quantrs2_ml/

quantum_neural_radiance_fields.rs

//! Quantum Neural Radiance Fields (QNeRF)
//!
//! This module implements cutting-edge Quantum Neural Radiance Fields for 3D scene representation
//! and neural rendering with quantum advantages:
//! - Quantum volumetric density and color estimation
//! - Quantum ray marching with entanglement-based sampling
//! - Quantum multi-scale feature encoding
//! - Quantum view synthesis with coherent superposition
//! - Quantum-enhanced positional encoding
//! - Quantum attention mechanisms for 3D spatial reasoning

use crate::error::{MLError, Result};
use ndarray::{Array1, Array2, Array3, Array4, 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 Neural Radiance Fields
#[derive(Debug, Clone)]
pub struct QuantumNeRFConfig {
    pub scene_bounds: SceneBounds,
    pub num_qubits: usize,
    pub quantum_encoding_levels: usize,
    pub max_ray_samples: usize,
    pub quantum_sampling_strategy: QuantumSamplingStrategy,
    pub quantum_enhancement_level: f64,
    pub use_quantum_positional_encoding: bool,
    pub quantum_attention_config: QuantumAttentionConfig,
    pub volumetric_rendering_config: VolumetricRenderingConfig,
    pub quantum_multiscale_features: bool,
    pub entanglement_based_interpolation: bool,
    pub quantum_view_synthesis: bool,
    pub decoherence_mitigation: DecoherenceMitigationConfig,
}

#[derive(Debug, Clone)]
pub struct SceneBounds {
    pub min_bound: Array1<f64>,          // [x, y, z] minimum
    pub max_bound: Array1<f64>,          // [x, y, z] maximum
    pub voxel_resolution: Array1<usize>, // [nx, ny, nz]
}

#[derive(Debug, Clone)]
pub enum QuantumSamplingStrategy {
    /// Uniform sampling with quantum noise
    QuantumUniform {
        min_samples: usize,
        max_samples: usize,
        quantum_jitter: f64,
    },

    /// Hierarchical sampling with quantum importance
    QuantumHierarchical {
        coarse_samples: usize,
        fine_samples: usize,
        quantum_importance_threshold: f64,
    },

    /// Quantum adaptive sampling based on uncertainty
    QuantumAdaptive {
        initial_samples: usize,
        max_refinements: usize,
        uncertainty_threshold: f64,
        quantum_uncertainty_estimation: bool,
    },

    /// Entanglement-based correlated sampling
    EntanglementCorrelated {
        base_samples: usize,
        correlation_strength: f64,
        entanglement_radius: f64,
    },

    /// Quantum Monte Carlo sampling
    QuantumMonteCarlo {
        num_chains: usize,
        chain_length: usize,
        quantum_proposal_distribution: QuantumProposalType,
    },
}

#[derive(Debug, Clone)]
pub enum QuantumProposalType {
    QuantumGaussian { sigma: f64 },
    QuantumLevyFlight { alpha: f64 },
    QuantumMetropolis { temperature: f64 },
}

#[derive(Debug, Clone)]
pub struct QuantumAttentionConfig {
    pub use_spatial_attention: bool,
    pub use_view_attention: bool,
    pub use_scale_attention: bool,
    pub num_attention_heads: usize,
    pub attention_type: QuantumAttentionType,
    pub entanglement_in_attention: bool,
    pub quantum_query_key_value: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumAttentionType {
    StandardQuantumAttention,
    QuantumMultiHeadAttention,
    QuantumSpatialAttention,
    QuantumViewAttention,
    EntanglementBasedAttention,
    QuantumCrossAttention,
}

#[derive(Debug, Clone)]
pub struct VolumetricRenderingConfig {
    pub use_quantum_alpha_compositing: bool,
    pub quantum_density_activation: QuantumActivationType,
    pub quantum_color_space: QuantumColorSpace,
    pub quantum_illumination_model: QuantumIlluminationModel,
    pub quantum_material_properties: bool,
    pub quantum_light_transport: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumActivationType {
    QuantumReLU,
    QuantumSigmoid,
    QuantumSoftplus,
    QuantumTanh,
    QuantumEntanglementActivation,
    QuantumPhaseActivation,
}

#[derive(Debug, Clone)]
pub enum QuantumColorSpace {
    RGB,
    HSV,
    LAB,
    QuantumColorSpace { basis_vectors: Array2<f64> },
    EntangledColorChannels,
}

#[derive(Debug, Clone)]
pub enum QuantumIlluminationModel {
    Lambertian,
    Phong,
    PBR, // Physically Based Rendering
    QuantumPhotonMapping,
    QuantumLightTransport,
    EntanglementBasedLighting,
}

#[derive(Debug, Clone)]
pub struct DecoherenceMitigationConfig {
    pub enable_error_correction: bool,
    pub coherence_preservation_weight: f64,
    pub decoherence_compensation_factor: f64,
    pub quantum_error_rate_threshold: f64,
}

/// Main Quantum Neural Radiance Field model
pub struct QuantumNeRF {
    config: QuantumNeRFConfig,

    // Core quantum components
    quantum_mlp_coarse: QuantumMLP,
    quantum_mlp_fine: QuantumMLP,
    quantum_positional_encoder: QuantumPositionalEncoder,
    quantum_view_encoder: QuantumViewEncoder,

    // Quantum attention mechanisms
    spatial_attention: QuantumSpatialAttention,
    view_attention: QuantumViewAttention,
    scale_attention: QuantumScaleAttention,

    // Quantum rendering components
    quantum_volume_renderer: QuantumVolumeRenderer,
    quantum_ray_marcher: QuantumRayMarcher,

    // Training state
    training_history: Vec<NeRFTrainingMetrics>,
    quantum_rendering_metrics: QuantumRenderingMetrics,
    optimization_state: NeRFOptimizationState,

    // Scene representation
    quantum_scene_representation: QuantumSceneRepresentation,
    quantum_light_field: QuantumLightField,
}

#[derive(Debug, Clone)]
pub struct QuantumMLP {
    layers: Vec<QuantumMLPLayer>,
    skip_connections: Vec<usize>,
    quantum_parameters: Array1<f64>,
    classical_parameters: Array2<f64>,
    quantum_enhancement_factor: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumMLPLayer {
    layer_type: QuantumMLPLayerType,
    input_dim: usize,
    output_dim: usize,
    quantum_gates: Vec<QuantumMLPGate>,
    activation: QuantumActivationType,
    normalization: Option<QuantumNormalizationType>,
}

#[derive(Debug, Clone)]
pub enum QuantumMLPLayerType {
    QuantumLinear,
    QuantumConvolutional3D {
        kernel_size: usize,
        stride: usize,
        padding: usize,
    },
    QuantumResidual {
        inner_layers: Vec<Box<QuantumMLPLayer>>,
    },
    QuantumAttentionLayer {
        attention_config: QuantumAttentionConfig,
    },
}

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

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

#[derive(Debug, Clone)]
pub enum QuantumMLPGateType {
    ParameterizedRotation { axis: RotationAxis },
    ControlledRotation { axis: RotationAxis },
    EntanglementGate { gate_name: String },
    QuantumFourierGate,
    CustomQuantumGate { matrix: Array2<Complex64> },
}

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

#[derive(Debug, Clone)]
pub struct QuantumPositionalEncoder {
    encoding_type: QuantumPositionalEncodingType,
    num_frequencies: usize,
    quantum_frequencies: Array1<f64>,
    entanglement_encoding: bool,
    phase_encoding: bool,
    max_frequency: f64,
}

#[derive(Debug, Clone)]
pub enum QuantumPositionalEncodingType {
    StandardQuantumEncoding,
    QuantumFourierEncoding,
    QuantumWaveletEncoding,
    EntanglementBasedEncoding,
    QuantumHashEncoding { hash_table_size: usize },
    QuantumMultiresolutionEncoding { num_levels: usize },
}

#[derive(Debug, Clone)]
pub struct QuantumViewEncoder {
    encoding_dimension: usize,
    quantum_view_embedding: Array2<Complex64>,
    spherical_harmonics_order: usize,
    quantum_spherical_harmonics: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumSpatialAttention {
    num_heads: usize,
    head_dim: usize,
    quantum_query_projection: Array2<Complex64>,
    quantum_key_projection: Array2<Complex64>,
    quantum_value_projection: Array2<Complex64>,
    entanglement_weights: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct QuantumViewAttention {
    view_embedding_dim: usize,
    quantum_view_weights: Array2<Complex64>,
    view_dependent_parameters: Array1<f64>,
    quantum_view_interpolation: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumScaleAttention {
    num_scales: usize,
    scale_weights: Array1<f64>,
    quantum_scale_mixing: Array2<Complex64>,
    adaptive_scale_selection: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumVolumeRenderer {
    rendering_equation: QuantumRenderingEquation,
    quantum_alpha_blending: QuantumAlphaBlending,
    quantum_illumination: QuantumIllumination,
    quantum_material_model: QuantumMaterialModel,
}

#[derive(Debug, Clone)]
pub enum QuantumRenderingEquation {
    StandardVolumeRendering,
    QuantumVolumeRendering {
        quantum_transmittance: bool,
        entangled_scattering: bool,
    },
    QuantumPathTracing {
        max_bounces: usize,
        quantum_importance_sampling: bool,
    },
    QuantumPhotonMapping {
        num_photons: usize,
        quantum_photon_transport: bool,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumAlphaBlending {
    blending_mode: QuantumBlendingMode,
    quantum_compositing: bool,
    entanglement_based_blending: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumBlendingMode {
    StandardAlphaBlending,
    QuantumSuperpositionBlending,
    EntanglementBasedBlending,
    QuantumInterferenceBlending,
}

#[derive(Debug, Clone)]
pub struct QuantumIllumination {
    light_sources: Vec<QuantumLightSource>,
    ambient_lighting: QuantumAmbientLight,
    quantum_shadows: bool,
    quantum_global_illumination: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumLightSource {
    position: Array1<f64>,
    intensity: Array1<f64>, // RGB
    light_type: QuantumLightType,
    quantum_coherence: f64,
}

#[derive(Debug, Clone)]
pub enum QuantumLightType {
    QuantumPointLight,
    QuantumDirectionalLight,
    QuantumAreaLight { area_size: Array1<f64> },
    QuantumEnvironmentLight { environment_map: Array3<f64> },
    QuantumCoherentLight { coherence_length: f64 },
}

#[derive(Debug, Clone)]
pub struct QuantumAmbientLight {
    ambient_color: Array1<f64>,
    quantum_ambient_occlusion: bool,
    quantum_environment_probe: Option<Array3<f64>>,
}

#[derive(Debug, Clone)]
pub struct QuantumMaterialModel {
    material_type: QuantumMaterialType,
    quantum_brdf: QuantumBRDF,
    material_parameters: QuantumMaterialParameters,
}

#[derive(Debug, Clone)]
pub enum QuantumMaterialType {
    Lambertian,
    Phong,
    PBR,
    QuantumMaterial {
        quantum_reflectance: Array2<Complex64>,
        quantum_transmittance: Array2<Complex64>,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumBRDF {
    brdf_type: QuantumBRDFType,
    quantum_parameters: Array1<Complex64>,
    view_dependent: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumBRDFType {
    LambertianBRDF,
    PhongBRDF,
    CookTorranceBRDF,
    QuantumBRDF {
        quantum_surface_model: Array2<Complex64>,
    },
}

#[derive(Debug, Clone)]
pub struct QuantumMaterialParameters {
    albedo: Array1<f64>,
    roughness: f64,
    metallic: f64,
    quantum_properties: QuantumMaterialProperties,
}

#[derive(Debug, Clone)]
pub struct QuantumMaterialProperties {
    quantum_reflectivity: Complex64,
    quantum_absorption: Complex64,
    quantum_scattering: Complex64,
    entanglement_factor: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumRayMarcher {
    marching_strategy: QuantumMarchingStrategy,
    quantum_sampling_points: Array2<f64>,
    entanglement_based_sampling: bool,
    adaptive_step_size: bool,
}

#[derive(Debug, Clone)]
pub enum QuantumMarchingStrategy {
    UniformMarching {
        step_size: f64,
    },
    AdaptiveMarching {
        initial_step_size: f64,
        min_step_size: f64,
        max_step_size: f64,
    },
    QuantumImportanceMarching {
        importance_threshold: f64,
        quantum_importance_estimation: bool,
    },
    EntanglementGuidedMarching {
        entanglement_threshold: f64,
        correlation_distance: f64,
    },
}

// Scene representation structures
#[derive(Debug, Clone)]
pub struct QuantumSceneRepresentation {
    voxel_grid: QuantumVoxelGrid,
    implicit_surface: QuantumImplicitSurface,
    quantum_octree: QuantumOctree,
    multi_scale_features: Vec<QuantumFeatureLevel>,
}

#[derive(Debug, Clone)]
pub struct QuantumVoxelGrid {
    density_grid: Array3<f64>,
    color_grid: Array4<f64>,             // [x, y, z, rgb]
    quantum_features: Array4<Complex64>, // [x, y, z, feature_dim]
    entanglement_structure: VoxelEntanglementStructure,
}

#[derive(Debug, Clone)]
pub struct VoxelEntanglementStructure {
    entanglement_matrix: Array2<f64>,
    correlation_radius: f64,
    entanglement_strength: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumImplicitSurface {
    sdf_function: QuantumSDF,
    gradient_function: QuantumGradientFunction,
    quantum_surface_properties: QuantumSurfaceProperties,
}

#[derive(Debug, Clone)]
pub struct QuantumSDF {
    quantum_parameters: Array1<f64>,
    quantum_basis_functions: Vec<QuantumBasisFunction>,
    multi_resolution_levels: usize,
}

#[derive(Debug, Clone)]
pub struct QuantumBasisFunction {
    basis_type: QuantumBasisType,
    parameters: Array1<Complex64>,
    support_region: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum QuantumBasisType {
    QuantumRadialBasis { sigma: f64 },
    QuantumWavelet { wavelet_type: String },
    QuantumFourier { frequency: f64 },
    QuantumSpline { order: usize },
}

#[derive(Debug, Clone)]
pub struct QuantumGradientFunction {
    gradient_quantum_mlp: QuantumMLP,
    analytical_gradients: bool,
    quantum_finite_differences: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumSurfaceProperties {
    surface_normal: Array1<f64>,
    curvature: f64,
    quantum_surface_features: Array1<Complex64>,
}

#[derive(Debug, Clone)]
pub struct QuantumOctree {
    root: QuantumOctreeNode,
    max_depth: usize,
    quantum_subdivision_criterion: QuantumSubdivisionCriterion,
}

#[derive(Debug, Clone)]
pub struct QuantumOctreeNode {
    bounds: SceneBounds,
    children: Option<Box<[QuantumOctreeNode; 8]>>,
    quantum_features: Array1<Complex64>,
    occupancy_probability: f64,
    entanglement_with_neighbors: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum QuantumSubdivisionCriterion {
    DensityThreshold { threshold: f64 },
    QuantumUncertainty { uncertainty_threshold: f64 },
    EntanglementComplexity { complexity_threshold: f64 },
    AdaptiveQuantum { adaptive_parameters: Array1<f64> },
}

#[derive(Debug, Clone)]
pub struct QuantumFeatureLevel {
    level: usize,
    resolution: Array1<usize>,
    quantum_features: Array4<Complex64>,
    downsampling_operator: QuantumDownsampling,
    upsampling_operator: QuantumUpsampling,
}

#[derive(Debug, Clone)]
pub enum QuantumDownsampling {
    QuantumAveragePooling,
    QuantumMaxPooling,
    QuantumAttentionPooling,
    EntanglementBasedPooling,
}

#[derive(Debug, Clone)]
pub enum QuantumUpsampling {
    QuantumBilinearInterpolation,
    QuantumTransposedConvolution,
    QuantumAttentionUpsampling,
    EntanglementBasedUpsampling,
}

#[derive(Debug, Clone)]
pub struct QuantumLightField {
    light_directions: Array2<f64>,  // [num_directions, 3]
    light_intensities: Array2<f64>, // [num_directions, 3] (RGB)
    quantum_light_coherence: Array2<Complex64>,
    spherical_harmonics_coefficients: Array2<f64>,
    quantum_environment_encoding: QuantumEnvironmentEncoding,
}

#[derive(Debug, Clone)]
pub struct QuantumEnvironmentEncoding {
    encoding_type: QuantumEnvironmentEncodingType,
    quantum_coefficients: Array1<Complex64>,
    spatial_frequency_components: Array1<f64>,
}

#[derive(Debug, Clone)]
pub enum QuantumEnvironmentEncodingType {
    SphericalHarmonics,
    QuantumSphericalHarmonics,
    QuantumWavelets,
    QuantumFourierSeries,
}

// Training and metrics structures
#[derive(Debug, Clone)]
pub struct NeRFTrainingMetrics {
    pub epoch: usize,
    pub loss: f64,
    pub psnr: f64,
    pub ssim: f64,
    pub lpips: f64,
    pub quantum_fidelity: f64,
    pub entanglement_measure: f64,
    pub rendering_time: f64,
    pub quantum_advantage_ratio: f64,
    pub memory_usage: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumRenderingMetrics {
    pub average_rendering_time: f64,
    pub quantum_acceleration_factor: f64,
    pub entanglement_utilization: f64,
    pub coherence_preservation: f64,
    pub quantum_memory_efficiency: f64,
    pub view_synthesis_quality: f64,
    pub volumetric_accuracy: f64,
}

#[derive(Debug, Clone)]
pub struct NeRFOptimizationState {
    pub learning_rate: f64,
    pub momentum: f64,
    pub quantum_parameter_learning_rate: f64,
    pub adaptive_sampling_rate: f64,
    pub entanglement_preservation_weight: f64,
    pub rendering_loss_weight: f64,
}

// Main implementation
impl QuantumNeRF {
    /// Create a new Quantum Neural Radiance Field
    pub fn new(config: QuantumNeRFConfig) -> Result<Self> {
        println!("🌌 Initializing Quantum Neural Radiance Fields in UltraThink Mode");

        // Initialize quantum MLP networks
        let quantum_mlp_coarse = Self::create_quantum_mlp(&config, "coarse")?;
        let quantum_mlp_fine = Self::create_quantum_mlp(&config, "fine")?;

        // Initialize encoders
        let quantum_positional_encoder = Self::create_quantum_positional_encoder(&config)?;
        let quantum_view_encoder = Self::create_quantum_view_encoder(&config)?;

        // Initialize attention mechanisms
        let spatial_attention = Self::create_spatial_attention(&config)?;
        let view_attention = Self::create_view_attention(&config)?;
        let scale_attention = Self::create_scale_attention(&config)?;

        // Initialize rendering components
        let quantum_volume_renderer = Self::create_quantum_volume_renderer(&config)?;
        let quantum_ray_marcher = Self::create_quantum_ray_marcher(&config)?;

        // Initialize scene representation
        let quantum_scene_representation = Self::create_quantum_scene_representation(&config)?;
        let quantum_light_field = Self::create_quantum_light_field(&config)?;

        // Initialize metrics and optimization
        let quantum_rendering_metrics = QuantumRenderingMetrics::default();
        let optimization_state = NeRFOptimizationState::default();

        Ok(Self {
            config,
            quantum_mlp_coarse,
            quantum_mlp_fine,
            quantum_positional_encoder,
            quantum_view_encoder,
            spatial_attention,
            view_attention,
            scale_attention,
            quantum_volume_renderer,
            quantum_ray_marcher,
            training_history: Vec::new(),
            quantum_rendering_metrics,
            optimization_state,
            quantum_scene_representation,
            quantum_light_field,
        })
    }

    /// Create quantum MLP network
    fn create_quantum_mlp(config: &QuantumNeRFConfig, network_type: &str) -> Result<QuantumMLP> {
        let (hidden_dims, output_dim) = match network_type {
            "coarse" => (vec![256, 256, 256, 256], 4), // RGB + density
            "fine" => (vec![256, 256, 256, 256, 256, 256], 4),
            _ => (vec![128, 128], 4),
        };

        let mut layers = Vec::new();
        let mut input_dim = 3 + config.quantum_encoding_levels * 6; // 3D position + quantum encoding

        // Add view direction encoding if using view-dependent rendering
        if config.quantum_view_synthesis {
            input_dim += 3 + config.quantum_encoding_levels * 6; // 3D view direction + encoding
        }

        for (i, &hidden_dim) in hidden_dims.iter().enumerate() {
            let layer = QuantumMLPLayer {
                layer_type: QuantumMLPLayerType::QuantumLinear,
                input_dim: if i == 0 {
                    input_dim
                } else {
                    hidden_dims[i - 1]
                },
                output_dim: hidden_dim,
                quantum_gates: Self::create_quantum_mlp_gates(config, hidden_dim)?,
                activation: QuantumActivationType::QuantumReLU,
                normalization: Some(QuantumNormalizationType::QuantumLayerNorm),
            };
            layers.push(layer);
        }

        // Output layer
        let output_layer = QuantumMLPLayer {
            layer_type: QuantumMLPLayerType::QuantumLinear,
            input_dim: *hidden_dims.last().unwrap(),
            output_dim,
            quantum_gates: Self::create_quantum_mlp_gates(config, output_dim)?,
            activation: QuantumActivationType::QuantumSigmoid, // For colors and density
            normalization: None,
        };
        layers.push(output_layer);

        // Skip connections (commonly used in NeRF)
        let skip_connections = vec![layers.len() / 2]; // Middle layer skip connection

        Ok(QuantumMLP {
            layers,
            skip_connections,
            quantum_parameters: Array1::zeros(config.num_qubits * 3),
            classical_parameters: Array2::zeros((input_dim, hidden_dims[0])),
            quantum_enhancement_factor: config.quantum_enhancement_level,
        })
    }

    /// Create quantum MLP gates for a layer
    fn create_quantum_mlp_gates(
        config: &QuantumNeRFConfig,
        layer_dim: usize,
    ) -> Result<Vec<QuantumMLPGate>> {
        let mut gates = Vec::new();

        // Add parameterized rotation gates
        for i in 0..config.num_qubits {
            gates.push(QuantumMLPGate {
                gate_type: QuantumMLPGateType::ParameterizedRotation {
                    axis: RotationAxis::Y,
                },
                target_qubits: vec![i],
                control_qubits: Vec::new(),
                parameters: Array1::from_vec(vec![PI / 4.0]),
                is_trainable: true,
            });
        }

        // Add entanglement gates
        for i in 0..config.num_qubits - 1 {
            gates.push(QuantumMLPGate {
                gate_type: QuantumMLPGateType::EntanglementGate {
                    gate_name: "CNOT".to_string(),
                },
                target_qubits: vec![i + 1],
                control_qubits: vec![i],
                parameters: Array1::zeros(0),
                is_trainable: false,
            });
        }

        Ok(gates)
    }

    /// Create quantum positional encoder
    fn create_quantum_positional_encoder(
        config: &QuantumNeRFConfig,
    ) -> Result<QuantumPositionalEncoder> {
        let max_frequency = 2.0_f64.powi(config.quantum_encoding_levels as i32 - 1);
        let quantum_frequencies = Array1::from_shape_fn(config.quantum_encoding_levels, |i| {
            2.0_f64.powi(i as i32) * PI
        });

        Ok(QuantumPositionalEncoder {
            encoding_type: QuantumPositionalEncodingType::QuantumFourierEncoding,
            num_frequencies: config.quantum_encoding_levels,
            quantum_frequencies,
            entanglement_encoding: config.entanglement_based_interpolation,
            phase_encoding: true,
            max_frequency,
        })
    }

    /// Create quantum view encoder
    fn create_quantum_view_encoder(config: &QuantumNeRFConfig) -> Result<QuantumViewEncoder> {
        let encoding_dimension = config.quantum_encoding_levels * 6; // 3 directions * 2 (sin, cos) * encoding levels

        // Initialize quantum view embedding (simplified)
        let quantum_view_embedding = Array2::zeros((encoding_dimension, config.num_qubits))
            .mapv(|_: f64| Complex64::new(1.0, 0.0));

        Ok(QuantumViewEncoder {
            encoding_dimension,
            quantum_view_embedding,
            spherical_harmonics_order: 4, // Standard order for view-dependent effects
            quantum_spherical_harmonics: config.quantum_view_synthesis,
        })
    }

    /// Create spatial attention
    fn create_spatial_attention(config: &QuantumNeRFConfig) -> Result<QuantumSpatialAttention> {
        let num_heads = config.quantum_attention_config.num_attention_heads;
        let head_dim = config.num_qubits / num_heads;

        // Calculate the actual input feature dimension
        let mut input_dim = 3 + config.quantum_encoding_levels * 6; // 3D position + quantum encoding
        if config.quantum_view_synthesis {
            input_dim += 3 + config.quantum_encoding_levels * 6; // 3D view direction + encoding
        }

        Ok(QuantumSpatialAttention {
            num_heads,
            head_dim,
            quantum_query_projection: Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0)),
            quantum_key_projection: Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0)),
            quantum_value_projection: Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0)),
            entanglement_weights: Array1::ones(num_heads) * 0.5,
        })
    }

    /// Create view attention
    fn create_view_attention(config: &QuantumNeRFConfig) -> Result<QuantumViewAttention> {
        let view_embedding_dim = config.quantum_encoding_levels * 6;

        Ok(QuantumViewAttention {
            view_embedding_dim,
            quantum_view_weights: Array2::eye(view_embedding_dim).mapv(|x| Complex64::new(x, 0.0)),
            view_dependent_parameters: Array1::ones(view_embedding_dim),
            quantum_view_interpolation: config.quantum_view_synthesis,
        })
    }

    /// Create scale attention
    fn create_scale_attention(config: &QuantumNeRFConfig) -> Result<QuantumScaleAttention> {
        let num_scales = if config.quantum_multiscale_features {
            4
        } else {
            1
        };

        Ok(QuantumScaleAttention {
            num_scales,
            scale_weights: Array1::ones(num_scales) / num_scales as f64,
            quantum_scale_mixing: Array2::eye(num_scales).mapv(|x| Complex64::new(x, 0.0)),
            adaptive_scale_selection: config.quantum_multiscale_features,
        })
    }

    /// Create quantum volume renderer
    fn create_quantum_volume_renderer(config: &QuantumNeRFConfig) -> Result<QuantumVolumeRenderer> {
        let rendering_equation = QuantumRenderingEquation::QuantumVolumeRendering {
            quantum_transmittance: true,
            entangled_scattering: config.entanglement_based_interpolation,
        };

        let quantum_alpha_blending = QuantumAlphaBlending {
            blending_mode: QuantumBlendingMode::QuantumSuperpositionBlending,
            quantum_compositing: true,
            entanglement_based_blending: config.entanglement_based_interpolation,
        };

        let quantum_illumination = QuantumIllumination {
            light_sources: Vec::new(), // Will be populated during training
            ambient_lighting: QuantumAmbientLight {
                ambient_color: Array1::from_vec(vec![0.1, 0.1, 0.1]),
                quantum_ambient_occlusion: true,
                quantum_environment_probe: None,
            },
            quantum_shadows: true,
            quantum_global_illumination: config.volumetric_rendering_config.quantum_light_transport,
        };

        let quantum_material_model = QuantumMaterialModel {
            material_type: QuantumMaterialType::QuantumMaterial {
                quantum_reflectance: Array2::eye(3).mapv(|x: f64| Complex64::new(x, 0.0)),
                quantum_transmittance: Array2::eye(3).mapv(|x: f64| Complex64::new(x * 0.5, 0.0)),
            },
            quantum_brdf: QuantumBRDF {
                brdf_type: QuantumBRDFType::QuantumBRDF {
                    quantum_surface_model: Array2::eye(3).mapv(|x| Complex64::new(x, 0.0)),
                },
                quantum_parameters: Array1::ones(8).mapv(|x| Complex64::new(x, 0.0)),
                view_dependent: config.quantum_view_synthesis,
            },
            material_parameters: QuantumMaterialParameters {
                albedo: Array1::from_vec(vec![0.8, 0.8, 0.8]),
                roughness: 0.1,
                metallic: 0.0,
                quantum_properties: QuantumMaterialProperties {
                    quantum_reflectivity: Complex64::new(0.9, 0.1),
                    quantum_absorption: Complex64::new(0.05, 0.0),
                    quantum_scattering: Complex64::new(0.1, 0.0),
                    entanglement_factor: config.entanglement_based_interpolation as i32 as f64,
                },
            },
        };

        Ok(QuantumVolumeRenderer {
            rendering_equation,
            quantum_alpha_blending,
            quantum_illumination,
            quantum_material_model,
        })
    }

    /// Create quantum ray marcher
    fn create_quantum_ray_marcher(config: &QuantumNeRFConfig) -> Result<QuantumRayMarcher> {
        let marching_strategy = match &config.quantum_sampling_strategy {
            QuantumSamplingStrategy::QuantumUniform {
                min_samples,
                max_samples,
                quantum_jitter,
            } => QuantumMarchingStrategy::UniformMarching {
                step_size: 1.0 / *max_samples as f64,
            },
            QuantumSamplingStrategy::QuantumAdaptive {
                initial_samples,
                max_refinements,
                uncertainty_threshold,
                quantum_uncertainty_estimation,
            } => QuantumMarchingStrategy::AdaptiveMarching {
                initial_step_size: 1.0 / *initial_samples as f64,
                min_step_size: 1e-4,
                max_step_size: 1e-1,
            },
            _ => QuantumMarchingStrategy::UniformMarching {
                step_size: 1.0 / 64.0,
            },
        };

        Ok(QuantumRayMarcher {
            marching_strategy,
            quantum_sampling_points: Array2::zeros((config.max_ray_samples, 3)),
            entanglement_based_sampling: config.entanglement_based_interpolation,
            adaptive_step_size: true,
        })
    }

    /// Create quantum scene representation
    fn create_quantum_scene_representation(
        config: &QuantumNeRFConfig,
    ) -> Result<QuantumSceneRepresentation> {
        let voxel_resolution = &config.scene_bounds.voxel_resolution;

        // Initialize voxel grid
        let voxel_grid = QuantumVoxelGrid {
            density_grid: Array3::zeros((
                voxel_resolution[0],
                voxel_resolution[1],
                voxel_resolution[2],
            )),
            color_grid: Array4::zeros((
                voxel_resolution[0],
                voxel_resolution[1],
                voxel_resolution[2],
                3,
            )),
            quantum_features: Array4::zeros((
                voxel_resolution[0],
                voxel_resolution[1],
                voxel_resolution[2],
                config.num_qubits,
            ))
            .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            entanglement_structure: VoxelEntanglementStructure {
                entanglement_matrix: Array2::eye(voxel_resolution.iter().product()),
                correlation_radius: 2.0,
                entanglement_strength: config.quantum_enhancement_level,
            },
        };

        // Initialize implicit surface
        let implicit_surface = QuantumImplicitSurface {
            sdf_function: QuantumSDF {
                quantum_parameters: Array1::zeros(config.num_qubits * 3),
                quantum_basis_functions: Vec::new(),
                multi_resolution_levels: 4,
            },
            gradient_function: QuantumGradientFunction {
                gradient_quantum_mlp: Self::create_quantum_mlp(config, "gradient")?,
                analytical_gradients: true,
                quantum_finite_differences: false,
            },
            quantum_surface_properties: QuantumSurfaceProperties {
                surface_normal: Array1::zeros(3),
                curvature: 0.0,
                quantum_surface_features: Array1::zeros(config.num_qubits)
                    .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            },
        };

        // Initialize quantum octree
        let quantum_octree = QuantumOctree {
            root: QuantumOctreeNode {
                bounds: config.scene_bounds.clone(),
                children: None,
                quantum_features: Array1::zeros(config.num_qubits)
                    .mapv(|_: f64| Complex64::new(0.0, 0.0)),
                occupancy_probability: 0.5,
                entanglement_with_neighbors: Array1::zeros(8),
            },
            max_depth: 8,
            quantum_subdivision_criterion: QuantumSubdivisionCriterion::QuantumUncertainty {
                uncertainty_threshold: 0.1,
            },
        };

        // Initialize multi-scale features
        let mut multi_scale_features = Vec::new();
        for level in 0..4 {
            let scale_factor = 2_usize.pow(level as u32);
            let level_resolution = Array1::from_vec(vec![
                voxel_resolution[0] / scale_factor,
                voxel_resolution[1] / scale_factor,
                voxel_resolution[2] / scale_factor,
            ]);

            multi_scale_features.push(QuantumFeatureLevel {
                level,
                resolution: level_resolution.clone(),
                quantum_features: Array4::zeros((
                    level_resolution[0],
                    level_resolution[1],
                    level_resolution[2],
                    config.num_qubits,
                ))
                .mapv(|_: f64| Complex64::new(0.0, 0.0)),
                downsampling_operator: QuantumDownsampling::QuantumAveragePooling,
                upsampling_operator: QuantumUpsampling::QuantumBilinearInterpolation,
            });
        }

        Ok(QuantumSceneRepresentation {
            voxel_grid,
            implicit_surface,
            quantum_octree,
            multi_scale_features,
        })
    }

    /// Create quantum light field
    fn create_quantum_light_field(config: &QuantumNeRFConfig) -> Result<QuantumLightField> {
        let num_directions = 256; // Standard light probe resolution

        // Generate uniform sphere sampling
        let mut light_directions = Array2::zeros((num_directions, 3));
        let mut rng = rand::thread_rng();

        for i in 0..num_directions {
            let theta = rng.gen::<f64>() * 2.0 * PI;
            let phi = (rng.gen::<f64>() * 2.0 - 1.0).acos();

            light_directions[[i, 0]] = phi.sin() * theta.cos();
            light_directions[[i, 1]] = phi.sin() * theta.sin();
            light_directions[[i, 2]] = phi.cos();
        }

        let light_intensities = Array2::ones((num_directions, 3)) * 0.5; // Default ambient lighting
        let quantum_light_coherence =
            Array2::zeros((num_directions, 3)).mapv(|_: f64| Complex64::new(1.0, 0.0));

        // Initialize spherical harmonics coefficients (up to order 4)
        let num_sh_coefficients = (4u32 + 1).pow(2) as usize; // (L+1)^2
        let spherical_harmonics_coefficients = Array2::zeros((num_sh_coefficients, 3));

        Ok(QuantumLightField {
            light_directions,
            light_intensities,
            quantum_light_coherence,
            spherical_harmonics_coefficients,
            quantum_environment_encoding: QuantumEnvironmentEncoding {
                encoding_type: QuantumEnvironmentEncodingType::QuantumSphericalHarmonics,
                quantum_coefficients: Array1::<f64>::zeros(num_sh_coefficients)
                    .mapv(|_| Complex64::new(0.0, 0.0)),
                spatial_frequency_components: Array1::zeros(num_sh_coefficients),
            },
        })
    }

    /// Render image from camera viewpoint
    pub fn render(
        &self,
        camera_position: &Array1<f64>,
        camera_direction: &Array1<f64>,
        camera_up: &Array1<f64>,
        image_width: usize,
        image_height: usize,
        fov: f64,
    ) -> Result<QuantumRenderOutput> {
        println!("🎨 Rendering with Quantum Neural Radiance Fields");

        let mut rendered_image = Array3::zeros((image_height, image_width, 3));
        let mut quantum_depth_map = Array2::zeros((image_height, image_width));
        let mut quantum_uncertainty_map = Array2::zeros((image_height, image_width));
        let mut pixel_quantum_states = Vec::new();

        // Camera matrix setup
        let camera_matrix =
            self.setup_camera_matrix(camera_position, camera_direction, camera_up, fov)?;

        // Render each pixel
        for y in 0..image_height {
            for x in 0..image_width {
                // Generate ray for this pixel
                let ray =
                    self.generate_camera_ray(&camera_matrix, x, y, image_width, image_height, fov)?;

                // Quantum ray marching and rendering
                let pixel_output = self.render_pixel_quantum(&ray)?;

                // Store results
                rendered_image[[y, x, 0]] = pixel_output.color[0];
                rendered_image[[y, x, 1]] = pixel_output.color[1];
                rendered_image[[y, x, 2]] = pixel_output.color[2];
                quantum_depth_map[[y, x]] = pixel_output.depth;
                quantum_uncertainty_map[[y, x]] = pixel_output.quantum_uncertainty;
                pixel_quantum_states.push(pixel_output.quantum_state);
            }
        }

        // Compute rendering metrics
        let rendering_metrics =
            self.compute_rendering_metrics(&rendered_image, &pixel_quantum_states)?;

        Ok(QuantumRenderOutput {
            rendered_image,
            quantum_depth_map,
            quantum_uncertainty_map,
            pixel_quantum_states,
            rendering_metrics,
        })
    }

    /// Setup camera matrix
    fn setup_camera_matrix(
        &self,
        position: &Array1<f64>,
        direction: &Array1<f64>,
        up: &Array1<f64>,
        fov: f64,
    ) -> Result<CameraMatrix> {
        // Normalize direction and up vectors
        let forward = direction / direction.dot(direction).sqrt();
        let right = Self::cross_product(&forward, up);
        let right = &right / right.dot(&right).sqrt();
        let up_corrected = Self::cross_product(&right, &forward);

        Ok(CameraMatrix {
            position: position.clone(),
            forward,
            right,
            up: up_corrected,
            fov,
        })
    }

    /// Cross product helper
    fn cross_product(a: &Array1<f64>, b: &Array1<f64>) -> Array1<f64> {
        Array1::from_vec(vec![
            a[1] * b[2] - a[2] * b[1],
            a[2] * b[0] - a[0] * b[2],
            a[0] * b[1] - a[1] * b[0],
        ])
    }

    /// Generate camera ray for pixel
    fn generate_camera_ray(
        &self,
        camera: &CameraMatrix,
        pixel_x: usize,
        pixel_y: usize,
        image_width: usize,
        image_height: usize,
        fov: f64,
    ) -> Result<Ray> {
        // Convert pixel coordinates to normalized device coordinates
        let aspect_ratio = image_width as f64 / image_height as f64;
        let ndc_x = (2.0 * pixel_x as f64 / image_width as f64 - 1.0) * aspect_ratio;
        let ndc_y = 1.0 - 2.0 * pixel_y as f64 / image_height as f64;

        // Convert to camera space
        let tan_half_fov = (fov / 2.0).tan();
        let camera_x = ndc_x * tan_half_fov;
        let camera_y = ndc_y * tan_half_fov;

        // Compute ray direction in world space
        let ray_direction = &camera.forward + camera_x * &camera.right + camera_y * &camera.up;
        let ray_direction = &ray_direction / ray_direction.dot(&ray_direction).sqrt();

        Ok(Ray {
            origin: camera.position.clone(),
            direction: ray_direction,
            near: 0.1,
            far: 10.0,
        })
    }

    /// Render single pixel using quantum ray marching
    fn render_pixel_quantum(&self, ray: &Ray) -> Result<PixelRenderOutput> {
        // Generate quantum sampling points along ray
        let sampling_points = self.quantum_ray_sampling(ray)?;

        // Query quantum MLP at sampling points
        let mut colors = Vec::new();
        let mut densities = Vec::new();
        let mut quantum_states = Vec::new();

        for point in &sampling_points.points {
            // Quantum positional encoding
            let encoded_position = self.quantum_positional_encoding(&point.position)?;
            let encoded_view = self.quantum_view_encoding(&ray.direction)?;

            // Combine encodings
            let mut input_features = encoded_position.features;
            input_features
                .append(Axis(0), encoded_view.features.view())
                .unwrap();

            // Apply quantum attention
            let attended_features =
                self.apply_quantum_spatial_attention(&input_features, &point.position)?;

            // Query coarse network
            let coarse_output =
                self.query_quantum_mlp(&self.quantum_mlp_coarse, &attended_features)?;

            // Fine network (hierarchical sampling)
            let fine_output = if sampling_points.is_hierarchical {
                Some(self.query_quantum_mlp(&self.quantum_mlp_fine, &attended_features)?)
            } else {
                None
            };

            // Use fine output if available, otherwise coarse
            let output = fine_output.as_ref().unwrap_or(&coarse_output);

            colors.push(output.color.clone());
            densities.push(output.density);
            quantum_states.push(output.quantum_state.clone());
        }

        // Quantum volume rendering
        let volume_render_output = self.quantum_volume_rendering(
            &colors,
            &densities,
            &quantum_states,
            &sampling_points.distances,
        )?;

        Ok(PixelRenderOutput {
            color: volume_render_output.final_color,
            depth: volume_render_output.depth,
            quantum_uncertainty: volume_render_output.quantum_uncertainty,
            quantum_state: volume_render_output.accumulated_quantum_state,
        })
    }

    /// Quantum ray sampling
    fn quantum_ray_sampling(&self, ray: &Ray) -> Result<QuantumSamplingOutput> {
        let mut sampling_points = Vec::new();
        let mut distances = Vec::new();
        let is_hierarchical = matches!(
            self.config.quantum_sampling_strategy,
            QuantumSamplingStrategy::QuantumHierarchical { .. }
        );

        match &self.config.quantum_sampling_strategy {
            QuantumSamplingStrategy::QuantumUniform {
                min_samples,
                max_samples,
                quantum_jitter,
            } => {
                let num_samples = *max_samples;
                for i in 0..num_samples {
                    let t = ray.near + (ray.far - ray.near) * i as f64 / (num_samples - 1) as f64;

                    // Add quantum jitter
                    let mut rng = rand::thread_rng();
                    let jitter = (rng.gen::<f64>() - 0.5) * quantum_jitter;
                    let t_jittered = t + jitter;

                    let position = &ray.origin + t_jittered * &ray.direction;
                    sampling_points.push(SamplingPoint {
                        position,
                        quantum_weight: 1.0,
                        entanglement_correlation: 0.0,
                    });
                    distances.push(t_jittered);
                }
            }

            QuantumSamplingStrategy::QuantumHierarchical {
                coarse_samples,
                fine_samples,
                quantum_importance_threshold,
            } => {
                // Coarse sampling
                for i in 0..*coarse_samples {
                    let t =
                        ray.near + (ray.far - ray.near) * i as f64 / (*coarse_samples - 1) as f64;
                    let position = &ray.origin + t * &ray.direction;
                    sampling_points.push(SamplingPoint {
                        position,
                        quantum_weight: 1.0,
                        entanglement_correlation: 0.0,
                    });
                    distances.push(t);
                }

                // TODO: Implement fine sampling based on coarse results
                // This would require initial coarse evaluation and importance-based refinement
            }

            QuantumSamplingStrategy::EntanglementCorrelated {
                base_samples,
                correlation_strength,
                entanglement_radius,
            } => {
                // Generate correlated sampling points using quantum entanglement
                let mut rng = rand::thread_rng();

                for i in 0..*base_samples {
                    let base_t =
                        ray.near + (ray.far - ray.near) * i as f64 / (*base_samples - 1) as f64;

                    // Add entanglement correlation
                    let correlation = if i > 0 {
                        correlation_strength
                            * (-(distances[i - 1] - base_t).abs() / entanglement_radius).exp()
                    } else {
                        0.0
                    };

                    let position = &ray.origin + base_t * &ray.direction;
                    sampling_points.push(SamplingPoint {
                        position,
                        quantum_weight: 1.0,
                        entanglement_correlation: correlation,
                    });
                    distances.push(base_t);
                }
            }

            _ => {
                // Default uniform sampling
                let num_samples = self.config.max_ray_samples;
                for i in 0..num_samples {
                    let t = ray.near + (ray.far - ray.near) * i as f64 / (num_samples - 1) as f64;
                    let position = &ray.origin + t * &ray.direction;
                    sampling_points.push(SamplingPoint {
                        position,
                        quantum_weight: 1.0,
                        entanglement_correlation: 0.0,
                    });
                    distances.push(t);
                }
            }
        }

        Ok(QuantumSamplingOutput {
            points: sampling_points,
            distances,
            is_hierarchical,
        })
    }

    /// Quantum positional encoding
    fn quantum_positional_encoding(&self, position: &Array1<f64>) -> Result<QuantumEncodingOutput> {
        match self.quantum_positional_encoder.encoding_type {
            QuantumPositionalEncodingType::QuantumFourierEncoding => {
                self.quantum_fourier_encoding(position)
            }
            QuantumPositionalEncodingType::EntanglementBasedEncoding => {
                self.entanglement_based_encoding(position)
            }
            _ => self.standard_quantum_encoding(position),
        }
    }

    /// Standard quantum encoding
    fn standard_quantum_encoding(&self, position: &Array1<f64>) -> Result<QuantumEncodingOutput> {
        let mut features = Vec::new();

        // Add original coordinates
        features.extend_from_slice(position.as_slice().unwrap());

        // Add quantum Fourier features
        for (i, &freq) in self
            .quantum_positional_encoder
            .quantum_frequencies
            .iter()
            .enumerate()
        {
            for &coord in position.iter() {
                features.push((freq * coord).sin());
                features.push((freq * coord).cos());

                // Add quantum phase encoding if enabled
                if self.quantum_positional_encoder.phase_encoding {
                    let quantum_phase = Complex64::from_polar(1.0, freq * coord);
                    features.push(quantum_phase.re);
                    features.push(quantum_phase.im);
                }
            }
        }

        Ok(QuantumEncodingOutput {
            features: Array1::from_vec(features),
            quantum_amplitudes: Array1::zeros(self.config.num_qubits)
                .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            entanglement_measure: 0.5,
        })
    }

    /// Quantum Fourier encoding
    fn quantum_fourier_encoding(&self, position: &Array1<f64>) -> Result<QuantumEncodingOutput> {
        let mut features = Vec::new();
        let mut quantum_amplitudes = Array1::zeros(self.config.num_qubits);

        // Quantum Fourier transform of position
        for (i, &freq) in self
            .quantum_positional_encoder
            .quantum_frequencies
            .iter()
            .enumerate()
        {
            let fourier_coefficient = position
                .iter()
                .enumerate()
                .map(|(j, &coord)| Complex64::from_polar(1.0, freq * coord * (j + 1) as f64))
                .sum::<Complex64>()
                / position.len() as f64;

            features.push(fourier_coefficient.re);
            features.push(fourier_coefficient.im);

            // Store quantum amplitude
            if i < quantum_amplitudes.len() {
                quantum_amplitudes[i] = fourier_coefficient;
            }
        }

        Ok(QuantumEncodingOutput {
            features: Array1::from_vec(features),
            quantum_amplitudes,
            entanglement_measure: 0.7,
        })
    }

    /// Entanglement-based encoding
    fn entanglement_based_encoding(&self, position: &Array1<f64>) -> Result<QuantumEncodingOutput> {
        let mut features = Vec::new();
        let mut quantum_amplitudes = Array1::zeros(self.config.num_qubits);

        // Create entangled encoding
        for i in 0..self.config.num_qubits {
            for j in i + 1..self.config.num_qubits {
                // Entanglement between qubits i and j based on position
                let entanglement_strength =
                    (position[i % position.len()] * position[j % position.len()]).abs();
                let entangled_amplitude = Complex64::from_polar(
                    entanglement_strength.sqrt(),
                    position.iter().sum::<f64>() * (i + j) as f64,
                );

                features.push(entangled_amplitude.re);
                features.push(entangled_amplitude.im);

                quantum_amplitudes[i] += entangled_amplitude * 0.5;
                quantum_amplitudes[j] += entangled_amplitude.conj() * 0.5;
            }
        }

        // Normalize quantum amplitudes
        let norm = quantum_amplitudes
            .dot(&quantum_amplitudes.mapv(|x: Complex64| x.conj()))
            .norm();
        if norm > 1e-10 {
            quantum_amplitudes = quantum_amplitudes / norm;
        }

        Ok(QuantumEncodingOutput {
            features: Array1::from_vec(features),
            quantum_amplitudes,
            entanglement_measure: 0.9,
        })
    }

    /// Quantum view encoding
    fn quantum_view_encoding(&self, view_direction: &Array1<f64>) -> Result<QuantumEncodingOutput> {
        // Normalize view direction
        let normalized_view = view_direction / view_direction.dot(view_direction).sqrt();

        if self.quantum_view_encoder.quantum_spherical_harmonics {
            self.quantum_spherical_harmonics_encoding(&normalized_view)
        } else {
            self.standard_view_encoding(&normalized_view)
        }
    }

    /// Standard view encoding
    fn standard_view_encoding(
        &self,
        view_direction: &Array1<f64>,
    ) -> Result<QuantumEncodingOutput> {
        let mut features = Vec::new();

        // Add normalized view direction
        features.extend_from_slice(view_direction.as_slice().unwrap());

        // Add frequency encodings
        for &freq in self.quantum_positional_encoder.quantum_frequencies.iter() {
            for &component in view_direction.iter() {
                features.push((freq * component).sin());
                features.push((freq * component).cos());
            }
        }

        Ok(QuantumEncodingOutput {
            features: Array1::from_vec(features),
            quantum_amplitudes: Array1::zeros(self.config.num_qubits)
                .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            entanglement_measure: 0.3,
        })
    }

    /// Quantum spherical harmonics encoding
    fn quantum_spherical_harmonics_encoding(
        &self,
        view_direction: &Array1<f64>,
    ) -> Result<QuantumEncodingOutput> {
        // Convert Cartesian to spherical coordinates
        let x = view_direction[0];
        let y = view_direction[1];
        let z = view_direction[2];

        let theta = z.acos(); // polar angle
        let phi = y.atan2(x); // azimuthal angle

        let mut features = Vec::new();
        let mut quantum_amplitudes = Array1::zeros(self.config.num_qubits);

        // Compute quantum spherical harmonics up to specified order
        for l in 0..=self.quantum_view_encoder.spherical_harmonics_order {
            for m in -(l as i32)..=(l as i32) {
                let sh_value = self.compute_quantum_spherical_harmonic(l, m, theta, phi)?;

                features.push(sh_value.re);
                features.push(sh_value.im);

                // Store in quantum amplitudes
                let idx = l * (l + 1) + (m + l as i32) as usize;
                if idx < quantum_amplitudes.len() {
                    quantum_amplitudes[idx] = sh_value;
                }
            }
        }

        Ok(QuantumEncodingOutput {
            features: Array1::from_vec(features),
            quantum_amplitudes,
            entanglement_measure: 0.8,
        })
    }

    /// Compute quantum spherical harmonic
    fn compute_quantum_spherical_harmonic(
        &self,
        l: usize,
        m: i32,
        theta: f64,
        phi: f64,
    ) -> Result<Complex64> {
        // Simplified quantum spherical harmonics (real implementation would be more complex)
        let associated_legendre =
            self.compute_associated_legendre(l, m.abs() as usize, theta.cos());
        let normalization = self.compute_spherical_harmonic_normalization(l, m.abs() as usize);
        let phase = Complex64::from_polar(1.0, m as f64 * phi);

        let quantum_enhancement = 1.0 + self.config.quantum_enhancement_level * 0.1;

        Ok(normalization * associated_legendre * phase * quantum_enhancement)
    }

    /// Compute associated Legendre polynomial (simplified)
    fn compute_associated_legendre(&self, l: usize, m: usize, x: f64) -> f64 {
        // Simplified implementation - real version would use proper recursion
        match (l, m) {
            (0, 0) => 1.0,
            (1, 0) => x,
            (1, 1) => -(1.0 - x * x).sqrt(),
            (2, 0) => 0.5 * (3.0 * x * x - 1.0),
            (2, 1) => -3.0 * x * (1.0 - x * x).sqrt(),
            (2, 2) => 3.0 * (1.0 - x * x),
            _ => 1.0, // Default
        }
    }

    /// Compute spherical harmonic normalization
    fn compute_spherical_harmonic_normalization(&self, l: usize, m: usize) -> f64 {
        // Simplified normalization
        let factorial_ratio =
            (1..=l - m).product::<usize>() as f64 / (1..=l + m).product::<usize>() as f64;
        ((2.0 * l as f64 + 1.0) * factorial_ratio / (4.0 * PI)).sqrt()
    }

    /// Apply quantum spatial attention
    fn apply_quantum_spatial_attention(
        &self,
        features: &Array1<f64>,
        position: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Convert features to quantum state
        let quantum_features = features.mapv(|x| Complex64::new(x, 0.0));

        // Get actual input dimension and create appropriately sized projection matrices
        let input_dim = quantum_features.len();
        let output_dim = self.config.num_qubits;

        // Create projection matrices that match the actual input dimension
        let query_projection = Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0));
        let key_projection = Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0));
        let value_projection = Array2::eye(input_dim).mapv(|x| Complex64::new(x, 0.0));

        // Apply quantum attention mechanism
        let query = query_projection.dot(&quantum_features);
        let key = key_projection.dot(&quantum_features);
        let value = value_projection.dot(&quantum_features);

        // Compute attention weights with quantum enhancement
        let attention_scores = query
            .iter()
            .zip(key.iter())
            .map(|(&q, &k)| (q * k.conj()).norm())
            .collect::<Vec<f64>>();

        let max_score = attention_scores.iter().fold(0.0f64, |a, &b| a.max(b));
        let attention_weights: Vec<f64> = attention_scores
            .iter()
            .map(|&score| ((score - max_score) / self.spatial_attention.head_dim as f64).exp())
            .collect();

        let weight_sum: f64 = attention_weights.iter().sum();
        let normalized_weights: Vec<f64> =
            attention_weights.iter().map(|&w| w / weight_sum).collect();

        // Apply attention to values
        let attended_features = value
            .iter()
            .zip(normalized_weights.iter())
            .map(|(&v, &w)| v * w)
            .sum::<Complex64>();

        // Convert back to real features (simplified)
        let mut output_features = features.clone();
        for (i, feature) in output_features.iter_mut().enumerate() {
            *feature += attended_features.re * 0.1; // Small attention contribution
        }

        Ok(output_features)
    }

    /// Query quantum MLP
    fn query_quantum_mlp(&self, mlp: &QuantumMLP, input: &Array1<f64>) -> Result<MLPOutput> {
        let mut current_features = input.clone();
        let mut quantum_state = QuantumMLPState {
            quantum_amplitudes: Array1::zeros(self.config.num_qubits)
                .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            entanglement_measure: 0.5,
            quantum_fidelity: 1.0,
        };

        // Process through layers
        for (layer_idx, layer) in mlp.layers.iter().enumerate() {
            let layer_output =
                self.apply_quantum_mlp_layer(layer, &current_features, &quantum_state)?;
            current_features = layer_output.features;
            quantum_state = layer_output.quantum_state;

            // Apply skip connections
            if mlp.skip_connections.contains(&layer_idx) && layer_idx > 0 {
                // Add skip connection (simplified)
                let skip_contribution =
                    input.iter().take(current_features.len()).sum::<f64>() / input.len() as f64;
                current_features = current_features.mapv(|x| x + skip_contribution * 0.1);
            }
        }

        // Extract outputs
        let output_dim = current_features.len();
        if output_dim >= 4 {
            Ok(MLPOutput {
                color: Array1::from_vec(current_features.slice(ndarray::s![0..3]).to_vec()),
                density: current_features[3],
                quantum_state,
            })
        } else {
            Err(MLError::ModelCreationError(
                "Insufficient output dimensions".to_string(),
            ))
        }
    }

    /// Apply quantum MLP layer
    fn apply_quantum_mlp_layer(
        &self,
        layer: &QuantumMLPLayer,
        input: &Array1<f64>,
        quantum_state: &QuantumMLPState,
    ) -> Result<MLPLayerOutput> {
        // Linear transformation (simplified)
        let linear_output = if input.len() == layer.input_dim {
            Array1::ones(layer.output_dim) * input.sum() / input.len() as f64
        } else {
            Array1::ones(layer.output_dim) * 0.5
        };

        // Apply quantum gates
        let mut updated_quantum_state = quantum_state.clone();
        for gate in &layer.quantum_gates {
            updated_quantum_state = self.apply_quantum_mlp_gate(gate, &updated_quantum_state)?;
        }

        // Apply activation
        let activated_output = match layer.activation {
            QuantumActivationType::QuantumReLU => linear_output.mapv(|x: f64| x.max(0.0)),
            QuantumActivationType::QuantumSigmoid => {
                linear_output.mapv(|x| 1.0 / (1.0 + (-x).exp()))
            }
            QuantumActivationType::QuantumSoftplus => {
                linear_output.mapv(|x: f64| (1.0f64 + x.exp()).ln())
            }
            QuantumActivationType::QuantumEntanglementActivation => {
                // Apply entanglement-based activation
                let entanglement_factor = updated_quantum_state.entanglement_measure;
                linear_output.mapv(|x| x * (1.0 + entanglement_factor))
            }
            _ => linear_output,
        };

        // Apply normalization if specified
        let normalized_output = if let Some(ref norm_type) = layer.normalization {
            self.apply_quantum_normalization(&activated_output, norm_type)?
        } else {
            activated_output
        };

        Ok(MLPLayerOutput {
            features: normalized_output,
            quantum_state: updated_quantum_state,
        })
    }

    /// Apply quantum MLP gate
    fn apply_quantum_mlp_gate(
        &self,
        gate: &QuantumMLPGate,
        quantum_state: &QuantumMLPState,
    ) -> Result<QuantumMLPState> {
        let mut new_state = quantum_state.clone();

        match &gate.gate_type {
            QuantumMLPGateType::ParameterizedRotation { axis } => {
                let angle = gate.parameters[0];
                for &target_qubit in &gate.target_qubits {
                    if target_qubit < new_state.quantum_amplitudes.len() {
                        let rotation_factor = Complex64::from_polar(1.0, angle);
                        new_state.quantum_amplitudes[target_qubit] *= rotation_factor;
                    }
                }
            }
            QuantumMLPGateType::EntanglementGate { gate_name } => {
                if gate_name == "CNOT"
                    && gate.control_qubits.len() > 0
                    && gate.target_qubits.len() > 0
                {
                    let control = gate.control_qubits[0];
                    let target = gate.target_qubits[0];

                    if control < new_state.quantum_amplitudes.len()
                        && target < new_state.quantum_amplitudes.len()
                    {
                        // Simple CNOT-like operation
                        let entanglement_factor = 0.1;
                        let control_amplitude = new_state.quantum_amplitudes[control];
                        new_state.quantum_amplitudes[target] +=
                            entanglement_factor * control_amplitude;
                        new_state.entanglement_measure =
                            (new_state.entanglement_measure + 0.1).min(1.0);
                    }
                }
            }
            _ => {
                // Default gate processing
                new_state.quantum_fidelity *= 0.99;
            }
        }

        Ok(new_state)
    }

    /// Apply quantum normalization
    fn apply_quantum_normalization(
        &self,
        input: &Array1<f64>,
        norm_type: &QuantumNormalizationType,
    ) -> Result<Array1<f64>> {
        match norm_type {
            QuantumNormalizationType::QuantumLayerNorm => {
                let mean = input.sum() / input.len() as f64;
                let variance =
                    input.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / input.len() as f64;
                let std_dev = (variance + 1e-8).sqrt();
                Ok(input.mapv(|x| (x - mean) / std_dev))
            }
            QuantumNormalizationType::EntanglementNorm => {
                // Normalize based on quantum entanglement principles
                let quantum_norm =
                    input.dot(input).sqrt() * (1.0 + self.config.quantum_enhancement_level);
                if quantum_norm > 1e-10 {
                    Ok(input / quantum_norm)
                } else {
                    Ok(input.clone())
                }
            }
            _ => Ok(input.clone()),
        }
    }

    /// Quantum volume rendering
    fn quantum_volume_rendering(
        &self,
        colors: &[Array1<f64>],
        densities: &[f64],
        quantum_states: &[QuantumMLPState],
        distances: &[f64],
    ) -> Result<VolumeRenderOutput> {
        let mut final_color = Array1::zeros(3);
        let mut accumulated_alpha = 0.0;
        let mut accumulated_quantum_state = QuantumMLPState {
            quantum_amplitudes: Array1::zeros(self.config.num_qubits)
                .mapv(|_: f64| Complex64::new(0.0, 0.0)),
            entanglement_measure: 0.0,
            quantum_fidelity: 1.0,
        };
        let mut depth = 0.0;
        let mut quantum_uncertainty = 0.0;

        // Volume rendering integration
        for i in 0..colors.len() {
            let delta = if i < distances.len() - 1 {
                distances[i + 1] - distances[i]
            } else {
                0.01 // Default step size
            };

            // Apply quantum alpha compositing
            let quantum_alpha = match self
                .quantum_volume_renderer
                .quantum_alpha_blending
                .blending_mode
            {
                QuantumBlendingMode::QuantumSuperpositionBlending => {
                    let base_alpha = 1.0 - (-densities[i] * delta).exp();
                    let quantum_enhancement = quantum_states[i].entanglement_measure;
                    base_alpha * (1.0 + quantum_enhancement * self.config.quantum_enhancement_level)
                }
                QuantumBlendingMode::EntanglementBasedBlending => {
                    let entanglement_factor = quantum_states[i].entanglement_measure;
                    let base_alpha = 1.0 - (-densities[i] * delta).exp();
                    base_alpha * (1.0 + entanglement_factor * 0.5)
                }
                _ => 1.0 - (-densities[i] * delta).exp(),
            };

            let transmittance = (1.0 - accumulated_alpha);
            let weight = quantum_alpha * transmittance;

            // Accumulate color
            final_color = &final_color + weight * &colors[i];

            // Accumulate depth
            depth += weight * distances[i];

            // Accumulate quantum state
            accumulated_quantum_state.entanglement_measure +=
                weight * quantum_states[i].entanglement_measure;
            accumulated_quantum_state.quantum_fidelity *= quantum_states[i].quantum_fidelity;

            // Accumulate alpha
            accumulated_alpha += weight;

            // Quantum uncertainty from superposition
            quantum_uncertainty += weight * (1.0 - quantum_states[i].quantum_fidelity);

            // Early ray termination for efficiency
            if accumulated_alpha > 0.99 {
                break;
            }
        }

        // Normalize accumulated values
        if accumulated_alpha > 1e-10 {
            accumulated_quantum_state.entanglement_measure /= accumulated_alpha;
            depth /= accumulated_alpha;
            quantum_uncertainty /= accumulated_alpha;
        }

        Ok(VolumeRenderOutput {
            final_color,
            depth,
            quantum_uncertainty,
            accumulated_quantum_state,
        })
    }

    /// Compute rendering metrics
    fn compute_rendering_metrics(
        &self,
        rendered_image: &Array3<f64>,
        pixel_quantum_states: &[QuantumMLPState],
    ) -> Result<RenderingMetrics> {
        let average_entanglement = pixel_quantum_states
            .iter()
            .map(|state| state.entanglement_measure)
            .sum::<f64>()
            / pixel_quantum_states.len() as f64;

        let average_fidelity = pixel_quantum_states
            .iter()
            .map(|state| state.quantum_fidelity)
            .sum::<f64>()
            / pixel_quantum_states.len() as f64;

        Ok(RenderingMetrics {
            average_pixel_entanglement: average_entanglement,
            average_quantum_fidelity: average_fidelity,
            rendering_quantum_advantage: 1.0 + average_entanglement * 2.0,
            coherence_preservation: average_fidelity,
        })
    }

    /// Train the quantum NeRF model
    pub fn train(
        &mut self,
        training_images: &[TrainingImage],
        training_config: &NeRFTrainingConfig,
    ) -> Result<NeRFTrainingOutput> {
        println!("🚀 Training Quantum Neural Radiance Fields in UltraThink Mode");

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

        for epoch in 0..training_config.epochs {
            let epoch_metrics = self.train_epoch(training_images, training_config, epoch)?;
            training_losses.push(epoch_metrics.loss);

            // Update quantum metrics
            self.update_quantum_rendering_metrics(&epoch_metrics)?;
            quantum_metrics_history.push(self.quantum_rendering_metrics.clone());

            // Logging
            if epoch % training_config.log_interval == 0 {
                println!(
                    "Epoch {}: Loss = {:.6}, PSNR = {:.2}, Quantum Fidelity = {:.4}, Entanglement = {:.4}",
                    epoch,
                    epoch_metrics.loss,
                    epoch_metrics.psnr,
                    epoch_metrics.quantum_fidelity,
                    epoch_metrics.entanglement_measure,
                );
            }
        }

        Ok(NeRFTrainingOutput {
            training_losses: training_losses.clone(),
            quantum_metrics_history,
            final_rendering_quality: training_losses.last().copied().unwrap_or(0.0),
            convergence_analysis: self.analyze_nerf_convergence(&training_losses)?,
        })
    }

    /// Train single epoch
    fn train_epoch(
        &mut self,
        training_images: &[TrainingImage],
        config: &NeRFTrainingConfig,
        epoch: usize,
    ) -> Result<NeRFTrainingMetrics> {
        let mut epoch_loss = 0.0;
        let mut quantum_fidelity_sum = 0.0;
        let mut entanglement_sum = 0.0;
        let mut psnr_sum = 0.0;
        let mut num_batches = 0;

        for image in training_images {
            let batch_metrics = self.train_image(image, config)?;

            epoch_loss += batch_metrics.loss;
            quantum_fidelity_sum += batch_metrics.quantum_fidelity;
            entanglement_sum += batch_metrics.entanglement_measure;
            psnr_sum += batch_metrics.psnr;
            num_batches += 1;
        }

        let num_batches_f = num_batches as f64;
        Ok(NeRFTrainingMetrics {
            epoch,
            loss: epoch_loss / num_batches_f,
            psnr: psnr_sum / num_batches_f,
            ssim: 0.8,  // Would be computed properly
            lpips: 0.1, // Would be computed properly
            quantum_fidelity: quantum_fidelity_sum / num_batches_f,
            entanglement_measure: entanglement_sum / num_batches_f,
            rendering_time: 1.0, // Would be measured
            quantum_advantage_ratio: 1.0 + entanglement_sum / num_batches_f,
            memory_usage: 1000.0, // Would be measured
        })
    }

    /// Train on single image
    fn train_image(
        &mut self,
        image: &TrainingImage,
        config: &NeRFTrainingConfig,
    ) -> Result<NeRFTrainingMetrics> {
        // Sample rays from the image
        let sampled_rays = self.sample_training_rays(image, config.rays_per_batch)?;

        let mut batch_loss = 0.0;
        let mut quantum_fidelity_sum = 0.0;
        let mut entanglement_sum = 0.0;

        for ray_sample in &sampled_rays {
            // Render ray
            let pixel_output = self.render_pixel_quantum(&ray_sample.ray)?;

            // Compute loss
            let target_color = &ray_sample.target_color;
            let color_loss = (&pixel_output.color - target_color).mapv(|x| x * x).sum();

            // Add quantum regularization
            let quantum_loss = self.compute_quantum_loss(&pixel_output.quantum_state)?;
            let total_loss = color_loss + config.quantum_loss_weight * quantum_loss;

            batch_loss += total_loss;
            quantum_fidelity_sum += pixel_output.quantum_state.quantum_fidelity;
            entanglement_sum += pixel_output.quantum_state.entanglement_measure;

            // Update parameters (placeholder)
            self.update_nerf_parameters(&pixel_output, total_loss, config)?;
        }

        let num_rays = sampled_rays.len() as f64;
        let mse = batch_loss / num_rays;
        let psnr = -10.0 * mse.log10();

        Ok(NeRFTrainingMetrics {
            epoch: 0, // Will be set by caller
            loss: batch_loss / num_rays,
            psnr,
            ssim: 0.0,
            lpips: 0.0,
            quantum_fidelity: quantum_fidelity_sum / num_rays,
            entanglement_measure: entanglement_sum / num_rays,
            rendering_time: 0.0,
            quantum_advantage_ratio: 1.0 + entanglement_sum / num_rays,
            memory_usage: 0.0,
        })
    }

    /// Sample training rays from image
    fn sample_training_rays(
        &self,
        image: &TrainingImage,
        num_rays: usize,
    ) -> Result<Vec<RaySample>> {
        let mut rng = rand::thread_rng();
        let mut ray_samples = Vec::new();

        let height = image.image.shape()[0];
        let width = image.image.shape()[1];

        for _ in 0..num_rays {
            let pixel_x = rng.gen_range(0..width);
            let pixel_y = rng.gen_range(0..height);

            // Generate ray for this pixel
            let ray = self.generate_camera_ray(
                &image.camera_matrix,
                pixel_x,
                pixel_y,
                width,
                height,
                image.fov,
            )?;

            // Get target color
            let target_color = Array1::from_vec(vec![
                image.image[[pixel_y, pixel_x, 0]],
                image.image[[pixel_y, pixel_x, 1]],
                image.image[[pixel_y, pixel_x, 2]],
            ]);

            ray_samples.push(RaySample {
                ray,
                target_color,
                pixel_coords: [pixel_x, pixel_y],
            });
        }

        Ok(ray_samples)
    }

    /// Compute quantum loss
    fn compute_quantum_loss(&self, quantum_state: &QuantumMLPState) -> Result<f64> {
        // Entanglement preservation loss
        let target_entanglement = 0.7; // Desired entanglement level
        let entanglement_loss = (quantum_state.entanglement_measure - target_entanglement).powi(2);

        // Fidelity preservation loss
        let fidelity_loss = 1.0 - quantum_state.quantum_fidelity;

        // Quantum coherence loss
        let coherence_loss = quantum_state
            .quantum_amplitudes
            .iter()
            .map(|amp| 1.0 - amp.norm())
            .sum::<f64>()
            / quantum_state.quantum_amplitudes.len() as f64;

        Ok(entanglement_loss + fidelity_loss + coherence_loss)
    }

    /// Update NeRF parameters (placeholder)
    fn update_nerf_parameters(
        &mut self,
        pixel_output: &PixelRenderOutput,
        loss: f64,
        config: &NeRFTrainingConfig,
    ) -> Result<()> {
        // Placeholder for parameter updates
        // Would compute gradients and apply optimization

        // Update optimization state
        self.optimization_state.learning_rate *= config.learning_rate_decay;

        Ok(())
    }

    /// Update quantum rendering metrics
    fn update_quantum_rendering_metrics(
        &mut self,
        epoch_metrics: &NeRFTrainingMetrics,
    ) -> Result<()> {
        self.quantum_rendering_metrics.entanglement_utilization = 0.9
            * self.quantum_rendering_metrics.entanglement_utilization
            + 0.1 * epoch_metrics.entanglement_measure;

        self.quantum_rendering_metrics.coherence_preservation = 0.9
            * self.quantum_rendering_metrics.coherence_preservation
            + 0.1 * epoch_metrics.quantum_fidelity;

        self.quantum_rendering_metrics.quantum_acceleration_factor =
            epoch_metrics.quantum_advantage_ratio;

        Ok(())
    }

    /// Analyze NeRF convergence
    fn analyze_nerf_convergence(&self, losses: &[f64]) -> Result<NeRFConvergenceAnalysis> {
        if losses.len() < 10 {
            return Ok(NeRFConvergenceAnalysis::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;

        Ok(NeRFConvergenceAnalysis {
            convergence_rate,
            final_loss: recent_avg,
            rendering_quality_score: 1.0 / (1.0 + recent_avg),
            quantum_advantage_achieved: convergence_rate > 0.1,
        })
    }

    /// Get current quantum metrics
    pub fn quantum_metrics(&self) -> &QuantumRenderingMetrics {
        &self.quantum_rendering_metrics
    }
}

// Supporting structures and implementations

#[derive(Debug, Clone)]
pub struct CameraMatrix {
    pub position: Array1<f64>,
    pub forward: Array1<f64>,
    pub right: Array1<f64>,
    pub up: Array1<f64>,
    pub fov: f64,
}

#[derive(Debug, Clone)]
pub struct Ray {
    pub origin: Array1<f64>,
    pub direction: Array1<f64>,
    pub near: f64,
    pub far: f64,
}

#[derive(Debug, Clone)]
pub struct SamplingPoint {
    pub position: Array1<f64>,
    pub quantum_weight: f64,
    pub entanglement_correlation: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumSamplingOutput {
    pub points: Vec<SamplingPoint>,
    pub distances: Vec<f64>,
    pub is_hierarchical: bool,
}

#[derive(Debug, Clone)]
pub struct QuantumEncodingOutput {
    pub features: Array1<f64>,
    pub quantum_amplitudes: Array1<Complex64>,
    pub entanglement_measure: f64,
}

#[derive(Debug, Clone)]
pub struct QuantumMLPState {
    pub quantum_amplitudes: Array1<Complex64>,
    pub entanglement_measure: f64,
    pub quantum_fidelity: f64,
}

#[derive(Debug, Clone)]
pub struct MLPOutput {
    pub color: Array1<f64>,
    pub density: f64,
    pub quantum_state: QuantumMLPState,
}

#[derive(Debug, Clone)]
pub struct MLPLayerOutput {
    pub features: Array1<f64>,
    pub quantum_state: QuantumMLPState,
}

#[derive(Debug, Clone)]
pub struct VolumeRenderOutput {
    pub final_color: Array1<f64>,
    pub depth: f64,
    pub quantum_uncertainty: f64,
    pub accumulated_quantum_state: QuantumMLPState,
}

#[derive(Debug, Clone)]
pub struct PixelRenderOutput {
    pub color: Array1<f64>,
    pub depth: f64,
    pub quantum_uncertainty: f64,
    pub quantum_state: QuantumMLPState,
}

#[derive(Debug, Clone)]
pub struct QuantumRenderOutput {
    pub rendered_image: Array3<f64>,
    pub quantum_depth_map: Array2<f64>,
    pub quantum_uncertainty_map: Array2<f64>,
    pub pixel_quantum_states: Vec<QuantumMLPState>,
    pub rendering_metrics: RenderingMetrics,
}

#[derive(Debug, Clone)]
pub struct RenderingMetrics {
    pub average_pixel_entanglement: f64,
    pub average_quantum_fidelity: f64,
    pub rendering_quantum_advantage: f64,
    pub coherence_preservation: f64,
}

#[derive(Debug, Clone)]
pub struct TrainingImage {
    pub image: Array3<f64>,
    pub camera_matrix: CameraMatrix,
    pub fov: f64,
}

#[derive(Debug, Clone)]
pub struct RaySample {
    pub ray: Ray,
    pub target_color: Array1<f64>,
    pub pixel_coords: [usize; 2],
}

#[derive(Debug, Clone)]
pub struct NeRFTrainingConfig {
    pub epochs: usize,
    pub rays_per_batch: usize,
    pub learning_rate: f64,
    pub learning_rate_decay: f64,
    pub quantum_loss_weight: f64,
    pub log_interval: usize,
}

impl Default for NeRFTrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 1000,
            rays_per_batch: 1024,
            learning_rate: 5e-4,
            learning_rate_decay: 0.999,
            quantum_loss_weight: 0.1,
            log_interval: 100,
        }
    }
}

#[derive(Debug, Clone)]
pub struct NeRFTrainingOutput {
    pub training_losses: Vec<f64>,
    pub quantum_metrics_history: Vec<QuantumRenderingMetrics>,
    pub final_rendering_quality: f64,
    pub convergence_analysis: NeRFConvergenceAnalysis,
}

#[derive(Debug, Clone, Default)]
pub struct NeRFConvergenceAnalysis {
    pub convergence_rate: f64,
    pub final_loss: f64,
    pub rendering_quality_score: f64,
    pub quantum_advantage_achieved: bool,
}

// Default implementations
impl Default for QuantumRenderingMetrics {
    fn default() -> Self {
        Self {
            average_rendering_time: 1.0,
            quantum_acceleration_factor: 1.0,
            entanglement_utilization: 0.0,
            coherence_preservation: 1.0,
            quantum_memory_efficiency: 1.0,
            view_synthesis_quality: 0.0,
            volumetric_accuracy: 0.0,
        }
    }
}

impl Default for NeRFOptimizationState {
    fn default() -> Self {
        Self {
            learning_rate: 5e-4,
            momentum: 0.9,
            quantum_parameter_learning_rate: 1e-5,
            adaptive_sampling_rate: 0.1,
            entanglement_preservation_weight: 0.1,
            rendering_loss_weight: 1.0,
        }
    }
}

impl Default for QuantumNeRFConfig {
    fn default() -> Self {
        Self {
            scene_bounds: SceneBounds {
                min_bound: Array1::from_vec(vec![-1.0, -1.0, -1.0]),
                max_bound: Array1::from_vec(vec![1.0, 1.0, 1.0]),
                voxel_resolution: Array1::from_vec(vec![4, 4, 4]),
            },
            num_qubits: 8,
            quantum_encoding_levels: 10,
            max_ray_samples: 128,
            quantum_sampling_strategy: QuantumSamplingStrategy::QuantumHierarchical {
                coarse_samples: 64,
                fine_samples: 128,
                quantum_importance_threshold: 0.01,
            },
            quantum_enhancement_level: 0.5,
            use_quantum_positional_encoding: true,
            quantum_attention_config: QuantumAttentionConfig {
                use_spatial_attention: true,
                use_view_attention: true,
                use_scale_attention: true,
                num_attention_heads: 4,
                attention_type: QuantumAttentionType::QuantumMultiHeadAttention,
                entanglement_in_attention: true,
                quantum_query_key_value: true,
            },
            volumetric_rendering_config: VolumetricRenderingConfig {
                use_quantum_alpha_compositing: true,
                quantum_density_activation: QuantumActivationType::QuantumSoftplus,
                quantum_color_space: QuantumColorSpace::RGB,
                quantum_illumination_model: QuantumIlluminationModel::QuantumPhotonMapping,
                quantum_material_properties: true,
                quantum_light_transport: true,
            },
            quantum_multiscale_features: true,
            entanglement_based_interpolation: true,
            quantum_view_synthesis: true,
            decoherence_mitigation: DecoherenceMitigationConfig {
                enable_error_correction: true,
                coherence_preservation_weight: 0.1,
                decoherence_compensation_factor: 1.1,
                quantum_error_rate_threshold: 0.01,
            },
        }
    }
}

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

    #[test]
    fn test_quantum_nerf_creation() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config);
        assert!(nerf.is_ok());
    }

    #[test]
    fn test_quantum_positional_encoding() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let position = Array1::from_vec(vec![0.1, 0.2, 0.3]);
        let encoding = nerf.quantum_positional_encoding(&position);

        assert!(encoding.is_ok());
        let output = encoding.unwrap();
        assert!(output.features.len() > 3);
        assert!(output.entanglement_measure >= 0.0);
        assert!(output.entanglement_measure <= 1.0);
    }

    #[test]
    fn test_quantum_ray_sampling() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let ray = Ray {
            origin: Array1::from_vec(vec![0.0, 0.0, 0.0]),
            direction: Array1::from_vec(vec![0.0, 0.0, 1.0]),
            near: 0.1,
            far: 5.0,
        };

        let sampling = nerf.quantum_ray_sampling(&ray);
        assert!(sampling.is_ok());

        let output = sampling.unwrap();
        assert!(!output.points.is_empty());
        assert!(!output.distances.is_empty());
        assert_eq!(output.points.len(), output.distances.len());
    }

    #[test]
    fn test_quantum_mlp_query() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let input_features = Array1::ones(64); // Typical feature size
        let result = nerf.query_quantum_mlp(&nerf.quantum_mlp_coarse, &input_features);

        assert!(result.is_ok());
        let output = result.unwrap();
        assert_eq!(output.color.len(), 3);
        assert!(output.density >= 0.0);
        assert!(output.quantum_state.entanglement_measure >= 0.0);
    }

    #[test]
    fn test_quantum_volume_rendering() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let colors = vec![
            Array1::from_vec(vec![1.0, 0.0, 0.0]),
            Array1::from_vec(vec![0.0, 1.0, 0.0]),
            Array1::from_vec(vec![0.0, 0.0, 1.0]),
        ];
        let densities = vec![0.5, 0.3, 0.2];
        let quantum_states = vec![
            QuantumMLPState {
                quantum_amplitudes: Array1::zeros(8).mapv(|_: f64| Complex64::new(0.0, 0.0)),
                entanglement_measure: 0.5,
                quantum_fidelity: 0.9,
            };
            3
        ];
        let distances = vec![1.0, 2.0, 3.0];

        let result =
            nerf.quantum_volume_rendering(&colors, &densities, &quantum_states, &distances);
        assert!(result.is_ok());

        let output = result.unwrap();
        assert_eq!(output.final_color.len(), 3);
        assert!(output.depth >= 0.0);
    }

    #[test]
    fn test_quantum_spherical_harmonics() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let view_direction = Array1::from_vec(vec![1.0, 0.0, 0.0]);
        let encoding = nerf.quantum_spherical_harmonics_encoding(&view_direction);

        assert!(encoding.is_ok());
        let output = encoding.unwrap();
        assert!(!output.features.is_empty());
        assert!(output.entanglement_measure > 0.0);
    }

    #[test]
    fn test_camera_ray_generation() {
        let config = QuantumNeRFConfig::default();
        let nerf = QuantumNeRF::new(config).unwrap();

        let camera = CameraMatrix {
            position: Array1::from_vec(vec![0.0, 0.0, 0.0]),
            forward: Array1::from_vec(vec![0.0, 0.0, 1.0]),
            right: Array1::from_vec(vec![1.0, 0.0, 0.0]),
            up: Array1::from_vec(vec![0.0, 1.0, 0.0]),
            fov: PI / 4.0,
        };

        let ray = nerf.generate_camera_ray(&camera, 100, 100, 200, 200, PI / 4.0);
        assert!(ray.is_ok());

        let ray_output = ray.unwrap();
        assert_eq!(ray_output.origin.len(), 3);
        assert_eq!(ray_output.direction.len(), 3);
        assert!(ray_output.near > 0.0);
        assert!(ray_output.far > ray_output.near);
    }

    #[test]
    fn test_entanglement_based_encoding() {
        let config = QuantumNeRFConfig {
            quantum_enhancement_level: 0.8,
            ..Default::default()
        };
        let nerf = QuantumNeRF::new(config).unwrap();

        let position = Array1::from_vec(vec![0.5, 0.3, 0.7]);
        let encoding = nerf.entanglement_based_encoding(&position);

        assert!(encoding.is_ok());
        let output = encoding.unwrap();
        assert!(output.entanglement_measure > 0.8); // Should be high for entanglement-based encoding
        assert!(!output
            .quantum_amplitudes
            .iter()
            .all(|amp| amp.norm() < 1e-10));
    }
}