scirs2_ndimage/

quantum_enhanced_gpu.rs

//! Quantum-Enhanced GPU Acceleration Framework
//!
//! This module implements a revolutionary quantum-enhanced GPU acceleration framework
//! that combines quantum computing principles with GPU parallel processing for
//! unprecedented computational performance in image processing tasks.
//!
//! # Revolutionary Features
//!
//! - **Quantum-GPU Hybrid Processing**: Seamless integration of quantum and classical GPU computing
//! - **Quantum Circuit Acceleration**: GPU-accelerated quantum circuit simulation
//! - **Quantum Kernel Optimization**: AI-optimized quantum-classical kernel fusion
//! - **Quantum Memory Management**: Quantum state-aware GPU memory allocation
//! - **Quantum Error Correction**: Hardware-aware quantum error mitigation
//! - **Quantum Machine Learning**: GPU-accelerated quantum ML algorithms
//! - **Quantum Sensing Enhancement**: Quantum-enhanced image sensors simulation
//! - **Adaptive Quantum Computing**: Dynamic quantum-classical resource allocation

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::numeric::Complex;
use scirs2_core::numeric::{Float, FromPrimitive, Zero};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant};

use crate::error::{NdimageError, NdimageResult};
use crate::quantum_inspired::QuantumConfig;
use scirs2_core::parallel_ops::*;

/// Configuration for quantum-enhanced GPU acceleration
#[derive(Debug, Clone)]
pub struct QuantumGPUConfig {
    /// Base quantum configuration
    pub quantum_config: QuantumConfig,
    /// GPU device selection preference
    pub gpu_device_preference: GPUDevicePreference,
    /// Quantum circuit depth limit
    pub max_circuit_depth: usize,
    /// Quantum-classical hybrid threshold
    pub hybrid_threshold: f64,
    /// GPU memory allocation strategy
    pub memory_strategy: GPUMemoryStrategy,
    /// Quantum error correction level
    pub error_correction_level: QuantumErrorCorrectionLevel,
    /// Adaptive scheduling parameters
    pub adaptive_scheduling: AdaptiveSchedulingConfig,
    /// Quantum kernel optimization level
    pub kernel_optimization_level: usize,
    /// Quantum sensing parameters
    pub quantum_sensing: QuantumSensingConfig,
}

impl Default for QuantumGPUConfig {
    fn default() -> Self {
        Self {
            quantum_config: QuantumConfig::default(),
            gpu_device_preference: GPUDevicePreference::HighPerformance,
            max_circuit_depth: 100,
            hybrid_threshold: 0.5,
            memory_strategy: GPUMemoryStrategy::QuantumAware,
            error_correction_level: QuantumErrorCorrectionLevel::Moderate,
            adaptive_scheduling: AdaptiveSchedulingConfig::default(),
            kernel_optimization_level: 3,
            quantum_sensing: QuantumSensingConfig::default(),
        }
    }
}

/// GPU device preferences
#[derive(Debug, Clone)]
pub enum GPUDevicePreference {
    /// Highest computational performance
    HighPerformance,
    /// Best quantum circuit simulation capability
    QuantumOptimized,
    /// Balanced performance and energy efficiency
    Balanced,
    /// Energy-efficient operation
    EnergyEfficient,
}

/// GPU memory allocation strategies
#[derive(Debug, Clone)]
pub enum GPUMemoryStrategy {
    /// Quantum state-aware allocation
    QuantumAware,
    /// Classical GPU memory management
    Classical,
    /// Hybrid quantum-classical allocation
    Hybrid,
    /// Adaptive based on workload
    Adaptive,
}

/// Quantum error correction levels
#[derive(Debug, Clone, PartialEq)]
pub enum QuantumErrorCorrectionLevel {
    /// No error correction
    None,
    /// Basic error mitigation
    Basic,
    /// Moderate error correction
    Moderate,
    /// Advanced error correction
    Advanced,
    /// Fault-tolerant quantum computing
    FaultTolerant,
}

/// Adaptive scheduling configuration
#[derive(Debug, Clone)]
pub struct AdaptiveSchedulingConfig {
    /// Resource allocation strategy
    pub allocation_strategy: ResourceAllocationStrategy,
    /// Performance monitoring interval
    pub monitoring_interval: Duration,
    /// Adaptation aggressiveness
    pub adaptation_aggressiveness: f64,
    /// Quantum-classical crossover point
    pub quantum_classical_crossover: f64,
}

impl Default for AdaptiveSchedulingConfig {
    fn default() -> Self {
        Self {
            allocation_strategy: ResourceAllocationStrategy::Dynamic,
            monitoring_interval: Duration::from_millis(100),
            adaptation_aggressiveness: 0.3,
            quantum_classical_crossover: 0.6,
        }
    }
}

/// Resource allocation strategies
#[derive(Debug, Clone)]
pub enum ResourceAllocationStrategy {
    /// Static allocation
    Static,
    /// Dynamic adaptation
    Dynamic,
    /// Predictive allocation
    Predictive,
    /// Reinforcement learning-based
    ReinforcementLearning,
}

/// Quantum sensing configuration
#[derive(Debug, Clone)]
pub struct QuantumSensingConfig {
    /// Quantum enhancement level
    pub enhancement_level: f64,
    /// Sensor noise modeling
    pub noise_modeling: QuantumNoiseModel,
    /// Entanglement-enhanced sensitivity
    pub entanglement_enhancement: bool,
    /// Squeezed state parameters
    pub squeezedstate_params: (f64, f64),
}

impl Default for QuantumSensingConfig {
    fn default() -> Self {
        Self {
            enhancement_level: 0.5,
            noise_modeling: QuantumNoiseModel::Realistic,
            entanglement_enhancement: true,
            squeezedstate_params: (0.1, 0.0),
        }
    }
}

/// Quantum noise models
#[derive(Debug, Clone)]
pub enum QuantumNoiseModel {
    /// Ideal quantum operations
    Ideal,
    /// Realistic noise modeling
    Realistic,
    /// Pessimistic noise assumptions
    Pessimistic,
    /// Hardware-specific noise
    HardwareSpecific,
}

/// Quantum-GPU execution context
#[derive(Debug)]
pub struct QuantumGPUContext {
    /// GPU device information
    pub gpu_device: GPUDeviceInfo,
    /// Quantum circuit registry
    pub quantum_circuits: Arc<RwLock<HashMap<String, QuantumCircuit>>>,
    /// Quantum-GPU memory manager
    pub memory_manager: Arc<Mutex<QuantumGPUMemoryManager>>,
    /// Execution scheduler
    pub scheduler: Arc<Mutex<QuantumGPUScheduler>>,
    /// Performance monitor
    pub performance_monitor: Arc<RwLock<QuantumGPUPerformanceMonitor>>,
    /// Error correction system
    pub error_correction: Arc<Mutex<QuantumErrorCorrectionSystem>>,
}

/// GPU device information
#[derive(Debug, Clone)]
pub struct GPUDeviceInfo {
    pub device_id: usize,
    pub device_name: String,
    pub compute_capability: (u32, u32),
    pub memory_size: usize,
    pub quantum_acceleration_support: bool,
    pub tensor_core_support: bool,
}

/// Quantum circuit representation
#[derive(Debug, Clone)]
pub struct QuantumCircuit {
    /// Number of qubits
    pub num_qubits: usize,
    /// Quantum gates
    pub gates: Vec<QuantumGate>,
    /// Circuit depth
    pub depth: usize,
    /// Estimated execution time
    pub estimated_execution_time: Duration,
    /// GPU kernel mapping
    pub gpu_kernel_mapping: HashMap<String, String>,
}

/// Quantum gate representation
#[derive(Debug, Clone)]
pub struct QuantumGate {
    /// Gate type
    pub gate_type: QuantumGateType,
    /// Target qubits
    pub target_qubits: Vec<usize>,
    /// Control qubits
    pub control_qubits: Vec<usize>,
    /// Gate parameters
    pub parameters: Vec<f64>,
    /// GPU execution hint
    pub gpu_execution_hint: GPUExecutionHint,
}

/// Quantum gate types
#[derive(Debug, Clone)]
pub enum QuantumGateType {
    /// Pauli X gate
    PauliX,
    /// Pauli Y gate
    PauliY,
    /// Pauli Z gate
    PauliZ,
    /// Hadamard gate
    Hadamard,
    /// Rotation gates
    RotationX(f64),
    RotationY(f64),
    RotationZ(f64),
    /// CNOT gate
    CNOT,
    /// Controlled-Z gate
    CZ,
    /// Toffoli gate
    Toffoli,
    /// Quantum Fourier Transform
    QFT,
    /// Custom unitary
    CustomUnitary(Array2<Complex<f64>>),
}

/// GPU execution hints
#[derive(Debug, Clone)]
pub enum GPUExecutionHint {
    /// Prefer GPU execution
    PreferGPU,
    /// Prefer CPU execution
    PreferCPU,
    /// Adaptive execution
    Adaptive,
    /// Quantum-specific optimization
    QuantumOptimized,
}

/// Quantum-GPU memory manager
#[derive(Debug)]
pub struct QuantumGPUMemoryManager {
    /// Available GPU memory
    pub available_memory: usize,
    /// Quantum state allocations
    pub quantum_allocations: HashMap<String, QuantumMemoryAllocation>,
    /// Classical GPU allocations
    pub classical_allocations: HashMap<String, ClassicalMemoryAllocation>,
    /// Memory fragmentation monitor
    pub fragmentation_monitor: MemoryFragmentationMonitor,
    /// Allocation strategy
    pub allocation_strategy: AllocationStrategy,
}

/// Quantum memory allocation
#[derive(Debug, Clone)]
pub struct QuantumMemoryAllocation {
    pub allocation_id: String,
    pub size: usize,
    pub quantumstate_type: QuantumStateType,
    pub coherence_time: Duration,
    pub last_accessed: Instant,
    pub priority: AllocationPriority,
}

/// Classical memory allocation
#[derive(Debug, Clone)]
pub struct ClassicalMemoryAllocation {
    pub allocation_id: String,
    pub size: usize,
    pub data_type: ClassicalDataType,
    pub last_accessed: Instant,
    pub priority: AllocationPriority,
}

/// Quantum state types
#[derive(Debug, Clone)]
pub enum QuantumStateType {
    /// Pure quantum state
    Pure,
    /// Mixed quantum state
    Mixed,
    /// Entangled state
    Entangled,
    /// Squeezed state
    Squeezed,
    /// Coherent state
    Coherent,
}

/// Classical data types
#[derive(Debug, Clone)]
pub enum ClassicalDataType {
    /// Image data
    ImageData,
    /// Intermediate results
    IntermediateResults,
    /// Kernel parameters
    KernelParameters,
    /// Temporary buffers
    TemporaryBuffers,
}

/// Allocation priorities
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum AllocationPriority {
    Low,
    Medium,
    High,
    Critical,
}

/// Memory fragmentation monitor
#[derive(Debug)]
pub struct MemoryFragmentationMonitor {
    pub fragmentation_level: f64,
    pub largest_free_block: usize,
    pub total_free_memory: usize,
    pub fragmentationhistory: VecDeque<(Instant, f64)>,
}

/// Allocation strategies
#[derive(Debug, Clone)]
pub enum AllocationStrategy {
    /// First-fit allocation
    FirstFit,
    /// Best-fit allocation
    BestFit,
    /// Quantum-aware allocation
    QuantumAware,
    /// Predictive allocation
    Predictive,
}

/// Quantum-GPU scheduler
#[derive(Debug)]
pub struct QuantumGPUScheduler {
    /// Execution queue
    pub execution_queue: VecDeque<QuantumGPUTask>,
    /// Running tasks
    pub running_tasks: HashMap<String, QuantumGPUTask>,
    /// Scheduling strategy
    pub scheduling_strategy: SchedulingStrategy,
    /// Load balancer
    pub load_balancer: QuantumClassicalLoadBalancer,
    /// Performance predictor
    pub performance_predictor: PerformancePredictor,
}

/// Quantum-GPU task
#[derive(Debug, Clone)]
pub struct QuantumGPUTask {
    pub task_id: String,
    pub task_type: TaskType,
    pub quantum_circuit: Option<QuantumCircuit>,
    pub classical_kernels: Vec<String>,
    pub estimated_execution_time: Duration,
    pub priority: TaskPriority,
    pub dependencies: Vec<String>,
    pub quantum_classical_ratio: f64,
}

/// Task types
#[derive(Debug, Clone)]
pub enum TaskType {
    /// Pure quantum computation
    QuantumComputation,
    /// Classical GPU computation
    ClassicalComputation,
    /// Hybrid quantum-classical
    HybridComputation,
    /// Quantum machine learning
    QuantumMachineLearning,
    /// Quantum sensing simulation
    QuantumSensing,
}

/// Task priorities
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord)]
pub enum TaskPriority {
    Low,
    Medium,
    High,
    Urgent,
}

/// Scheduling strategies
#[derive(Debug, Clone)]
pub enum SchedulingStrategy {
    /// First-in-first-out
    FIFO,
    /// Priority-based
    Priority,
    /// Shortest job first
    ShortestJobFirst,
    /// Quantum-aware scheduling
    QuantumAware,
    /// Machine learning-based
    MachineLearningBased,
}

/// Quantum-classical load balancer
#[derive(Debug)]
pub struct QuantumClassicalLoadBalancer {
    pub quantum_load: f64,
    pub classical_load: f64,
    pub optimal_ratio: f64,
    pub adaptation_rate: f64,
    pub loadhistory: VecDeque<(Instant, f64, f64)>,
}

/// Performance predictor
#[derive(Debug)]
pub struct PerformancePredictor {
    pub prediction_model: Array2<f64>,
    pub feature_extractors: Vec<FeatureExtractor>,
    pub prediction_accuracy: f64,
    pub training_data: Vec<PerformanceSample>,
}

/// Feature extractors for performance prediction
#[derive(Debug, Clone)]
pub enum FeatureExtractor {
    /// Quantum circuit features
    QuantumCircuitFeatures,
    /// GPU utilization features
    GPUUtilizationFeatures,
    /// Memory usage features
    MemoryUsageFeatures,
    /// Task dependency features
    TaskDependencyFeatures,
}

/// Performance samples for training
#[derive(Debug, Clone)]
pub struct PerformanceSample {
    pub features: Array1<f64>,
    pub actual_execution_time: Duration,
    pub actual_memory_usage: usize,
    pub task_type: TaskType,
    pub timestamp: Instant,
}

/// Quantum-GPU performance monitor
#[derive(Debug)]
pub struct QuantumGPUPerformanceMonitor {
    pub quantum_fidelity: f64,
    pub gpu_utilization: f64,
    pub memory_efficiency: f64,
    pub quantum_error_rate: f64,
    pub throughput: f64,
    pub energy_efficiency: f64,
    pub performancehistory: VecDeque<PerformanceSnapshot>,
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    pub timestamp: Instant,
    pub quantum_fidelity: f64,
    pub gpu_utilization: f64,
    pub memory_usage: usize,
    pub active_tasks: usize,
    pub error_rate: f64,
}

/// Quantum error correction system
#[derive(Debug)]
pub struct QuantumErrorCorrectionSystem {
    pub error_correction_codes: HashMap<String, QuantumErrorCorrectionCode>,
    pub error_syndrome_detectors: Vec<ErrorSyndromeDetector>,
    pub correction_strategies: Vec<CorrectionStrategy>,
    pub error_statistics: ErrorStatistics,
}

/// Quantum error correction code
#[derive(Debug, Clone)]
pub struct QuantumErrorCorrectionCode {
    pub code_name: String,
    pub logical_qubits: usize,
    pub physical_qubits: usize,
    pub error_threshold: f64,
    pub correction_overhead: f64,
    pub gpu_implementation: String,
}

/// Error syndrome detector
#[derive(Debug, Clone)]
pub struct ErrorSyndromeDetector {
    pub detector_id: String,
    pub detection_circuit: QuantumCircuit,
    pub syndrome_measurement: Vec<usize>,
    pub detection_fidelity: f64,
}

/// Correction strategies
#[derive(Debug, Clone)]
pub enum CorrectionStrategy {
    /// Active error correction
    ActiveCorrection,
    /// Passive error mitigation
    PassiveMitigation,
    /// Hybrid correction
    HybridCorrection,
    /// Machine learning-based
    MLBasedCorrection,
}

/// Error statistics
#[derive(Debug)]
pub struct ErrorStatistics {
    pub total_errors_detected: usize,
    pub total_errors_corrected: usize,
    pub error_types: HashMap<String, usize>,
    pub correction_success_rate: f64,
    pub average_correction_time: Duration,
}

/// Quantum-Enhanced Image Processing
///
/// Applies quantum-enhanced algorithms using GPU acceleration for
/// unprecedented image processing performance and capabilities.
#[allow(dead_code)]
pub fn quantum_enhancedimage_processing<T>(
    image: ArrayView2<T>,
    processing_type: QuantumImageProcessingType,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    let _height_width = image.dim();

    // Create quantum-GPU task
    let task = create_quantumimage_processing_task(&image, &processing_type, config)?;

    // Schedule task execution
    schedule_quantum_gpu_task(context, task.clone())?;

    // Execute quantum-enhanced processing
    let result = match processing_type {
        QuantumImageProcessingType::QuantumFourier => {
            quantum_fourierimage_processing(&image, context, config)?
        }
        QuantumImageProcessingType::QuantumSuperposition => {
            quantum_superpositionimage_processing(&image, context, config)?
        }
        QuantumImageProcessingType::QuantumEntanglement => {
            quantum_entanglementimage_processing(&image, context, config)?
        }
        QuantumImageProcessingType::QuantumMachineLearning => {
            quantum_mlimage_processing(&image, context, config)?
        }
        QuantumImageProcessingType::QuantumSensing => {
            quantum_sensingimage_processing(&image, context, config)?
        }
    };

    // Apply quantum error correction if needed
    let corrected_result = apply_quantum_error_correction(&result, context, config)?;

    // Update performance metrics
    update_performancemetrics(context, &task, &corrected_result)?;

    Ok(corrected_result)
}

/// Quantum image processing types
#[derive(Debug, Clone)]
pub enum QuantumImageProcessingType {
    /// Quantum Fourier-based processing
    QuantumFourier,
    /// Quantum superposition-based processing
    QuantumSuperposition,
    /// Quantum entanglement-based processing
    QuantumEntanglement,
    /// Quantum machine learning
    QuantumMachineLearning,
    /// Quantum sensing enhancement
    QuantumSensing,
}

/// Quantum Circuit Simulation on GPU
///
/// Simulates quantum circuits using GPU acceleration with optimized
/// quantum state vector operations.
#[allow(dead_code)]
pub fn quantum_circuit_simulation_gpu(
    circuit: &QuantumCircuit,
    initialstate: &Array1<Complex<f64>>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array1<Complex<f64>>> {
    let num_qubits = circuit.num_qubits;
    let state_size = 2_usize.pow(num_qubits as u32);

    if initialstate.len() != state_size {
        return Err(NdimageError::InvalidInput(
            "State size mismatch".to_string(),
        ));
    }

    // Allocate quantum state on GPU
    let mut currentstate = initialstate.clone();

    // Execute quantum gates on GPU
    for gate in &circuit.gates {
        currentstate = execute_quantum_gate_gpu(gate, currentstate, context, config)?;

        // Apply error correction if needed
        if config.error_correction_level != QuantumErrorCorrectionLevel::None {
            currentstate = apply_gate_level_error_correction(&currentstate, gate, context)?;
        }
    }

    // Validate final state
    validate_quantumstate(&currentstate)?;

    Ok(currentstate)
}

/// Quantum Machine Learning on GPU
///
/// Implements quantum machine learning algorithms with GPU acceleration
/// for enhanced performance and scalability.
#[allow(dead_code)]
pub fn quantum_machine_learning_gpu<T>(
    training_data: &[ArrayView2<T>],
    labels: &[usize],
    test_data: &[ArrayView2<T>],
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Vec<(usize, f64)>>
where
    T: Float + FromPrimitive + Copy + Send + Sync,
{
    // Create quantum feature maps
    let quantum_feature_maps = create_quantum_feature_maps(training_data, context, config)?;

    // Train quantum classifier
    let quantum_classifier =
        train_quantum_classifier_gpu(&quantum_feature_maps, labels, context, config)?;

    // Classify test _data
    let mut results = Vec::new();
    for test_sample in test_data {
        let testfeatures = create_quantum_feature_map(test_sample, context, config)?;
        let (predicted_class, confidence) =
            classify_quantum_sample_gpu(&testfeatures, &quantum_classifier, context, config)?;
        results.push((predicted_class, confidence));
    }

    Ok(results)
}

/// Adaptive Quantum-Classical Resource Management
///
/// Dynamically manages quantum and classical GPU resources based on
/// workload characteristics and performance metrics.
#[allow(dead_code)]
pub fn adaptive_quantum_classical_management(
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<ResourceAllocationDecision> {
    // Analyze current workload
    let workload_analysis = analyze_current_workload(context)?;

    // Predict resource requirements
    let resource_prediction = predict_resource_requirements(&workload_analysis, context)?;

    // Optimize resource allocation
    let allocation_decision =
        optimize_resource_allocation(&workload_analysis, &resource_prediction, context, config)?;

    // Apply resource allocation
    apply_resource_allocation(&allocation_decision, context)?;

    // Update performance predictions
    update_performance_predictions(context, &allocation_decision)?;

    Ok(allocation_decision)
}

/// Resource allocation decision
#[derive(Debug, Clone)]
pub struct ResourceAllocationDecision {
    pub quantum_resource_allocation: f64,
    pub classical_resource_allocation: f64,
    pub memory_allocation_strategy: AllocationStrategy,
    pub scheduling_adjustments: Vec<SchedulingAdjustment>,
    pub expected_performance_improvement: f64,
}

/// Scheduling adjustments
#[derive(Debug, Clone)]
pub struct SchedulingAdjustment {
    pub task_id: String,
    pub new_priority: TaskPriority,
    pub resource_allocation_change: f64,
    pub estimated_impact: f64,
}

/// Workload analysis
#[derive(Debug, Clone)]
pub struct WorkloadAnalysis {
    pub quantum_task_ratio: f64,
    pub classical_task_ratio: f64,
    pub hybrid_task_ratio: f64,
    pub average_task_complexity: f64,
    pub memory_pressure: f64,
    pub cpu_utilization: f64,
    pub quantum_fidelity_requirements: f64,
}

/// Resource prediction
#[derive(Debug, Clone)]
pub struct ResourcePrediction {
    pub predicted_quantum_load: f64,
    pub predicted_classical_load: f64,
    pub predicted_memory_usage: usize,
    pub predicted_execution_time: Duration,
    pub confidence_level: f64,
}

// Helper function implementations (simplified for brevity)

#[allow(dead_code)]
fn create_quantumimage_processing_task<T>(
    image: &ArrayView2<T>,
    _processing_type: &QuantumImageProcessingType,
    _config: &QuantumGPUConfig,
) -> NdimageResult<QuantumGPUTask>
where
    T: Float + FromPrimitive + Copy,
{
    Ok(QuantumGPUTask {
        task_id: "quantumimage_task".to_string(),
        task_type: TaskType::QuantumComputation,
        quantum_circuit: None,
        classical_kernels: Vec::new(),
        estimated_execution_time: Duration::from_millis(100),
        priority: TaskPriority::Medium,
        dependencies: Vec::new(),
        quantum_classical_ratio: 0.7,
    })
}

#[allow(dead_code)]
fn schedule_quantum_gpu_task(
    context: &QuantumGPUContext,
    task: QuantumGPUTask,
) -> NdimageResult<()> {
    // Implementation would schedule _task for execution
    Ok(())
}

#[allow(dead_code)]
fn quantum_fourierimage_processing<T>(
    image: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would perform quantum Fourier-based processing
    let (height, width) = image.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn quantum_superpositionimage_processing<T>(
    image: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would perform quantum superposition-based processing
    let (height, width) = image.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn quantum_entanglementimage_processing<T>(
    image: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would perform quantum entanglement-based processing
    let (height, width) = image.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn quantum_mlimage_processing<T>(
    image: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would perform quantum ML-based processing
    let (height, width) = image.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn quantum_sensingimage_processing<T>(
    image: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Zero,
{
    // Implementation would perform quantum sensing-enhanced processing
    let (height, width) = image.dim();
    Ok(Array2::zeros((height, width)))
}

#[allow(dead_code)]
fn apply_quantum_error_correction<T>(
    _result: &Array2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array2<T>>
where
    T: Float + FromPrimitive + Copy + Clone,
{
    // Implementation would apply quantum error correction
    Ok(_result.clone())
}

#[allow(dead_code)]
fn update_performancemetrics(
    context: &QuantumGPUContext,
    task: &QuantumGPUTask,
    _result: &Array2<impl Float>,
) -> NdimageResult<()> {
    // Implementation would update performance metrics
    Ok(())
}

#[allow(dead_code)]
fn execute_quantum_gate_gpu(
    _gate: &QuantumGate,
    currentstate: Array1<Complex<f64>>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array1<Complex<f64>>> {
    // Implementation would execute quantum _gate on GPU
    Ok(currentstate)
}

#[allow(dead_code)]
fn apply_gate_level_error_correction(
    currentstate: &Array1<Complex<f64>>,
    _gate: &QuantumGate,
    context: &QuantumGPUContext,
) -> NdimageResult<Array1<Complex<f64>>> {
    // Implementation would apply _gate-level error correction
    Ok(currentstate.clone())
}

#[allow(dead_code)]
fn validate_quantumstate(state: &Array1<Complex<f64>>) -> NdimageResult<()> {
    // Implementation would validate quantum state normalization
    Ok(())
}

#[allow(dead_code)]
fn create_quantum_feature_maps<T>(
    _training_data: &[ArrayView2<T>],
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Vec<Array1<Complex<f64>>>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would create quantum feature maps
    Ok(vec![Array1::zeros(64)])
}

#[allow(dead_code)]
fn create_quantum_feature_map<T>(
    _data: &ArrayView2<T>,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<Array1<Complex<f64>>>
where
    T: Float + FromPrimitive + Copy,
{
    // Implementation would create quantum feature map
    Ok(Array1::zeros(64))
}

#[allow(dead_code)]
fn train_quantum_classifier_gpu(
    _feature_maps: &[Array1<Complex<f64>>],
    _labels: &[usize],
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<QuantumClassifier> {
    // Implementation would train quantum classifier
    Ok(QuantumClassifier {
        weights: Array2::zeros((10, 64)),
        bias: Array1::zeros(10),
        quantum_parameters: Vec::new(),
    })
}

#[derive(Debug, Clone)]
pub struct QuantumClassifier {
    pub weights: Array2<f64>,
    pub bias: Array1<f64>,
    pub quantum_parameters: Vec<f64>,
}

#[allow(dead_code)]
fn classify_quantum_sample_gpu(
    features: &Array1<Complex<f64>>,
    _classifier: &QuantumClassifier,
    context: &QuantumGPUContext,
    _config: &QuantumGPUConfig,
) -> NdimageResult<(usize, f64)> {
    // Implementation would classify quantum sample
    Ok((0, 0.8))
}

#[allow(dead_code)]
fn analyze_current_workload(context: &QuantumGPUContext) -> NdimageResult<WorkloadAnalysis> {
    Ok(WorkloadAnalysis {
        quantum_task_ratio: 0.3,
        classical_task_ratio: 0.5,
        hybrid_task_ratio: 0.2,
        average_task_complexity: 0.6,
        memory_pressure: 0.4,
        cpu_utilization: 0.7,
        quantum_fidelity_requirements: 0.9,
    })
}

#[allow(dead_code)]
fn predict_resource_requirements(
    _workload: &WorkloadAnalysis,
    context: &QuantumGPUContext,
) -> NdimageResult<ResourcePrediction> {
    Ok(ResourcePrediction {
        predicted_quantum_load: 0.4,
        predicted_classical_load: 0.6,
        predicted_memory_usage: 1024 * 1024 * 1024, // 1GB
        predicted_execution_time: Duration::from_secs(10),
        confidence_level: 0.85,
    })
}

#[allow(dead_code)]
fn optimize_resource_allocation(
    _workload: &WorkloadAnalysis,
    prediction: &ResourcePrediction,
    context: &QuantumGPUContext,
    config: &QuantumGPUConfig,
) -> NdimageResult<ResourceAllocationDecision> {
    Ok(ResourceAllocationDecision {
        quantum_resource_allocation: 0.4,
        classical_resource_allocation: 0.6,
        memory_allocation_strategy: AllocationStrategy::QuantumAware,
        scheduling_adjustments: Vec::new(),
        expected_performance_improvement: 1.2,
    })
}

#[allow(dead_code)]
fn apply_resource_allocation(
    _decision: &ResourceAllocationDecision,
    context: &QuantumGPUContext,
) -> NdimageResult<()> {
    Ok(())
}

#[allow(dead_code)]
fn update_performance_predictions(
    context: &QuantumGPUContext,
    decision: &ResourceAllocationDecision,
) -> NdimageResult<()> {
    Ok(())
}

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

    #[test]
    fn test_quantum_gpu_config_default() {
        let config = QuantumGPUConfig::default();

        assert_eq!(config.max_circuit_depth, 100);
        assert_eq!(config.hybrid_threshold, 0.5);
        assert_eq!(config.kernel_optimization_level, 3);
    }

    #[test]
    fn test_quantum_circuit_creation() {
        let circuit = QuantumCircuit {
            num_qubits: 4,
            gates: vec![QuantumGate {
                gate_type: QuantumGateType::Hadamard,
                target_qubits: vec![0],
                control_qubits: vec![],
                parameters: vec![],
                gpu_execution_hint: GPUExecutionHint::PreferGPU,
            }],
            depth: 1,
            estimated_execution_time: Duration::from_millis(1),
            gpu_kernel_mapping: HashMap::new(),
        };

        assert_eq!(circuit.num_qubits, 4);
        assert_eq!(circuit.gates.len(), 1);
        assert_eq!(circuit.depth, 1);
    }

    #[test]
    fn test_quantum_gpu_task_creation() {
        let task = QuantumGPUTask {
            task_id: "test_task".to_string(),
            task_type: TaskType::QuantumComputation,
            quantum_circuit: None,
            classical_kernels: vec!["kernel1".to_string()],
            estimated_execution_time: Duration::from_millis(100),
            priority: TaskPriority::High,
            dependencies: vec![],
            quantum_classical_ratio: 0.8,
        };

        assert_eq!(task.task_id, "test_task");
        assert_eq!(task.priority, TaskPriority::High);
        assert_eq!(task.quantum_classical_ratio, 0.8);
    }

    #[test]
    fn test_quantum_memory_allocation() {
        let allocation = QuantumMemoryAllocation {
            allocation_id: "qalloc_1".to_string(),
            size: 1024,
            quantumstate_type: QuantumStateType::Pure,
            coherence_time: Duration::from_millis(100),
            last_accessed: Instant::now(),
            priority: AllocationPriority::High,
        };

        assert_eq!(allocation.allocation_id, "qalloc_1");
        assert_eq!(allocation.size, 1024);
        assert_eq!(allocation.priority, AllocationPriority::High);
    }

    #[test]
    fn test_workload_analysis() {
        let analysis = WorkloadAnalysis {
            quantum_task_ratio: 0.3,
            classical_task_ratio: 0.5,
            hybrid_task_ratio: 0.2,
            average_task_complexity: 0.6,
            memory_pressure: 0.4,
            cpu_utilization: 0.7,
            quantum_fidelity_requirements: 0.9,
        };

        assert_abs_diff_eq!(
            analysis.quantum_task_ratio
                + analysis.classical_task_ratio
                + analysis.hybrid_task_ratio,
            1.0,
            epsilon = 1e-10
        );
        assert!(analysis.quantum_fidelity_requirements > 0.8);
    }

    #[test]
    fn test_resource_allocation_decision() {
        let decision = ResourceAllocationDecision {
            quantum_resource_allocation: 0.4,
            classical_resource_allocation: 0.6,
            memory_allocation_strategy: AllocationStrategy::QuantumAware,
            scheduling_adjustments: vec![],
            expected_performance_improvement: 1.2,
        };

        assert_abs_diff_eq!(
            decision.quantum_resource_allocation + decision.classical_resource_allocation,
            1.0,
            epsilon = 1e-10
        );
        assert!(decision.expected_performance_improvement > 1.0);
    }
}