derive(Debug, Clone)]
pub enum RepresentationMethod {
    /// Quantum Coordinate Networks with quantum MLP layers
    QuantumCoordinateNetwork {
        layer_config: QuantumLayerConfig,
        skip_connections: Vec<usize>,
    },

    /// Quantum SIREN with quantum sinusoidal activations
    QuantumSIREN {
        omega_0: f64,
        omega_hidden: f64,
        quantum_frequency_modulation: bool,
    },

    /// Quantum Neural Radiance Fields for 3D scene representation
    QuantumNeRF {
        position_encoding_levels: usize,
        direction_encoding_levels: usize,
        density_activation: QuantumActivation,
        color_activation: QuantumActivation,
    },

    /// Quantum Hash Encoding for high-frequency details
    QuantumHashEncoding {
        hash_table_size: usize,
        levels: usize,
        quantum_hash_function: QuantumHashFunction,
    },

    /// Quantum Fourier Features for periodic signals
    QuantumFourierFeatures {
        num_frequencies: usize,
        frequency_scale: f64,
        quantum_fourier_basis: bool,
    },

    /// Quantum Multi-Resolution Networks
    QuantumMultiRes {
        resolution_levels: Vec<usize>,
        level_weights: Array1<f64>,
        quantum_level_fusion: bool,
    },

    /// Quantum Compositional Networks for structured signals
    QuantumCompositional {
        component_networks: Vec<ComponentNetwork>,
        composition_strategy: CompositionStrategy,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumLayerConfig {
    pub layer_type: QuantumLayerType,
    pub normalization: Option<QuantumNormalization>,
    pub dropout_rate: f64,
    pub quantum_gate_sequence: Vec<QuantumGateType>,
}

#[derive(Debug, Clone)]
pub enum QuantumLayerType {
    QuantumLinear {
        input_dim: usize,
        output_dim: usize,
        quantum_weight_encoding: WeightEncodingType,
    },
    QuantumConvolutional {
        in_channels: usize,
        out_channels: usize,
        kernel_size: usize,
        quantum_convolution_type: QuantumConvolutionType,
    },
    QuantumAttention {
        num_heads: usize,
        head_dim: usize,
        attention_mechanism: QuantumAttentionMechanism,
    },
    QuantumResidual {
        inner_layers: Vec<Box<QuantumLayerConfig>>,
    },
}

#[derive(Debug, Clone)]
pub enum WeightEncodingType {
    AmplitudeEncoding,
    AngleEncoding,
    BasisEncoding,
    QuantumSuperposition,
}

#[derive(Debug, Clone)]
pub enum QuantumConvolutionType {
    StandardQuantum,
    QuantumDepthwise,
    QuantumSeparable,
    EntanglementConvolution,
}

#[derive(Debug, Clone)]
pub enum QuantumAttentionMechanism {
    QuantumSelfAttention,
    QuantumCrossAttention,
    EntanglementAttention,
    QuantumFourierAttention,
}

#[derive(Debug, Clone)]
pub enum QuantumHashFunction {
    QuantumUniversalHash,
    EntanglementHash,
    QuantumLocalitySensitiveHash,
    PhaseBasedHash,
}

#[derive(Debug, Clone)]
pub struct ComponentNetwork {
    pub network_id: usize,
    pub specialization: NetworkSpecialization,
    pub architecture: RepresentationMethod,
    pub weight: f64,
}

#[derive(Debug, Clone)]
pub enum NetworkSpecialization {
    LowFrequency,
    HighFrequency,
    EdgeFeatures,
    TextureFeatures,
    GeometricFeatures,
    TemporalFeatures,
    SpatialFeatures,
}

#[derive(Debug, Clone)]
pub enum CompositionStrategy {
    WeightedSum,
    QuantumSuperposition,
    EntanglementCombination,
    AttentionWeighted,
    HierarchicalComposition,
}

#[derive(Debug, Clone)]
pub struct QuantumPositionalEncoding {
    pub encoding_type: PositionalEncodingType,
    pub num_frequencies: usize,
    pub frequency_scale: f64,
    pub quantum_enhancement: bool,
    pub learnable_frequencies: bool,
}

#[derive(Debug, Clone)]
pub enum PositionalEncodingType {
    /// Standard sinusoidal encoding with quantum enhancement
    QuantumSinusoidal {
        base_frequency: f64,
        frequency_progression: FrequencyProgression,
    },

    /// Quantum Fourier features
    QuantumFourier {
        bandwidth: f64,
        random_features: bool,
    },

    /// Hash-based encoding with quantum hash functions
    QuantumHash {
        hash_table_size: usize,
        collision_resolution: CollisionResolution,
    },

    /// Learnable quantum embedding
    QuantumLearnable {
        embedding_dim: usize,
        initialization_strategy: InitializationStrategy,
    },

    /// Spherical harmonics encoding for 3D data
    QuantumSphericalHarmonics {
        max_degree: usize,
        quantum_coefficients: bool,
    },

    /// Multi-scale encoding
    QuantumMultiScale {
        scale_levels: Vec<f64>,
        scale_weights: Array1<f64>,
    },
}

#[derive(Debug, Clone)]
pub enum FrequencyProgression {
    Logarithmic,
    Linear,
    Exponential,
    Adaptive,
}

#[derive(Debug, Clone)]
pub enum CollisionResolution {
    Chaining,
    OpenAddressing,
    QuantumSuperposition,
}

#[derive(Debug, Clone)]
pub enum InitializationStrategy {
    Xavier,
    Kaiming,
    QuantumRandom,
    EntanglementBased,
}

#[derive(Debug, Clone)]
pub struct QuantumActivationConfig {
    pub activation_type: QuantumActivation,
    pub frequency_modulation: bool,
    pub phase_modulation: bool,
    pub amplitude_control: bool,
    pub quantum_nonlinearity_strength: f64,
}

#[derive(Debug, Clone)]
pub enum QuantumActivation {
    /// Quantum SIREN activation with learnable frequencies
    QuantumSiren { omega: f64 },

    /// Quantum ReLU with quantum gates
    QuantumReLU { threshold: f64 },

    /// Quantum Gaussian activation
    QuantumGaussian { sigma: f64 },

    /// Quantum sinusoidal activation
    QuantumSin { frequency: f64, phase: f64 },

    /// Quantum polynomial activation
    QuantumPolynomial {
        degree: usize,
        coefficients: Array1<f64>,
    },

    /// Quantum exponential linear unit
    QuantumELU { alpha: f64 },

    /// Quantum swish activation
    QuantumSwish { beta: f64 },

    /// Entanglement-based activation
    EntanglementActivation { entanglement_strength: f64 },

    /// Phase-based activation
    PhaseActivation { phase_range: f64 },

    /// Superposition activation
    SuperpositionActivation {
        component_activations: Vec<QuantumActivation>,
        weights: Array1<f64>,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumNormalization {
    QuantumBatchNorm,
    QuantumLayerNorm,
    QuantumInstanceNorm,
    QuantumGroupNorm { num_groups: usize },
    EntanglementNorm,
    PhaseNorm,
}

#[derive(Debug, Clone)]
pub enum QuantumGateType {
    RotationX { angle: f64 },
    RotationY { angle: f64 },
    RotationZ { angle: f64 },
    Hadamard,
    CNOT { control: usize, target: usize },
    CZ { control: usize, target: usize },
    Toffoli { controls: Vec<usize>, target: usize },
    Phase { angle: f64 },
    Amplitude { amplitude: Complex64 },
    Custom { matrix: Array2<Complex64> },
}

#[derive(Debug, Clone)]
pub struct CompressionConfig {
    pub compression_method: CompressionMethod,
    pub target_compression_ratio: f64,
    pub quality_preservation: f64,
    pub quantum_compression_enhancement: f64,
    pub adaptive_compression: bool,
}

#[derive(Debug, Clone)]
pub enum CompressionMethod {
    /// Quantum weight pruning
    QuantumPruning {
        sparsity_target: f64,
        pruning_strategy: PruningStrategy,
    },

    /// Quantum quantization
    QuantumQuantization {
        bit_width: usize,
        quantization_scheme: QuantizationScheme,
    },

    /// Quantum low-rank decomposition
    QuantumLowRank {
        rank_reduction_factor: f64,
        decomposition_method: DecompositionMethod,
    },

    /// Quantum knowledge distillation
    QuantumDistillation {
        teacher_config: Box<QuantumINRConfig>,
        distillation_temperature: f64,
    },

    /// Quantum neural architecture search for compression
    QuantumNAS {
        search_space: SearchSpace,
        efficiency_objective: EfficiencyObjective,
    },
}

#[derive(Debug, Clone)]
pub enum PruningStrategy {
    MagnitudeBased,
    GradientBased,
    QuantumEntanglement,
    QuantumCoherence,
}

#[derive(Debug, Clone)]
pub enum QuantizationScheme {
    Uniform,
    NonUniform,
    QuantumStates,
    AdaptiveQuantum,
}

#[derive(Debug, Clone)]
pub enum DecompositionMethod {
    SVD,
    QR,
    QuantumSingularValueDecomposition,
    TensorDecomposition,
}

#[derive(Debug, Clone)]
pub struct SearchSpace {
    pub layer_depths: Vec<usize>,
    pub hidden_dimensions: Vec<usize>,
    pub activation_functions: Vec<QuantumActivation>,
    pub quantum_gate_sequences: Vec<Vec<QuantumGateType>>,
}

#[derive(Debug, Clone)]
pub enum EfficiencyObjective {
    MinimizeParameters,
    MinimizeLatency,
    MinimizeMemory,
    MaximizeCompressionRatio,
    BalancedEfficiency,
}

#[derive(Debug, Clone)]
pub struct MetaLearningConfig {
    pub meta_learning_method: MetaLearningMethod,
    pub adaptation_steps: usize,
    pub meta_learning_rate: f64,
    pub inner_learning_rate: f64,
    pub quantum_meta_enhancement: f64,
}

#[derive(Debug, Clone)]
pub enum MetaLearningMethod {
    /// Model-Agnostic Meta-Learning with quantum enhancement
    QuantumMAML {
        first_order: bool,
        quantum_gradient_estimation: bool,
    },

    /// Quantum Reptile algorithm
    QuantumReptile {
        reptile_step_size: f64,
        quantum_interpolation: bool,
    },

    /// Quantum hypernetwork-based meta-learning
    QuantumHyperNetwork {
        hypernetwork_architecture: HyperNetworkArchitecture,
        context_encoding: ContextEncoding,
    },

    /// Quantum gradient-based meta-learning
    QuantumGradientBased {
        gradient_steps: usize,
        learned_loss: bool,
    },

    /// Quantum memory-augmented meta-learning
    QuantumMemoryAugmented {
        memory_size: usize,
        memory_update_rule: MemoryUpdateRule,
    },
}

#[derive(Debug, Clone)]
pub struct HyperNetworkArchitecture {
    pub encoder_layers: Vec<usize>,
    pub decoder_layers: Vec<usize>,
    pub quantum_context_processing: bool,
}

#[derive(Debug, Clone)]
pub enum ContextEncoding {
    Direct,
    Attention,
    QuantumEmbedding,
    HierarchicalEncoding,
}

#[derive(Debug, Clone)]
pub enum MemoryUpdateRule {
    LSTM,
    GRU,
    QuantumMemory,
    AttentionBased,
}

#[derive(Debug, Clone)]
pub struct OptimizationConfig {
    pub optimizer_type: QuantumOptimizerType,
    pub learning_rate_schedule: LearningRateSchedule,
    pub gradient_estimation: GradientEstimation,
    pub regularization: RegularizationConfig,
    pub convergence_criteria: ConvergenceCriteria,
}

#[derive(Debug, Clone)]
pub enum QuantumOptimizerType {
    /// Quantum Adam optimizer
    QuantumAdam {
        beta1: f64,
        beta2: f64,
        epsilon: f64,
        quantum_momentum: bool,
    },

    /// Quantum natural gradient
    QuantumNaturalGradient {
        damping_parameter: f64,
        quantum_fisher_information: bool,
    },

    /// Parameter-shift rule optimizer
    ParameterShiftRule {
        shift_value: f64,
        second_order: bool,
    },

    /// Quantum annealing optimizer
    QuantumAnnealing {
        temperature_schedule: Array1<f64>,
        annealing_steps: usize,
    },

    /// Quantum evolutionary strategy
    QuantumEvolutionStrategy {
        population_size: usize,
        mutation_strength: f64,
        quantum_selection: bool,
    },
}

#[derive(Debug, Clone)]
pub enum LearningRateSchedule {
    Constant {
        rate: f64,
    },
    Exponential {
        initial_rate: f64,
        decay_rate: f64,
    },
    Cosine {
        max_rate: f64,
        min_rate: f64,
        period: usize,
    },
    Adaptive {
        adaptation_strategy: AdaptationStrategy,
    },
    QuantumAdaptive {
        quantum_feedback: bool,
    },
}

#[derive(Debug, Clone)]
pub enum AdaptationStrategy {
    LossBasedAdaptation,
    GradientBasedAdaptation,
    QuantumStateAdaptation,
    EntanglementBasedAdaptation,
}

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

#[derive(Debug, Clone)]
pub struct RegularizationConfig {
    pub weight_decay: f64,
    pub spectral_normalization: bool,
    pub quantum_regularization: QuantumRegularization,
    pub smoothness_regularization: f64,
}

#[derive(Debug, Clone)]
pub enum QuantumRegularization {
    EntanglementRegularization { strength: f64 },
    CoherenceRegularization { strength: f64 },
    QuantumVolumeRegularization { strength: f64 },
    FidelityRegularization { target_fidelity: f64 },
}

#[derive(Debug, Clone)]
pub struct ConvergenceCriteria {
    pub max_iterations: usize,
    pub tolerance: f64,
    pub patience: usize,
    pub quantum_convergence_metric: QuantumConvergenceMetric,
}

#[derive(Debug, Clone)]
pub enum QuantumConvergenceMetric {
    LossConvergence,
    GradientNorm,
    ParameterChange,
    QuantumFidelity,
    EntanglementMeasure,
}

/// Main Quantum Implicit Neural Representation model
pub struct QuantumImplicitNeuralRepresentation {
    config: QuantumINRConfig,

    // Core network components
    coordinate_network: QuantumCoordinateNetwork,
    positional_encoder: QuantumPositionalEncoder,

    // Quantum components
    quantum_layers: Vec<QuantumLayer>,
    quantum_state_manager: QuantumStateManager,
    entanglement_manager: EntanglementManager,

    // Meta-learning components
    meta_learner: Option<QuantumMetaLearner>,
    adaptation_parameters: AdaptationParameters,

    // Optimization components
    optimizer: QuantumOptimizer,
    gradient_estimator: QuantumGradientEstimator,

    // Training state
    training_history: Vec<INRTrainingMetrics>,
    quantum_metrics: QuantumINRMetrics,

    // Compression state
    compression_manager: CompressionManager,
    compressed_representation: Option<CompressedRepresentation>,
}

#[derive(Debug, Clone)]
pub struct QuantumCoordinateNetwork {
    layers: Vec<QuantumLayer>,
    skip_connections: Vec<SkipConnection>,
    output_activation: Option<QuantumActivation>,
    quantum_parameters: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct QuantumLayer {
    layer_id: usize,
    layer_type: QuantumLayerType,
    quantum_weights: Array2<Complex64>,
    classical_weights: Array2<f64>,
    bias: Array1<f64>,
    activation: QuantumActivation,
    normalization: Option<QuantumNormalization>,
    quantum_gates: Vec<QuantumGate>,
    entanglement_pattern: EntanglementPattern,
}

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

#[derive(Debug, Clone)]
pub enum EntanglementPattern {
    Linear,
    Circular,
    AllToAll,
    Custom { connectivity_matrix: Array2<bool> },
    Adaptive { adaptation_rule: AdaptationRule },
}

#[derive(Debug, Clone)]
pub enum AdaptationRule {
    GradientBased,
    PerformanceBased,
    QuantumStateDependent,
}

#[derive(Debug, Clone)]
pub struct SkipConnection {
    from_layer: usize,
    to_layer: usize,
    connection_type: ConnectionType,
    weight: f64,
}

#[derive(Debug, Clone)]
pub enum ConnectionType {
    Additive,
    Concatenative,
    Multiplicative,
    QuantumSuperposition,
    EntanglementBased,
}

#[derive(Debug, Clone)]
pub struct QuantumPositionalEncoder {
    encoding_config: QuantumPositionalEncoding,
    frequency_parameters: Array2<f64>,
    quantum_frequencies: Array2<Complex64>,
    phase_offsets: Array1<f64>,
    learnable_parameters: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct QuantumStateManager {
    quantum_states: Vec<QuantumSystemState>,
    coherence_tracker: CoherenceTracker,
    decoherence_model: DecoherenceModel,
    state_evolution_history: Vec<StateEvolution>,
}

#[derive(Debug, Clone)]
pub struct QuantumSystemState {
    amplitudes: Array1<Complex64>,
    phases: Array1<f64>,
    entanglement_measure: f64,
    coherence_time: f64,
    fidelity: f64,
    quantum_volume: f64,
}

#[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,
    AdaptiveCorrection,
}

#[derive(Debug, Clone)]
pub struct DecoherenceModel {
    t1_time: f64,
    t2_time: f64,
    gate_error_rate: f64,
    measurement_error_rate: f64,
    environmental_coupling: f64,
}

#[derive(Debug, Clone)]
pub struct StateEvolution {
    timestamp: usize,
    initial_state: QuantumSystemState,
    final_state: QuantumSystemState,
    evolution_operator: Array2<Complex64>,
    fidelity_loss: f64,
}

#[derive(Debug, Clone)]
pub struct EntanglementManager {
    entanglement_map: Array2<f64>,
    entanglement_operations: Vec<EntanglementOperation>,
    entanglement_budget: f64,
    entanglement_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct EntanglementOperation {
    operation_type: EntanglementOperationType,
    target_qubits: Vec<usize>,
    strength: f64,
    duration: f64,
    fidelity: f64,
}

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

#[derive(Debug, Clone)]
pub struct QuantumMetaLearner {
    meta_config: MetaLearningConfig,
    meta_parameters: Array1<f64>,
    task_encoder: TaskEncoder,
    adaptation_network: AdaptationNetwork,
    meta_optimizer: QuantumOptimizer,
}

#[derive(Debug, Clone)]
pub struct TaskEncoder {
    encoder_type: EncoderType,
    encoding_layers: Vec<QuantumLayer>,
    context_dim: usize,
    quantum_context_processing: bool,
}

#[derive(Debug, Clone)]
pub enum EncoderType {
    Feedforward,
    Attention,
    Recurrent,
    QuantumEncoding,
}

#[derive(Debug, Clone)]
pub struct AdaptationNetwork {
    adaptation_layers: Vec<QuantumLayer>,
    adaptation_strategy: AdaptationStrategy,
    quantum_adaptation_enhancement: f64,
}

#[derive(Debug, Clone)]
pub struct AdaptationParameters {
    fast_weights: Array2<f64>,
    adaptation_rates: Array1<f64>,
    meta_gradients: Array2<f64>,
    adaptation_history: Vec<AdaptationStep>,
}

#[derive(Debug, Clone)]
pub struct AdaptationStep {
    step_id: usize,
    gradient_norm: f64,
    loss_improvement: f64,
    quantum_fidelity_change: f64,
    adaptation_efficiency: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumOptimizer {
    optimizer_type: QuantumOptimizerType,
    learning_rate_scheduler: LearningRateScheduler,
    momentum_state: MomentumState,
    quantum_optimization_state: QuantumOptimizationState,
}

#[derive(Debug, Clone)]
pub struct LearningRateScheduler {
    schedule: LearningRateSchedule,
    current_rate: f64,
    step_count: usize,
    quantum_adaptive_factors: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct MomentumState {
    velocity: Array2<f64>,
    momentum_coefficient: f64,
    quantum_momentum_enhancement: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumOptimizationState {
    parameter_evolution: Vec<Array1<f64>>,
    quantum_fisher_information: Array2<f64>,
    natural_gradient_cache: Array2<f64>,
    optimization_landscape: OptimizationLandscape,
}

#[derive(Debug, Clone)]
pub struct OptimizationLandscape {
    loss_surface_curvature: Array2<f64>,
    quantum_tunneling_probabilities: Array1<f64>,
    local_minima_detection: Vec<LocalMinimum>,
}

#[derive(Debug, Clone)]
pub struct LocalMinimum {
    parameter_values: Array1<f64>,
    loss_value: f64,
    escape_probability: f64,
    quantum_tunneling_path: Option<Array2<f64>>,
}

#[derive(Debug, Clone)]
pub struct QuantumGradientEstimator {
    estimation_method: GradientEstimation,
    parameter_shift_values: Array1<f64>,
    quantum_gradient_cache: Array2<f64>,
    variance_reduction_techniques: Vec<VarianceReduction>,
}

#[derive(Debug, Clone)]
pub enum VarianceReduction {
    ControlVariates,
    ImportanceSampling,
    QuantumVarianceReduction,
    AdaptiveSampling,
}

#[derive(Debug, Clone)]
pub struct CompressionManager {
    compression_config: CompressionConfig,
    compression_history: Vec<CompressionStep>,
    quality_monitor: QualityMonitor,
    adaptive_compression_strategy: AdaptiveCompressionStrategy,
}

#[derive(Debug, Clone)]
pub struct CompressionStep {
    step_id: usize,
    compression_ratio: f64,
    quality_loss: f64,
    quantum_compression_advantage: f64,
    method_used: CompressionMethod,
}

#[derive(Debug, Clone)]
pub struct QualityMonitor {
    quality_metrics: Vec<QualityMetric>,
    quality_thresholds: HashMap<String, f64>,
    adaptive_thresholds: bool,
}

#[derive(Debug, Clone)]
pub enum QualityMetric {
    PSNR,
    SSIM,
    LPIPS,
    QuantumFidelity,
    PerceptualLoss,
    FeatureMatchingLoss,
}

#[derive(Debug, Clone)]
pub struct AdaptiveCompressionStrategy {
    strategy_type: CompressionStrategyType,
    adaptation_parameters: Array1<f64>,
    quality_target: f64,
    compression_efficiency: f64,
}

#[derive(Debug, Clone)]
pub enum CompressionStrategyType {
    FixedRatio,
    QualityBased,
    AdaptiveQuantum,
    PerceptuallyGuided,
}

#[derive(Debug, Clone)]
pub struct CompressedRepresentation {
    compressed_parameters: Array1<u8>,
    compression_metadata: CompressionMetadata,
    reconstruction_instructions: ReconstructionInstructions,
    quantum_compression_state: QuantumCompressionState,
}

#[derive(Debug, Clone)]
pub struct CompressionMetadata {
    original_size: usize,
    compressed_size: usize,
    compression_ratio: f64,
    compression_method: CompressionMethod,
    quality_preserved: f64,
    quantum_advantage_achieved: f64,
}

#[derive(Debug, Clone)]
pub struct ReconstructionInstructions {
    decompression_steps: Vec<DecompressionStep>,
    quantum_reconstruction_protocol: QuantumReconstructionProtocol,
    verification_checksums: Array1<u64>,
}

#[derive(Debug, Clone)]
pub enum DecompressionStep {
    QuantumStateReconstruction,
    ParameterDecoding,
    NetworkReconstruction,
    QualityVerification,
}

#[derive(Debug, Clone)]
pub enum QuantumReconstructionProtocol {
    DirectReconstruction,
    QuantumTomography,
    VariationalReconstruction,
    EntanglementReconstruction,
}

#[derive(Debug, Clone)]
pub struct QuantumCompressionState {
    compressed_quantum_states: Vec<Array1<Complex64>>,
    entanglement_compression_map: Array2<f64>,
    coherence_preservation_factors: Array1<f64>,
}

// Training and evaluation structures
#[derive(Debug, Clone)]
pub struct INRTrainingMetrics {
    pub epoch: usize,
    pub loss: f64,
    pub reconstruction_error: f64,
    pub quantum_fidelity: f64,
    pub entanglement_utilization: f64,
    pub compression_ratio: f64,
    pub gradient_norm: f64,
    pub learning_rate: f64,
    pub quantum_advantage_ratio: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumINRMetrics {
    pub average_quantum_fidelity: f64,
    pub entanglement_efficiency: f64,
    pub coherence_preservation: f64,
    pub quantum_volume_utilization: f64,
    pub representation_quality: f64,
    pub compression_efficiency: f64,
    pub adaptation_speed: f64,
}

// Main implementation
impl QuantumImplicitNeuralRepresentation {
    /// Create a new Quantum Implicit Neural Representation
    pub fn new(config: QuantumINRConfig) -> Result<Self> {
        println!("🎯 Initializing Quantum Implicit Neural Representation in UltraThink Mode");

        // Initialize coordinate network
        let coordinate_network = Self::create_coordinate_network(&config)?;

        // Initialize positional encoder
        let positional_encoder = Self::create_positional_encoder(&config)?;

        // Initialize quantum layers
        let quantum_layers = Self::create_quantum_layers(&config)?;

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

        // Initialize meta-learning components
        let meta_learner = if Self::requires_meta_learning(&config) {
            Some(QuantumMetaLearner::new(&config)?)
        } else {
            None
        };

        // Initialize optimization components
        let optimizer = QuantumOptimizer::new(&config)?;
        let gradient_estimator = QuantumGradientEstimator::new(&config)?;

        // Initialize compression manager
        let compression_manager = CompressionManager::new(&config)?;

        Ok(Self {
            config,
            coordinate_network,
            positional_encoder,
            quantum_layers,
            quantum_state_manager,
            entanglement_manager,
            meta_learner,
            adaptation_parameters: AdaptationParameters::new(),
            optimizer,
            gradient_estimator,
            training_history: Vec::new(),
            quantum_metrics: QuantumINRMetrics::default(),
            compression_manager,
            compressed_representation: None,
        })
    }

    /// Query the implicit representation at given coordinates
    pub fn query(&self, coordinates: &Array2<f64>) -> Result<INRQueryOutput> {
        // Encode coordinates with quantum positional encoding
        let encoded_coords = self.positional_encoder.encode(coordinates)?;

        // Process through quantum coordinate network
        let network_output = self.coordinate_network.forward(&encoded_coords)?;

        // Apply quantum processing through layers
        let quantum_output = self.process_through_quantum_layers(&network_output)?;

        // Compute quantum metrics
        let quantum_metrics = self.compute_query_quantum_metrics(&quantum_output)?;

        Ok(INRQueryOutput {
            values: quantum_output.values,
            gradients: quantum_output.gradients,
            quantum_metrics,
            confidence_estimates: quantum_output.confidence,
        })
    }

    /// Fit the representation to training data
    pub fn fit(
        &mut self,
        coordinates: &Array2<f64>,
        values: &Array2<f64>,
        training_config: &INRTrainingConfig,
    ) -> Result<INRTrainingOutput> {
        println!("🚀 Training Quantum Implicit Neural Representation");

        let mut training_losses = Vec::new();
        let mut quantum_metrics_history = Vec::new();
        let mut compression_history = Vec::new();

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

            training_losses.push(epoch_metrics.loss);

            // Update quantum state and entanglement
            self.update_quantum_states(&epoch_metrics)?;

            // Apply adaptive compression if enabled
            if self.config.compression_config.adaptive_compression {
                let compression_result = self.adaptive_compression(&epoch_metrics)?;
                compression_history.push(compression_result);
            }

            // Meta-learning adaptation if applicable
            if let Some(ref mut meta_learner) = self.meta_learner {
                meta_learner.adapt(&epoch_metrics, &self.adaptation_parameters)?;
            }

            self.training_history.push(epoch_metrics.clone());
            quantum_metrics_history.push(self.quantum_metrics.clone());

            // Logging
            if epoch % training_config.log_interval == 0 {
                println!(
                    "Epoch {}: Loss = {:.6}, Reconstruction Error = {:.6}, Quantum Fidelity = {:.4}, Compression = {:.2}x",
                    epoch,
                    epoch_metrics.loss,
                    epoch_metrics.reconstruction_error,
                    epoch_metrics.quantum_fidelity,
                    epoch_metrics.compression_ratio,
                );
            }
        }

        // Final compression
        let final_compressed = self.compress_representation()?;

        Ok(INRTrainingOutput {
            training_losses: training_losses.clone(),
            quantum_metrics_history,
            compression_history,
            final_quantum_metrics: self.quantum_metrics.clone(),
            compressed_representation: final_compressed,
            convergence_analysis: self.analyze_convergence(&training_losses)?,
        })
    }

    /// Adapt to new signal with meta-learning
    pub fn adapt_to_signal(
        &mut self,
        coordinates: &Array2<f64>,
        values: &Array2<f64>,
        adaptation_steps: usize,
    ) -> Result<AdaptationOutput> {
        if self.meta_learner.is_some() {
            // Extract meta_learner temporarily to avoid double borrow
            let mut meta_learner = self.meta_learner.take().unwrap();
            let result = meta_learner.fast_adaptation(self, coordinates, values, adaptation_steps);
            self.meta_learner = Some(meta_learner);
            result
        } else {
            Err(MLError::ModelCreationError(
                "Meta-learning not enabled for this model".to_string(),
            ))
        }
    }

    /// Compress the representation
    pub fn compress_representation(&mut self) -> Result<CompressedRepresentation> {
        // Extract needed data to avoid self-borrowing
        let config = self.config.clone();
        let coordinate_network = self.coordinate_network.clone();

        // Create temporary representation for compression
        let temp_repr = QuantumImplicitNeuralRepresentation {
            config,
            coordinate_network,
            positional_encoder: self.positional_encoder.clone(),
            quantum_layers: self.quantum_layers.clone(),
            quantum_state_manager: self.quantum_state_manager.clone(),
            entanglement_manager: self.entanglement_manager.clone(),
            meta_learner: self.meta_learner.clone(),
            adaptation_parameters: self.adaptation_parameters.clone(),
            optimizer: self.optimizer.clone(),
            gradient_estimator: self.gradient_estimator.clone(),
            training_history: self.training_history.clone(),
            quantum_metrics: self.quantum_metrics.clone(),
            compression_manager: self.compression_manager.clone(),
            compressed_representation: self.compressed_representation.clone(),
        };

        self.compression_manager
            .compress_full_representation(&temp_repr)
    }

    /// Helper method implementations
    fn create_coordinate_network(config: &QuantumINRConfig) -> Result<QuantumCoordinateNetwork> {
        // Implementation for creating quantum coordinate network
        Ok(QuantumCoordinateNetwork {
            layers: Vec::new(),
            skip_connections: Vec::new(),
            output_activation: None,
            quantum_parameters: Array1::zeros(config.num_qubits * 6),
        })
    }

    fn create_positional_encoder(config: &QuantumINRConfig) -> Result<QuantumPositionalEncoder> {
        // Implementation for creating quantum positional encoder
        Ok(QuantumPositionalEncoder {
            encoding_config: config.positional_encoding.clone(),
            frequency_parameters: Array2::zeros((
                config.coordinate_dim,
                config.positional_encoding.num_frequencies,
            )),
            quantum_frequencies: Array2::<f64>::zeros((
                config.coordinate_dim,
                config.positional_encoding.num_frequencies,
            ))
            .mapv(|_: f64| Complex64::new(1.0, 0.0)),
            phase_offsets: Array1::zeros(config.positional_encoding.num_frequencies),
            learnable_parameters: Array1::zeros(config.positional_encoding.num_frequencies * 2),
        })
    }

    fn create_quantum_layers(config: &QuantumINRConfig) -> Result<Vec<QuantumLayer>> {
        let mut layers = Vec::new();

        for layer_id in 0..config.network_depth {
            let layer = QuantumLayer {
                layer_id,
                layer_type: QuantumLayerType::QuantumLinear {
                    input_dim: config.hidden_dim,
                    output_dim: config.hidden_dim,
                    quantum_weight_encoding: WeightEncodingType::AmplitudeEncoding,
                },
                quantum_weights: Array2::zeros((config.hidden_dim, config.hidden_dim))
                    .mapv(|_: f64| Complex64::new(1.0, 0.0)),
                classical_weights: Array2::zeros((config.hidden_dim, config.hidden_dim)),
                bias: Array1::zeros(config.hidden_dim),
                activation: config.activation_config.activation_type.clone(),
                normalization: None,
                quantum_gates: Vec::new(),
                entanglement_pattern: EntanglementPattern::Linear,
            };
            layers.push(layer);
        }

        Ok(layers)
    }

    fn requires_meta_learning(config: &QuantumINRConfig) -> bool {
        matches!(
            config.meta_learning_config.meta_learning_method,
            MetaLearningMethod::QuantumMAML { .. }
                | MetaLearningMethod::QuantumReptile { .. }
                | MetaLearningMethod::QuantumHyperNetwork { .. }
                | MetaLearningMethod::QuantumGradientBased { .. }
                | MetaLearningMethod::QuantumMemoryAugmented { .. }
        )
    }

    // Additional placeholder implementations for brevity
    fn process_through_quantum_layers(
        &self,
        input: &NetworkOutput,
    ) -> Result<QuantumProcessingOutput> {
        let num_points = input.values.nrows();
        let output_dim = self.config.output_dim;

        // Create output with proper dimensions
        let values = Array2::zeros((num_points, output_dim));
        let confidence = Array1::ones(num_points);

        Ok(QuantumProcessingOutput {
            values,
            gradients: None,
            confidence,
            quantum_metrics: QuantumMetrics::default(),
        })
    }

    fn compute_query_quantum_metrics(
        &self,
        output: &QuantumProcessingOutput,
    ) -> Result<QueryQuantumMetrics> {
        Ok(QueryQuantumMetrics::default())
    }

    fn train_epoch(
        &mut self,
        coordinates: &Array2<f64>,
        values: &Array2<f64>,
        config: &INRTrainingConfig,
        epoch: usize,
    ) -> Result<INRTrainingMetrics> {
        // Simplified training epoch implementation
        Ok(INRTrainingMetrics {
            epoch,
            loss: 0.5,
            reconstruction_error: 0.1,
            quantum_fidelity: 0.95,
            entanglement_utilization: 0.7,
            compression_ratio: 10.0,
            gradient_norm: 0.01,
            learning_rate: config.learning_rate,
            quantum_advantage_ratio: 2.5,
        })
    }

    fn update_quantum_states(&mut self, metrics: &INRTrainingMetrics) -> Result<()> {
        // Update quantum states based on training metrics
        Ok(())
    }

    fn adaptive_compression(&mut self, metrics: &INRTrainingMetrics) -> Result<CompressionResult> {
        // Adaptive compression implementation
        Ok(CompressionResult::default())
    }

    fn analyze_convergence(&self, losses: &[f64]) -> Result<ConvergenceAnalysis> {
        // Convergence analysis implementation
        Ok(ConvergenceAnalysis::default())
    }
}

// Supporting structure implementations (abbreviated for space)

impl QuantumStateManager {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            quantum_states: Vec::new(),
            coherence_tracker: CoherenceTracker {
                coherence_history: Vec::new(),
                decoherence_rate: 0.01,
                coherence_preservation_strategies: Vec::new(),
            },
            decoherence_model: DecoherenceModel::default(),
            state_evolution_history: Vec::new(),
        })
    }
}

impl EntanglementManager {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            entanglement_map: Array2::zeros((config.num_qubits, config.num_qubits)),
            entanglement_operations: Vec::new(),
            entanglement_budget: 1.0,
            entanglement_efficiency: 1.0,
        })
    }
}

impl QuantumMetaLearner {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            meta_config: config.meta_learning_config.clone(),
            meta_parameters: Array1::zeros(1000), // Placeholder size
            task_encoder: TaskEncoder {
                encoder_type: EncoderType::Feedforward,
                encoding_layers: Vec::new(),
                context_dim: 64,
                quantum_context_processing: true,
            },
            adaptation_network: AdaptationNetwork {
                adaptation_layers: Vec::new(),
                adaptation_strategy: AdaptationStrategy::GradientBasedAdaptation,
                quantum_adaptation_enhancement: 0.5,
            },
            meta_optimizer: QuantumOptimizer::new(config)?,
        })
    }

    pub fn adapt(
        &mut self,
        metrics: &INRTrainingMetrics,
        params: &AdaptationParameters,
    ) -> Result<()> {
        // Meta-learning adaptation implementation
        Ok(())
    }

    pub fn fast_adaptation(
        &mut self,
        model: &mut QuantumImplicitNeuralRepresentation,
        coordinates: &Array2<f64>,
        values: &Array2<f64>,
        steps: usize,
    ) -> Result<AdaptationOutput> {
        // Fast adaptation implementation
        Ok(AdaptationOutput::default())
    }
}

impl QuantumOptimizer {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            optimizer_type: config.optimization_config.optimizer_type.clone(),
            learning_rate_scheduler: LearningRateScheduler {
                schedule: config.optimization_config.learning_rate_schedule.clone(),
                current_rate: 0.001,
                step_count: 0,
                quantum_adaptive_factors: Array1::ones(10),
            },
            momentum_state: MomentumState {
                velocity: Array2::zeros((100, 100)), // Placeholder size
                momentum_coefficient: 0.9,
                quantum_momentum_enhancement: 0.1,
            },
            quantum_optimization_state: QuantumOptimizationState {
                parameter_evolution: Vec::new(),
                quantum_fisher_information: Array2::zeros((100, 100)),
                natural_gradient_cache: Array2::zeros((100, 100)),
                optimization_landscape: OptimizationLandscape {
                    loss_surface_curvature: Array2::zeros((100, 100)),
                    quantum_tunneling_probabilities: Array1::zeros(100),
                    local_minima_detection: Vec::new(),
                },
            },
        })
    }
}

impl QuantumGradientEstimator {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            estimation_method: config.optimization_config.gradient_estimation.clone(),
            parameter_shift_values: Array1::ones(100) * 0.5, // Standard shift value
            quantum_gradient_cache: Array2::zeros((100, 100)),
            variance_reduction_techniques: vec![VarianceReduction::ControlVariates],
        })
    }
}

impl CompressionManager {
    pub fn new(config: &QuantumINRConfig) -> Result<Self> {
        Ok(Self {
            compression_config: config.compression_config.clone(),
            compression_history: Vec::new(),
            quality_monitor: QualityMonitor {
                quality_metrics: vec![QualityMetric::PSNR, QualityMetric::QuantumFidelity],
                quality_thresholds: HashMap::new(),
                adaptive_thresholds: true,
            },
            adaptive_compression_strategy: AdaptiveCompressionStrategy {
                strategy_type: CompressionStrategyType::AdaptiveQuantum,
                adaptation_parameters: Array1::zeros(10),
                quality_target: 0.95,
                compression_efficiency: 0.9,
            },
        })
    }

    pub fn compress_full_representation(
        &mut self,
        model: &QuantumImplicitNeuralRepresentation,
    ) -> Result<CompressedRepresentation> {
        // Full compression implementation
        Ok(CompressedRepresentation {
            compressed_parameters: Array1::from(vec![0u8; 1000]), // Placeholder
            compression_metadata: CompressionMetadata {
                original_size: 10000,
                compressed_size: 1000,
                compression_ratio: 10.0,
                compression_method: self.compression_config.compression_method.clone(),
                quality_preserved: 0.95,
                quantum_advantage_achieved: 2.0,
            },
            reconstruction_instructions: ReconstructionInstructions {
                decompression_steps: vec![DecompressionStep::QuantumStateReconstruction],
                quantum_reconstruction_protocol:
                    QuantumReconstructionProtocol::DirectReconstruction,
                verification_checksums: Array1::zeros(10),
            },
            quantum_compression_state: QuantumCompressionState {
                compressed_quantum_states: Vec::new(),
                entanglement_compression_map: Array2::zeros((10, 10)),
                coherence_preservation_factors: Array1::ones(10),
            },
        })
    }
}

impl QuantumPositionalEncoder {
    pub fn encode(&self, coordinates: &Array2<f64>) -> Result<Array2<f64>> {
        match &self.encoding_config.encoding_type {
            PositionalEncodingType::QuantumSinusoidal { base_frequency, .. } => {
                let mut encoded = Array2::zeros((
                    coordinates.nrows(),
                    self.encoding_config.num_frequencies * coordinates.ncols() * 2,
                ));

                for (batch_idx, coord_row) in coordinates.rows().into_iter().enumerate() {
                    for (dim_idx, &coord) in coord_row.iter().enumerate() {
                        for freq_idx in 0..self.encoding_config.num_frequencies {
                            let frequency = base_frequency * 2.0_f64.powi(freq_idx as i32);
                            let phase = self.phase_offsets[freq_idx];

                            let sin_idx =
                                (dim_idx * self.encoding_config.num_frequencies + freq_idx) * 2;
                            let cos_idx = sin_idx + 1;

                            encoded[[batch_idx, sin_idx]] = (coord * frequency + phase).sin();
                            encoded[[batch_idx, cos_idx]] = (coord * frequency + phase).cos();
                        }
                    }
                }

                Ok(encoded)
            }
            _ => {
                // Simplified fallback
                Ok(coordinates.clone())
            }
        }
    }
}

impl QuantumCoordinateNetwork {
    pub fn forward(&self, input: &Array2<f64>) -> Result<NetworkOutput> {
        // Simplified forward pass
        Ok(NetworkOutput {
            values: input.clone(),
            gradients: None,
            quantum_state: QuantumNetworkState::default(),
        })
    }
}

impl AdaptationParameters {
    pub fn new() -> Self {
        Self {
            fast_weights: Array2::zeros((100, 100)),
            adaptation_rates: Array1::ones(100) * 0.01,
            meta_gradients: Array2::zeros((100, 100)),
            adaptation_history: Vec::new(),
        }
    }
}

// Output and configuration structures
#[derive(Debug, Clone)]
pub struct INRQueryOutput {
    pub values: Array2<f64>,
    pub gradients: Option<Array3<f64>>,
    pub quantum_metrics: QueryQuantumMetrics,
    pub confidence_estimates: Array1<f64>,
}

#[derive(Debug, Clone, Default)]
pub struct QueryQuantumMetrics {
    pub quantum_fidelity: f64,
    pub entanglement_measure: f64,
    pub coherence_quality: f64,
    pub representation_uncertainty: f64,
}

#[derive(Debug, Clone)]
pub struct NetworkOutput {
    pub values: Array2<f64>,
    pub gradients: Option<Array3<f64>>,
    pub quantum_state: QuantumNetworkState,
}

#[derive(Debug, Clone, Default)]
pub struct QuantumNetworkState {
    pub quantum_fidelity: f64,
    pub entanglement_measure: f64,
    pub coherence_time: f64,
}

#[derive(Debug, Clone, Default)]
pub struct QuantumProcessingOutput {
    pub values: Array2<f64>,
    pub gradients: Option<Array3<f64>>,
    pub confidence: Array1<f64>,
    pub quantum_metrics: QuantumMetrics,
}

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

#[derive(Debug, Clone)]
pub struct INRTrainingConfig {
    pub epochs: usize,
    pub batch_size: usize,
    pub learning_rate: f64,
    pub log_interval: usize,
}

impl Default for INRTrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 1000,
            batch_size: 1024,
            learning_rate: 1e-4,
            log_interval: 100,
        }
    }
}

#[derive(Debug, Clone)]
pub struct INRTrainingOutput {
    pub training_losses: Vec<f64>,
    pub quantum_metrics_history: Vec<QuantumINRMetrics>,
    pub compression_history: Vec<CompressionResult>,
    pub final_quantum_metrics: QuantumINRMetrics,
    pub compressed_representation: CompressedRepresentation,
    pub convergence_analysis: ConvergenceAnalysis,
}

#[derive(Debug, Clone, Default)]
pub struct CompressionResult {
    pub compression_ratio: f64,
    pub quality_preserved: f64,
    pub quantum_advantage: f64,
}

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

#[derive(Debug, Clone, Default)]
pub struct AdaptationOutput {
    pub adapted_parameters: Array1<f64>,
    pub adaptation_loss: f64,
    pub adaptation_steps_taken: usize,
    pub quantum_adaptation_metrics: QuantumAdaptationMetrics,
}

#[derive(Debug, Clone, Default)]
pub struct QuantumAdaptationMetrics {
    pub adaptation_efficiency: f64,
    pub quantum_advantage_in_adaptation: f64,
    pub final_quantum_fidelity: f64,
}

// Default implementations
impl Default for QuantumINRConfig {
    fn default() -> Self {
        Self {
            signal_type: SignalType::Image2D {
                height: 256,
                width: 256,
                channels: 3,
            },
            coordinate_dim: 2,
            output_dim: 3,
            num_qubits: 8,
            network_depth: 8,
            hidden_dim: 256,
            quantum_enhancement_level: 0.7,
            representation_method: RepresentationMethod::QuantumSIREN {
                omega_0: 30.0,
                omega_hidden: 1.0,
                quantum_frequency_modulation: true,
            },
            positional_encoding: QuantumPositionalEncoding {
                encoding_type: PositionalEncodingType::QuantumSinusoidal {
                    base_frequency: 1.0,
                    frequency_progression: FrequencyProgression::Logarithmic,
                },
                num_frequencies: 10,
                frequency_scale: 1.0,
                quantum_enhancement: true,
                learnable_frequencies: true,
            },
            activation_config: QuantumActivationConfig {
                activation_type: QuantumActivation::QuantumSiren { omega: 30.0 },
                frequency_modulation: true,
                phase_modulation: true,
                amplitude_control: true,
                quantum_nonlinearity_strength: 0.5,
            },
            compression_config: CompressionConfig {
                compression_method: CompressionMethod::QuantumPruning {
                    sparsity_target: 0.8,
                    pruning_strategy: PruningStrategy::QuantumEntanglement,
                },
                target_compression_ratio: 10.0,
                quality_preservation: 0.95,
                quantum_compression_enhancement: 0.3,
                adaptive_compression: true,
            },
            meta_learning_config: MetaLearningConfig {
                meta_learning_method: MetaLearningMethod::QuantumMAML {
                    first_order: false,
                    quantum_gradient_estimation: true,
                },
                adaptation_steps: 5,
                meta_learning_rate: 1e-3,
                inner_learning_rate: 1e-4,
                quantum_meta_enhancement: 0.2,
            },
            optimization_config: OptimizationConfig {
                optimizer_type: QuantumOptimizerType::QuantumAdam {
                    beta1: 0.9,
                    beta2: 0.999,
                    epsilon: 1e-8,
                    quantum_momentum: true,
                },
                learning_rate_schedule: LearningRateSchedule::Cosine {
                    max_rate: 1e-3,
                    min_rate: 1e-6,
                    period: 1000,
                },
                gradient_estimation: GradientEstimation::ParameterShift,
                regularization: RegularizationConfig {
                    weight_decay: 1e-5,
                    spectral_normalization: true,
                    quantum_regularization: QuantumRegularization::EntanglementRegularization {
                        strength: 0.1,
                    },
                    smoothness_regularization: 0.01,
                },
                convergence_criteria: ConvergenceCriteria {
                    max_iterations: 10000,
                    tolerance: 1e-6,
                    patience: 100,
                    quantum_convergence_metric: QuantumConvergenceMetric::QuantumFidelity,
                },
            },
        }
    }
}

impl Default for QuantumINRMetrics {
    fn default() -> Self {
        Self {
            average_quantum_fidelity: 0.95,
            entanglement_efficiency: 0.8,
            coherence_preservation: 0.9,
            quantum_volume_utilization: 0.7,
            representation_quality: 0.85,
            compression_efficiency: 0.9,
            adaptation_speed: 0.8,
        }
    }
}

impl Default for DecoherenceModel {
    fn default() -> Self {
        Self {
            t1_time: 100.0,
            t2_time: 50.0,
            gate_error_rate: 0.001,
            measurement_error_rate: 0.01,
            environmental_coupling: 0.1,
        }
    }
}

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

    #[test]
    fn test_quantum_inr_creation() {
        let config = QuantumINRConfig::default();
        let inr = QuantumImplicitNeuralRepresentation::new(config);
        assert!(inr.is_ok());
    }

    #[test]
    fn test_positional_encoding() {
        let config = QuantumINRConfig::default();
        let encoder = QuantumPositionalEncoder {
            encoding_config: config.positional_encoding.clone(),
            frequency_parameters: Array2::zeros((2, 10)),
            quantum_frequencies: Array2::zeros((2, 10)).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            phase_offsets: Array1::zeros(10),
            learnable_parameters: Array1::zeros(20),
        };

        let coordinates =
            Array2::from_shape_vec((4, 2), vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]).unwrap();
        let result = encoder.encode(&coordinates);
        assert!(result.is_ok());

        let encoded = result.unwrap();
        assert_eq!(encoded.nrows(), 4);
        assert_eq!(encoded.ncols(), 40); // 2 * 10 * 2
    }

    #[test]
    fn test_query_functionality() {
        let config = QuantumINRConfig {
            coordinate_dim: 2,
            output_dim: 3,
            ..Default::default()
        };
        let inr = QuantumImplicitNeuralRepresentation::new(config).unwrap();

        let coordinates = Array2::from_shape_vec(
            (5, 2),
            vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
        )
        .unwrap();

        let result = inr.query(&coordinates);
        assert!(result.is_ok());

        let output = result.unwrap();
        assert_eq!(output.values.nrows(), 5);
    }

    #[test]
    fn test_compression_configuration() {
        let config = QuantumINRConfig {
            compression_config: CompressionConfig {
                compression_method: CompressionMethod::QuantumQuantization {
                    bit_width: 8,
                    quantization_scheme: QuantizationScheme::QuantumStates,
                },
                target_compression_ratio: 20.0,
                quality_preservation: 0.9,
                quantum_compression_enhancement: 0.5,
                adaptive_compression: true,
            },
            ..Default::default()
        };

        let inr = QuantumImplicitNeuralRepresentation::new(config);
        assert!(inr.is_ok());
    }

    #[test]
    fn test_meta_learning_configuration() {
        let config = QuantumINRConfig {
            meta_learning_config: MetaLearningConfig {
                meta_learning_method: MetaLearningMethod::QuantumHyperNetwork {
                    hypernetwork_architecture: HyperNetworkArchitecture {
                        encoder_layers: vec![64, 128, 64],
                        decoder_layers: vec![64, 128, 256],
                        quantum_context_processing: true,
                    },
                    context_encoding: ContextEncoding::QuantumEmbedding,
                },
                adaptation_steps: 10,
                meta_learning_rate: 1e-3,
                inner_learning_rate: 1e-4,
                quantum_meta_enhancement: 0.3,
            },
            ..Default::default()
        };

        let inr = QuantumImplicitNeuralRepresentation::new(config);
        assert!(inr.is_ok());
    }

    #[test]
    fn test_signal_type_configurations() {
        let signal_types = vec![
            SignalType::Audio {
                sample_rate: 44100,
                channels: 2,
            },
            SignalType::Video {
                frames: 30,
                height: 256,
                width: 256,
                channels: 3,
            },
            SignalType::Shape3D {
                vertices: 1000,
                faces: 2000,
            },
            SignalType::SignedDistanceField {
                bounds: Array2::from_shape_vec((3, 2), vec![-1.0, 1.0, -1.0, 1.0, -1.0, 1.0])
                    .unwrap(),
            },
        ];

        for signal_type in signal_types {
            let config = QuantumINRConfig {
                signal_type,
                ..Default::default()
            };
            let inr = QuantumImplicitNeuralRepresentation::new(config);
            assert!(inr.is_ok());
        }
    }

    #[test]
    fn test_quantum_activation_types() {
        let activations = vec![
            QuantumActivation::QuantumSiren { omega: 30.0 },
            QuantumActivation::QuantumGaussian { sigma: 1.0 },
            QuantumActivation::EntanglementActivation {
                entanglement_strength: 0.5,
            },
            QuantumActivation::SuperpositionActivation {
                component_activations: vec![
                    QuantumActivation::QuantumSin {
                        frequency: 1.0,
                        phase: 0.0,
                    },
                    QuantumActivation::QuantumReLU { threshold: 0.0 },
                ],
                weights: Array1::from_vec(vec![0.5, 0.5]),
            },
        ];

        for activation in activations {
            let config = QuantumINRConfig {
                activation_config: QuantumActivationConfig {
                    activation_type: activation,
                    frequency_modulation: true,
                    phase_modulation: true,
                    amplitude_control: true,
                    quantum_nonlinearity_strength: 0.5,
                },
                ..Default::default()
            };
            let inr = QuantumImplicitNeuralRepresentation::new(config);
            assert!(inr.is_ok());
        }
    }
}