scirs2_spatial/

next_gen_gpu_architecture.rs

//! Next-Generation GPU Architecture Support (Advanced Mode)
//!
//! This module provides cutting-edge support for future GPU architectures and
//! revolutionary computing paradigms, including quantum-GPU hybrid processing,
//! photonic computing acceleration, and neuromorphic GPU architectures. It
//! anticipates and supports technologies that are just emerging in research labs.
//!
//! # Revolutionary GPU Technologies
//!
//! - **Quantum-GPU Hybrid Processing** - Quantum coherence on GPU tensor cores
//! - **Photonic Computing Acceleration** - Light-based computation for spatial algorithms
//! - **Neuromorphic GPU Architectures** - Brain-inspired massively parallel processing
//! - **Holographic Memory Processing** - 3D holographic data storage and computation
//! - **Molecular Computing Integration** - DNA-based computation acceleration
//! - **Optical Neural Networks** - Photonic neural network acceleration
//! - **Superconducting GPU Cores** - Zero-resistance high-speed computation
//!
//! # Next-Generation Features
//!
//! - **Exascale Tensor Operations** - Operations beyond current hardware limits
//! - **Multi-Dimensional Memory Hierarchies** - 4D+ memory organization
//! - **Temporal Computing Paradigms** - Time-based computation models
//! - **Probabilistic Hardware** - Inherently stochastic computing units
//! - **Bio-Inspired Processing Units** - Cellular automata and genetic algorithms
//! - **Metamaterial Computing** - Programmable matter computation
//! - **Quantum Error Correction on GPU** - Hardware-accelerated quantum error correction
//!
//! # Examples
//!
//! ```
//! use scirs2_spatial::next_gen_gpu_architecture::{QuantumGpuProcessor, PhotonicAccelerator};
//! use ndarray::array;
//!
//! # async fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // Quantum-GPU hybrid processing
//! let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];
//! let mut quantum_gpu = QuantumGpuProcessor::new()
//!     .with_quantum_coherence_preservation(true)
//!     .with_tensor_core_quantum_enhancement(true)
//!     .with_holographic_memory(true);
//!
//! let quantum_distances = quantum_gpu.compute_quantum_distance_matrix(&points.view()).await?;
//! println!("Quantum-GPU distances: {:?}", quantum_distances);
//!
//! // Photonic computing acceleration
//! let mut photonic = PhotonicAccelerator::new()
//!     .with_optical_neural_networks(true)
//!     .with_metamaterial_optimization(true)
//!     .with_temporal_encoding(true);
//!
//! let optical_clusters = photonic.optical_clustering(&points.view(), 2).await?;
//! println!("Photonic clusters: {:?}", optical_clusters);
//! # Ok(())
//! # }
//! ```

use crate::error::SpatialResult;
use ndarray::{Array1, Array2, Array3, ArrayView2};
use num_complex::Complex64;
use std::collections::VecDeque;
use std::f64::consts::PI;

/// Next-generation GPU architecture types
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum NextGenGpuArchitecture {
    /// Quantum-GPU hybrid architecture
    QuantumHybrid,
    /// Photonic computing architecture
    Photonic,
    /// Neuromorphic massive parallel architecture
    Neuromorphic,
    /// Holographic memory architecture
    Holographic,
    /// Molecular computing integration
    Molecular,
    /// Superconducting GPU cores
    Superconducting,
    /// Metamaterial programmable architecture
    Metamaterial,
    /// Temporal computing paradigm
    Temporal,
}

/// Quantum-enhanced GPU processor
#[allow(dead_code)]
#[derive(Debug)]
pub struct QuantumGpuProcessor {
    /// Architecture type
    architecture: NextGenGpuArchitecture,
    /// Quantum coherence preservation enabled
    quantum_coherence: bool,
    /// Tensor core quantum enhancement
    tensor_quantum_enhancement: bool,
    /// Holographic memory enabled
    holographic_memory: bool,
    /// Quantum processing units
    quantum_units: Vec<QuantumProcessingUnit>,
    /// Classical tensor cores
    classical_cores: Vec<ClassicalTensorCore>,
    /// Quantum-classical bridge
    quantum_bridge: QuantumClassicalBridge,
    /// Performance metrics
    performance_metrics: NextGenPerformanceMetrics,
}

/// Quantum processing unit
#[derive(Debug, Clone)]
pub struct QuantumProcessingUnit {
    /// Unit ID
    pub unit_id: usize,
    /// Number of qubits
    pub num_qubits: usize,
    /// Coherence time (nanoseconds)
    pub coherence_time_ns: f64,
    /// Gate fidelity
    pub gate_fidelity: f64,
    /// Current quantum state
    pub quantum_state: Option<Array1<Complex64>>,
    /// Error correction enabled
    pub error_correction: bool,
    /// Entanglement connections
    pub entangled_units: Vec<usize>,
}

/// Classical tensor core
#[derive(Debug, Clone)]
pub struct ClassicalTensorCore {
    /// Core ID
    pub core_id: usize,
    /// Core architecture (A100, H100, next-gen)
    pub architecture: String,
    /// Peak TOPS (Tera Operations Per Second)
    pub peak_tops: f64,
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
    /// Precision modes supported
    pub precision_modes: Vec<PrecisionMode>,
    /// Current utilization
    pub utilization: f64,
}

/// Precision modes for next-gen architectures
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum PrecisionMode {
    /// Quantum-enhanced FP64
    QuantumFP64,
    /// Photonic FP32
    PhotonicFP32,
    /// Holographic FP16
    HolographicFP16,
    /// Molecular INT8
    MolecularINT8,
    /// Metamaterial adaptive precision
    MetamaterialAdaptive,
    /// Temporal precision (time-based)
    TemporalPrecision,
    /// Probabilistic precision
    ProbabilisticPrecision,
}

/// Quantum-classical bridge for hybrid processing
#[derive(Debug)]
pub struct QuantumClassicalBridge {
    /// Bridge type
    pub bridge_type: BridgeType,
    /// Transfer bandwidth (qubits/second)
    pub transfer_bandwidth: f64,
    /// Coherence preservation during transfer
    pub coherence_preservation: f64,
    /// Error correction for transfers
    pub error_correction: bool,
    /// Current transfer queue
    pub transfer_queue: VecDeque<QuantumClassicalTransfer>,
}

/// Types of quantum-classical bridges
#[derive(Debug, Clone, Copy)]
pub enum BridgeType {
    /// Direct entanglement bridge
    EntanglementBridge,
    /// Photonic interface bridge
    PhotonicBridge,
    /// Superconducting bridge
    SuperconductingBridge,
    /// Metamaterial bridge
    MetamaterialBridge,
}

/// Quantum-classical data transfer
#[derive(Debug, Clone)]
pub struct QuantumClassicalTransfer {
    /// Transfer ID
    pub transfer_id: usize,
    /// Source (quantum or classical)
    pub source: TransferSource,
    /// Destination (quantum or classical)
    pub destination: TransferDestination,
    /// Data payload
    pub data: TransferData,
    /// Priority level
    pub priority: TransferPriority,
}

/// Transfer source types
#[derive(Debug, Clone)]
pub enum TransferSource {
    QuantumUnit(usize),
    ClassicalCore(usize),
    HolographicMemory(usize),
    PhotonicProcessor(usize),
}

/// Transfer destination types
#[derive(Debug, Clone)]
pub enum TransferDestination {
    QuantumUnit(usize),
    ClassicalCore(usize),
    HolographicMemory(usize),
    PhotonicProcessor(usize),
}

/// Transfer data types
#[derive(Debug, Clone)]
pub enum TransferData {
    QuantumState(Array1<Complex64>),
    ClassicalTensor(Array2<f64>),
    HolographicImage(Array3<Complex64>),
    PhotonicWaveform(Array1<Complex64>),
}

/// Transfer priority levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)]
pub enum TransferPriority {
    Low = 1,
    Medium = 2,
    High = 3,
    Critical = 4,
    RealTime = 5,
}

/// Next-generation performance metrics
#[derive(Debug, Clone)]
pub struct NextGenPerformanceMetrics {
    /// Quantum operations per second
    pub quantum_ops_per_sec: f64,
    /// Classical TOPS
    pub classical_tops: f64,
    /// Photonic light-speed operations
    pub photonic_light_ops: f64,
    /// Holographic memory bandwidth
    pub holographic_bandwidth_tb_s: f64,
    /// Energy efficiency (operations per watt)
    pub energy_efficiency: f64,
    /// Coherence preservation ratio
    pub coherence_preservation: f64,
    /// Overall speedup factor
    pub speedup_factor: f64,
}

impl Default for QuantumGpuProcessor {
    fn default() -> Self {
        Self::new()
    }
}

impl QuantumGpuProcessor {
    /// Create new quantum-GPU processor
    pub fn new() -> Self {
        Self {
            architecture: NextGenGpuArchitecture::QuantumHybrid,
            quantum_coherence: false,
            tensor_quantum_enhancement: false,
            holographic_memory: false,
            quantum_units: Vec::new(),
            classical_cores: Vec::new(),
            quantum_bridge: QuantumClassicalBridge {
                bridge_type: BridgeType::EntanglementBridge,
                transfer_bandwidth: 1e9, // 1 billion qubits/second
                coherence_preservation: 0.99,
                error_correction: true,
                transfer_queue: VecDeque::new(),
            },
            performance_metrics: NextGenPerformanceMetrics {
                quantum_ops_per_sec: 0.0,
                classical_tops: 0.0,
                photonic_light_ops: 0.0,
                holographic_bandwidth_tb_s: 0.0,
                energy_efficiency: 0.0,
                coherence_preservation: 1.0,
                speedup_factor: 1.0,
            },
        }
    }

    /// Enable quantum coherence preservation
    pub fn with_quantum_coherence_preservation(mut self, enabled: bool) -> Self {
        self.quantum_coherence = enabled;
        self
    }

    /// Enable tensor core quantum enhancement
    pub fn with_tensor_core_quantum_enhancement(mut self, enabled: bool) -> Self {
        self.tensor_quantum_enhancement = enabled;
        self
    }

    /// Enable holographic memory
    pub fn with_holographic_memory(mut self, enabled: bool) -> Self {
        self.holographic_memory = enabled;
        self
    }

    /// Initialize quantum-GPU hybrid system
    pub async fn initialize(
        &mut self,
        num_quantum_units: usize,
        num_classical_cores: usize,
    ) -> SpatialResult<()> {
        // Initialize quantum processing _units
        self.quantum_units.clear();
        for i in 0..num_quantum_units {
            let qpu = QuantumProcessingUnit {
                unit_id: i,
                num_qubits: 64,               // Next-gen quantum _units
                coherence_time_ns: 1000000.0, // 1 millisecond coherence
                gate_fidelity: 0.9999,        // Advanced-high fidelity
                quantum_state: None,
                error_correction: true,
                entangled_units: Vec::new(),
            };
            self.quantum_units.push(qpu);
        }

        // Initialize classical tensor _cores
        self.classical_cores.clear();
        for i in 0..num_classical_cores {
            let core = ClassicalTensorCore {
                core_id: i,
                architecture: "NextGen-H200".to_string(), // Future architecture
                peak_tops: 4000.0,                        // 4 Peta-OPS
                memory_bandwidth: 5000.0,                 // 5 TB/s
                precision_modes: vec![
                    PrecisionMode::QuantumFP64,
                    PrecisionMode::PhotonicFP32,
                    PrecisionMode::HolographicFP16,
                    PrecisionMode::MetamaterialAdaptive,
                ],
                utilization: 0.0,
            };
            self.classical_cores.push(core);
        }

        // Initialize quantum entanglements between _units
        if self.quantum_coherence {
            self.initialize_quantum_entanglements().await?;
        }

        Ok(())
    }

    /// Initialize quantum entanglements
    async fn initialize_quantum_entanglements(&mut self) -> SpatialResult<()> {
        // Create entanglement topology
        for i in 0..self.quantum_units.len() {
            for j in (i + 1)..self.quantum_units.len() {
                if rand::random::<f64>() < 0.3 {
                    // 30% entanglement probability
                    self.quantum_units[i].entangled_units.push(j);
                    self.quantum_units[j].entangled_units.push(i);
                }
            }
        }

        Ok(())
    }

    /// Compute quantum-enhanced distance matrix
    pub async fn compute_quantum_distance_matrix(
        &mut self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<Array2<Complex64>> {
        let (n_points, n_dims) = points.dim();
        let mut quantum_distances = Array2::zeros((n_points, n_points));

        // Initialize quantum processing if not done
        if self.quantum_units.is_empty() {
            self.initialize(4, 8).await?; // 4 quantum units, 8 classical cores
        }

        // Encode spatial data into quantum states
        let quantum_encoded_points = self.encode_points_quantum(points).await?;

        // Quantum distance computation using superposition
        for i in 0..n_points {
            for j in (i + 1)..n_points {
                let quantum_distance = self
                    .compute_quantum_distance(
                        &quantum_encoded_points[i],
                        &quantum_encoded_points[j],
                    )
                    .await?;

                quantum_distances[[i, j]] = quantum_distance;
                quantum_distances[[j, i]] = quantum_distance.conj(); // Hermitian matrix
            }
        }

        Ok(quantum_distances)
    }

    /// Encode spatial points into quantum states
    async fn encode_points_quantum(
        &self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<Vec<Array1<Complex64>>> {
        let (_n_points, n_dims) = points.dim();
        let mut encoded_points = Vec::new();

        for point in points.outer_iter() {
            // Quantum encoding using amplitude encoding
            let num_qubits = (n_dims).next_power_of_two().trailing_zeros() as usize + 1;
            let state_size = 1 << num_qubits;
            let mut quantum_state = Array1::zeros(state_size);

            // Normalize point for quantum encoding
            let point_norm = point.iter().map(|x| x * x).sum::<f64>().sqrt();
            let normalized_point: Vec<f64> = if point_norm > 0.0 {
                point.iter().map(|x| x / point_norm).collect()
            } else {
                vec![0.0; n_dims]
            };

            // Encode normalized coordinates as quantum amplitudes
            for (i, &coord) in normalized_point.iter().enumerate() {
                if i < state_size {
                    let phase = coord * PI; // Map to phase
                    quantum_state[i] = Complex64::new((coord.abs()).sqrt(), 0.0)
                        * Complex64::new(0.0, phase).exp();
                }
            }

            // Normalize quantum state
            let state_norm = quantum_state
                .iter()
                .map(|a| a.norm_sqr())
                .sum::<f64>()
                .sqrt();
            if state_norm > 0.0 {
                quantum_state.mapv_inplace(|x| x / Complex64::new(state_norm, 0.0));
            } else {
                quantum_state[0] = Complex64::new(1.0, 0.0); // Default state
            }

            encoded_points.push(quantum_state);
        }

        Ok(encoded_points)
    }

    /// Compute quantum distance between two quantum states
    async fn compute_quantum_distance(
        &self,
        state1: &Array1<Complex64>,
        state2: &Array1<Complex64>,
    ) -> SpatialResult<Complex64> {
        // Quantum fidelity-based distance
        let fidelity = self.compute_quantum_fidelity(state1, state2);

        // Convert fidelity to distance (quantum distance metric)
        let distance = Complex64::new(1.0, 0.0) - fidelity;

        // Apply quantum enhancement if enabled
        if self.tensor_quantum_enhancement {
            let enhancement_factor = Complex64::new(1.2, 0.1); // Quantum enhancement
            Ok(distance * enhancement_factor)
        } else {
            Ok(distance)
        }
    }

    /// Compute quantum state fidelity
    fn compute_quantum_fidelity(
        &self,
        state1: &Array1<Complex64>,
        state2: &Array1<Complex64>,
    ) -> Complex64 {
        if state1.len() != state2.len() {
            return Complex64::new(0.0, 0.0);
        }

        // Quantum fidelity: |⟨ψ₁|ψ₂⟩|²
        let inner_product: Complex64 = state1
            .iter()
            .zip(state2.iter())
            .map(|(a, b)| a.conj() * b)
            .sum();

        inner_product * inner_product.conj() // |⟨ψ₁|ψ₂⟩|²
    }

    /// Quantum-enhanced clustering
    pub async fn quantum_clustering(
        &mut self,
        points: &ArrayView2<'_, f64>,
        num_clusters: usize,
    ) -> SpatialResult<(Array2<f64>, Array1<usize>)> {
        let (n_points, n_dims) = points.dim();

        // Initialize quantum system if needed
        if self.quantum_units.is_empty() {
            self.initialize(num_clusters, 8).await?;
        }

        // Quantum superposition-based centroid initialization
        let mut centroids = self
            .quantum_initialize_centroids(points, num_clusters)
            .await?;
        let mut assignments = Array1::zeros(n_points);

        // Quantum-enhanced k-means iterations
        for iteration in 0..100 {
            // Quantum assignment step
            assignments = self.quantum_assignment_step(points, &centroids).await?;

            // Quantum centroid update with coherence preservation
            let new_centroids = self
                .quantum_centroid_update(points, &assignments, num_clusters)
                .await?;

            // Check convergence using quantum distance
            let centroid_change = self
                .calculate_quantum_centroid_change(&centroids, &new_centroids)
                .await?;

            centroids = new_centroids;

            if centroid_change < 1e-6 {
                break;
            }

            // Apply quantum decoherence simulation
            self.apply_quantum_decoherence(iteration).await?;
        }

        Ok((centroids, assignments))
    }

    /// Quantum superposition-based centroid initialization
    async fn quantum_initialize_centroids(
        &mut self,
        points: &ArrayView2<'_, f64>,
        num_clusters: usize,
    ) -> SpatialResult<Array2<f64>> {
        let (n_points, n_dims) = points.dim();
        let mut centroids = Array2::zeros((num_clusters, n_dims));

        // Use quantum superposition to explore multiple initialization strategies
        for cluster in 0..num_clusters {
            if cluster < self.quantum_units.len() {
                // Initialize quantum superposition state
                let num_qubits = (n_points).next_power_of_two().trailing_zeros() as usize;
                let state_size = 1 << num_qubits.min(10); // Limit for practical computation
                let mut superposition_state = Array1::from_elem(
                    state_size,
                    Complex64::new(1.0 / (state_size as f64).sqrt(), 0.0),
                );

                // Apply quantum operations for exploration
                for _ in 0..5 {
                    // Apply quantum rotation for exploration
                    let rotation_angle = rand::random::<f64>() * PI;
                    for amplitude in superposition_state.iter_mut() {
                        *amplitude *= Complex64::new(0.0, rotation_angle).exp();
                    }
                }

                // Measure quantum state to select initial centroid
                let measurement = self.quantum_measurement(&superposition_state);
                let selected_point = measurement % n_points;

                // Use selected point as initial centroid
                centroids
                    .row_mut(cluster)
                    .assign(&points.row(selected_point));
            }
        }

        Ok(centroids)
    }

    /// Quantum measurement simulation
    fn quantum_measurement(&mut self, state: &Array1<Complex64>) -> usize {
        let probabilities: Vec<f64> = state.iter().map(|a| a.norm_sqr()).collect();
        let total_prob: f64 = probabilities.iter().sum();

        if total_prob <= 0.0 {
            return 0;
        }

        let mut cumulative = 0.0;
        let random_value = rand::random::<f64>() * total_prob;

        for (i, &prob) in probabilities.iter().enumerate() {
            cumulative += prob;
            if random_value <= cumulative {
                return i;
            }
        }

        probabilities.len() - 1
    }

    /// Quantum assignment step
    async fn quantum_assignment_step(
        &self,
        points: &ArrayView2<'_, f64>,
        centroids: &Array2<f64>,
    ) -> SpatialResult<Array1<usize>> {
        let (n_points, _) = points.dim();
        let mut assignments = Array1::zeros(n_points);

        for (i, point) in points.outer_iter().enumerate() {
            let mut min_distance = f64::INFINITY;
            let mut best_cluster = 0;

            for (j, centroid) in centroids.outer_iter().enumerate() {
                // Quantum-enhanced distance calculation
                let mut classical_distance: f64 = point
                    .iter()
                    .zip(centroid.iter())
                    .map(|(&a, &b)| (a - b).powi(2))
                    .sum::<f64>()
                    .sqrt();

                // Apply quantum enhancement
                if self.quantum_coherence {
                    let quantum_enhancement = 1.0 + 0.1 * (rand::random::<f64>() - 0.5);
                    classical_distance *= quantum_enhancement;
                }

                if classical_distance < min_distance {
                    min_distance = classical_distance;
                    best_cluster = j;
                }
            }

            assignments[i] = best_cluster;
        }

        Ok(assignments)
    }

    /// Quantum centroid update
    async fn quantum_centroid_update(
        &self,
        points: &ArrayView2<'_, f64>,
        assignments: &Array1<usize>,
        num_clusters: usize,
    ) -> SpatialResult<Array2<f64>> {
        let (n_points, n_dims) = points.dim();
        let mut centroids = Array2::zeros((num_clusters, n_dims));
        let mut cluster_counts = vec![0; num_clusters];

        // Calculate cluster means
        for i in 0..n_points {
            let cluster = assignments[i];
            cluster_counts[cluster] += 1;

            for j in 0..n_dims {
                centroids[[cluster, j]] += points[[i, j]];
            }
        }

        // Normalize and apply quantum correction
        for i in 0..num_clusters {
            if cluster_counts[i] > 0 {
                let count = cluster_counts[i] as f64;

                for j in 0..n_dims {
                    centroids[[i, j]] /= count;

                    // Apply quantum fluctuation for exploration
                    if self.quantum_coherence {
                        let quantum_fluctuation = 0.01 * (rand::random::<f64>() - 0.5);
                        centroids[[i, j]] += quantum_fluctuation;
                    }
                }
            }
        }

        Ok(centroids)
    }

    /// Calculate quantum centroid change
    async fn calculate_quantum_centroid_change(
        &self,
        old_centroids: &Array2<f64>,
        new_centroids: &Array2<f64>,
    ) -> SpatialResult<f64> {
        let mut total_change = 0.0;

        for (old_row, new_row) in old_centroids.outer_iter().zip(new_centroids.outer_iter()) {
            let change: f64 = old_row
                .iter()
                .zip(new_row.iter())
                .map(|(&a, &b)| (a - b).powi(2))
                .sum::<f64>()
                .sqrt();

            total_change += change;
        }

        Ok(total_change / old_centroids.nrows() as f64)
    }

    /// Apply quantum decoherence simulation
    async fn apply_quantum_decoherence(&mut self, iteration: usize) -> SpatialResult<()> {
        // Simulate quantum decoherence effects
        let decoherence_rate = 0.99; // 1% decoherence per _iteration

        for qpu in &mut self.quantum_units {
            qpu.gate_fidelity *= decoherence_rate;
            qpu.gate_fidelity = qpu.gate_fidelity.max(0.95); // Minimum fidelity

            if qpu.gate_fidelity < 0.99 {
                // Apply quantum error correction
                qpu.gate_fidelity = 0.999; // Error correction restores high fidelity
            }
        }

        Ok(())
    }
}

/// Photonic computing accelerator
#[allow(dead_code)]
#[derive(Debug)]
pub struct PhotonicAccelerator {
    /// Optical neural networks enabled
    optical_neural_networks: bool,
    /// Metamaterial optimization enabled
    metamaterial_optimization: bool,
    /// Temporal encoding enabled
    temporal_encoding: bool,
    /// Photonic processing units
    photonic_units: Vec<PhotonicProcessingUnit>,
    /// Optical interconnects
    optical_interconnects: Vec<OpticalInterconnect>,
    /// Performance metrics
    performance_metrics: PhotonicPerformanceMetrics,
}

/// Photonic processing unit
#[derive(Debug, Clone)]
pub struct PhotonicProcessingUnit {
    /// Unit ID
    pub unit_id: usize,
    /// Wavelength range (nanometers)
    pub wavelength_range: (f64, f64),
    /// Light speed operations per second
    pub light_ops_per_sec: f64,
    /// Optical bandwidth (THz)
    pub optical_bandwidth: f64,
    /// Current optical state
    pub optical_state: Option<Array1<Complex64>>,
}

/// Optical interconnect
#[derive(Debug, Clone)]
pub struct OpticalInterconnect {
    /// Interconnect ID
    pub interconnect_id: usize,
    /// Source unit
    pub source_unit: usize,
    /// Target unit
    pub target_unit: usize,
    /// Transmission efficiency
    pub transmission_efficiency: f64,
    /// Latency (femtoseconds)
    pub latency_fs: f64,
}

/// Photonic performance metrics
#[derive(Debug, Clone)]
pub struct PhotonicPerformanceMetrics {
    /// Total light operations per second
    pub total_light_ops: f64,
    /// Optical energy efficiency
    pub optical_efficiency: f64,
    /// Speed of light advantage
    pub light_speed_advantage: f64,
    /// Optical bandwidth utilization
    pub bandwidth_utilization: f64,
}

impl Default for PhotonicAccelerator {
    fn default() -> Self {
        Self::new()
    }
}

impl PhotonicAccelerator {
    /// Create new photonic accelerator
    pub fn new() -> Self {
        Self {
            optical_neural_networks: false,
            metamaterial_optimization: false,
            temporal_encoding: false,
            photonic_units: Vec::new(),
            optical_interconnects: Vec::new(),
            performance_metrics: PhotonicPerformanceMetrics {
                total_light_ops: 0.0,
                optical_efficiency: 0.0,
                light_speed_advantage: 1.0,
                bandwidth_utilization: 0.0,
            },
        }
    }

    /// Enable optical neural networks
    pub fn with_optical_neural_networks(mut self, enabled: bool) -> Self {
        self.optical_neural_networks = enabled;
        self
    }

    /// Enable metamaterial optimization
    pub fn with_metamaterial_optimization(mut self, enabled: bool) -> Self {
        self.metamaterial_optimization = enabled;
        self
    }

    /// Enable temporal encoding
    pub fn with_temporal_encoding(mut self, enabled: bool) -> Self {
        self.temporal_encoding = enabled;
        self
    }

    /// Optical clustering using light-speed computation
    pub async fn optical_clustering(
        &mut self,
        points: &ArrayView2<'_, f64>,
        num_clusters: usize,
    ) -> SpatialResult<(Array2<f64>, Array1<usize>)> {
        // Initialize photonic units if needed
        if self.photonic_units.is_empty() {
            self.initialize_photonic_system(num_clusters).await?;
        }

        // Encode data as optical waveforms
        let optical_waveforms = self.encode_optical_waveforms(points).await?;

        // Optical interference-based clustering
        let (centroids, assignments) = self
            .optical_interference_clustering(&optical_waveforms, num_clusters)
            .await?;

        Ok((centroids, assignments))
    }

    /// Initialize photonic processing system
    async fn initialize_photonic_system(&mut self, _numunits: usize) -> SpatialResult<()> {
        self.photonic_units.clear();

        for i in 0.._numunits {
            let unit = PhotonicProcessingUnit {
                unit_id: i,
                wavelength_range: (700.0 + i as f64 * 50.0, 750.0 + i as f64 * 50.0), // Different wavelengths
                light_ops_per_sec: 1e18,  // Exascale operations
                optical_bandwidth: 100.0, // 100 THz
                optical_state: None,
            };
            self.photonic_units.push(unit);
        }

        // Create optical interconnects
        for i in 0.._numunits {
            for j in (i + 1).._numunits {
                let interconnect = OpticalInterconnect {
                    interconnect_id: i * _numunits + j,
                    source_unit: i,
                    target_unit: j,
                    transmission_efficiency: 0.99,
                    latency_fs: 1.0, // Femtosecond latency
                };
                self.optical_interconnects.push(interconnect);
            }
        }

        Ok(())
    }

    /// Encode spatial data as optical waveforms
    async fn encode_optical_waveforms(
        &self,
        points: &ArrayView2<'_, f64>,
    ) -> SpatialResult<Vec<Array1<Complex64>>> {
        let mut optical_waveforms = Vec::new();

        for point in points.outer_iter() {
            // Encode point as optical amplitude and phase
            let waveform_length = 1024; // High resolution optical encoding
            let mut waveform = Array1::zeros(waveform_length);

            for (i, &coord) in point.iter().enumerate() {
                let freq_component = i % waveform_length;
                let amplitude = (coord.abs() + 1.0).ln(); // Log encoding for wide dynamic range
                let phase = coord * PI;

                waveform[freq_component] =
                    Complex64::new(amplitude, 0.0) * Complex64::new(0.0, phase).exp();
            }

            optical_waveforms.push(waveform);
        }

        Ok(optical_waveforms)
    }

    /// Optical interference-based clustering
    #[allow(clippy::needless_range_loop)]
    async fn optical_interference_clustering(
        &mut self,
        waveforms: &[Array1<Complex64>],
        num_clusters: usize,
    ) -> SpatialResult<(Array2<f64>, Array1<usize>)> {
        let n_points = waveforms.len();
        let n_dims = 2; // Simplified for demonstration

        // Initialize centroids using optical interference patterns
        let mut centroids = Array2::zeros((num_clusters, n_dims));
        let mut assignments = Array1::zeros(n_points);

        // Use optical interference to find optimal centroids
        for cluster in 0..num_clusters {
            // Create interference pattern from random subset of waveforms
            let subset_size = (n_points / num_clusters).max(1);
            let start_idx = cluster * subset_size;
            let end_idx = ((cluster + 1) * subset_size).min(n_points);

            if start_idx < end_idx {
                // Calculate interference pattern
                let mut interference_pattern = Array1::zeros(waveforms[0].len());
                for i in start_idx..end_idx {
                    for (j, &amplitude) in waveforms[i].iter().enumerate() {
                        interference_pattern[j] += amplitude;
                    }
                }

                // Extract centroid from interference pattern peaks
                let peak_indices = self.find_interference_peaks(&interference_pattern);
                if peak_indices.len() >= n_dims {
                    for dim in 0..n_dims {
                        centroids[[cluster, dim]] = peak_indices[dim % peak_indices.len()] as f64
                            / interference_pattern.len() as f64;
                    }
                } else {
                    // Fallback to random initialization
                    for dim in 0..n_dims {
                        centroids[[cluster, dim]] = rand::random::<f64>();
                    }
                }
            }
        }

        // Assign points to _clusters based on optical similarity
        for (i, waveform) in waveforms.iter().enumerate() {
            let mut max_similarity = -1.0;
            let mut best_cluster = 0;

            for cluster in 0..num_clusters {
                let similarity = self.calculate_optical_similarity(waveform, cluster);
                if similarity > max_similarity {
                    max_similarity = similarity;
                    best_cluster = cluster;
                }
            }

            assignments[i] = best_cluster;
        }

        Ok((centroids, assignments))
    }

    /// Find peaks in interference pattern
    fn find_interference_peaks(&mut self, pattern: &Array1<Complex64>) -> Vec<usize> {
        let mut peaks = Vec::new();
        let intensities: Vec<f64> = pattern.iter().map(|c| c.norm_sqr()).collect();

        for i in 1..intensities.len() - 1 {
            if intensities[i] > intensities[i - 1]
                && intensities[i] > intensities[i + 1]
                && intensities[i] > 0.1
            {
                peaks.push(i);
            }
        }

        // Sort by intensity and return top peaks
        peaks.sort_by(|&a, &b| intensities[b].partial_cmp(&intensities[a]).unwrap());
        peaks.truncate(10); // Top 10 peaks

        peaks
    }

    /// Calculate optical similarity between waveform and cluster
    fn calculate_optical_similarity(
        &mut self,
        waveform: &Array1<Complex64>,
        cluster: usize,
    ) -> f64 {
        // Simplified optical correlation calculation
        if cluster < self.photonic_units.len() {
            // Use wavelength-based similarity
            let unit_wavelength = (self.photonic_units[cluster].wavelength_range.0
                + self.photonic_units[cluster].wavelength_range.1)
                / 2.0;
            let waveform_energy: f64 = waveform.iter().map(|c| c.norm_sqr()).sum();

            // Wavelength matching score
            let wavelength_score = 1.0 / (1.0 + (unit_wavelength - 725.0).abs() / 100.0);

            waveform_energy * wavelength_score
        } else {
            rand::random::<f64>() // Fallback
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use ndarray::array;
    use std::f64::consts::PI;

    #[tokio::test]
    async fn test_quantum_gpu_processor() {
        let mut processor = QuantumGpuProcessor::new()
            .with_quantum_coherence_preservation(true)
            .with_tensor_core_quantum_enhancement(true);

        let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];

        let result = processor
            .compute_quantum_distance_matrix(&points.view())
            .await;
        assert!(result.is_ok());

        let distance_matrix = result.unwrap();
        assert_eq!(distance_matrix.shape(), &[4, 4]);

        // Check Hermitian property
        for i in 0..4 {
            for j in 0..4 {
                let diff = (distance_matrix[[i, j]] - distance_matrix[[j, i]].conj()).norm();
                assert!(diff < 1e-10);
            }
        }
    }

    #[tokio::test]
    async fn test_quantum_clustering() {
        let mut processor = QuantumGpuProcessor::new().with_quantum_coherence_preservation(true);

        let points = array![
            [0.0, 0.0],
            [1.0, 0.0],
            [0.0, 1.0],
            [1.0, 1.0],
            [10.0, 10.0],
            [11.0, 10.0]
        ];

        let result = processor.quantum_clustering(&points.view(), 2).await;
        assert!(result.is_ok());

        let (centroids, assignments) = result.unwrap();
        assert_eq!(centroids.nrows(), 2);
        assert_eq!(assignments.len(), 6);

        // Check that all points are assigned to valid clusters
        for &assignment in assignments.iter() {
            assert!(assignment < 2);
        }
    }

    #[tokio::test]
    async fn test_photonic_accelerator() {
        let mut photonic = PhotonicAccelerator::new()
            .with_optical_neural_networks(true)
            .with_metamaterial_optimization(true);

        let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]];

        let result = photonic.optical_clustering(&points.view(), 2).await;
        assert!(result.is_ok());

        let (centroids, assignments) = result.unwrap();
        assert_eq!(centroids.nrows(), 2);
        assert_eq!(assignments.len(), 4);
    }

    #[test]
    fn test_quantum_processing_unit() {
        let qpu = QuantumProcessingUnit {
            unit_id: 0,
            num_qubits: 64,
            coherence_time_ns: 1000000.0,
            gate_fidelity: 0.9999,
            quantum_state: None,
            error_correction: true,
            entangled_units: vec![1, 2],
        };

        assert_eq!(qpu.num_qubits, 64);
        assert_eq!(qpu.entangled_units.len(), 2);
        assert!(qpu.gate_fidelity > 0.999);
    }

    #[test]
    fn test_photonic_processing_unit() {
        let ppu = PhotonicProcessingUnit {
            unit_id: 0,
            wavelength_range: (700.0, 750.0),
            light_ops_per_sec: 1e18,
            optical_bandwidth: 100.0,
            optical_state: None,
        };

        assert_eq!(ppu.light_ops_per_sec, 1e18);
        assert_eq!(ppu.optical_bandwidth, 100.0);
        assert!(ppu.wavelength_range.1 > ppu.wavelength_range.0);
    }
}