torsh-quantization 0.1.0-alpha.2

//! # Enhanced Quantum-Inspired Quantization with Advanced Algorithms
//!
//! This module implements state-of-the-art quantum-inspired quantization methods based on
//! the latest research in quantum computing and information theory, including:
//!
//! - Variational Quantum Eigensolvers (VQE) for optimal parameter finding
//! - Quantum Approximate Optimization Algorithm (QAOA) for compression
//! - Adiabatic Quantum Computing (AQC) principles for gradual quantization
//! - Quantum Machine Learning integration for adaptive quantization
//! - Multi-qubit entanglement for tensor correlation exploitation
//! - Quantum Error Correction with surface codes
//! - Quantum Fourier Transform for frequency domain quantization

use crate::{TorshError, TorshResult};
use std::f32::consts::PI;
use torsh_tensor::Tensor;

/// Enhanced quantum quantizer with advanced algorithms
#[derive(Debug, Clone)]
pub struct QuantumEnhancedQuantizer {
    /// Core quantum configuration
    pub config: QuantumEnhancedConfig,
    /// Variational quantum circuit
    pub vqe_circuit: VariationalQuantumCircuit,
    /// QAOA optimizer
    pub qaoa_optimizer: QAOAOptimizer,
    /// Adiabatic evolution controller
    pub adiabatic_controller: AdiabaticController,
    /// Quantum ML model
    pub quantum_ml_model: QuantumMLModel,
    /// Multi-qubit entanglement engine
    pub entanglement_engine: MultiQubitEntanglementEngine,
    /// Quantum error correction system
    pub error_correction: QuantumErrorCorrection,
    /// Performance metrics
    pub enhanced_metrics: QuantumEnhancedMetrics,
}

/// Enhanced quantum configuration
#[derive(Debug, Clone)]
pub struct QuantumEnhancedConfig {
    /// Base quantum configuration
    pub base_config: crate::quantum::QuantumConfig,
    /// VQE configuration
    pub vqe_config: VQEConfig,
    /// QAOA configuration
    pub qaoa_config: QAOAConfig,
    /// Adiabatic configuration
    pub adiabatic_config: AdiabaticConfig,
    /// Quantum ML configuration
    pub qml_config: QuantumMLConfig,
    /// Error correction configuration
    pub error_correction_config: ErrorCorrectionConfig,
    /// Enable quantum speedup simulation
    pub enable_quantum_speedup: bool,
    /// Use hybrid classical-quantum processing
    pub hybrid_processing: bool,
}

/// Variational Quantum Eigensolver configuration
#[derive(Debug, Clone)]
pub struct VQEConfig {
    /// Number of ansatz layers
    pub ansatz_layers: usize,
    /// Optimization method
    pub optimizer: VQEOptimizer,
    /// Convergence threshold
    pub convergence_threshold: f32,
    /// Maximum iterations
    pub max_iterations: usize,
    /// Parameter bounds
    pub parameter_bounds: (f32, f32),
}

/// VQE optimizer types
#[derive(Debug, Clone)]
pub enum VQEOptimizer {
    COBYLA,
    BFGS,
    GradientDescent {
        learning_rate: f32,
    },
    Adam {
        learning_rate: f32,
        beta1: f32,
        beta2: f32,
    },
}

/// QAOA configuration
#[derive(Debug, Clone)]
pub struct QAOAConfig {
    /// Number of QAOA layers (p parameter)
    pub layers: usize,
    /// Cost Hamiltonian definition
    pub cost_hamiltonian: CostHamiltonian,
    /// Mixer Hamiltonian definition
    pub mixer_hamiltonian: MixerHamiltonian,
    /// Parameter optimization method
    pub parameter_optimization: ParameterOptimization,
    /// Maximum optimization iterations
    pub max_opt_iterations: usize,
}

/// Cost Hamiltonian for QAOA
#[derive(Debug, Clone)]
pub enum CostHamiltonian {
    MaxCut,
    CompressionObjective,
    TensorCorrelation,
    CustomObjective(String),
}

/// Mixer Hamiltonian for QAOA
#[derive(Debug, Clone)]
pub enum MixerHamiltonian {
    StandardX,
    ConstrainedMixer,
    AdaptiveMixer,
    CustomMixer(String),
}

/// Parameter optimization for QAOA
#[derive(Debug, Clone)]
pub enum ParameterOptimization {
    NelderMead,
    DifferentialEvolution,
    BayesianOptimization,
    QuantumNaturalGradient,
}

/// Adiabatic quantum computing configuration
#[derive(Debug, Clone)]
pub struct AdiabaticConfig {
    /// Total evolution time
    pub total_time: f32,
    /// Number of time steps
    pub time_steps: usize,
    /// Initial Hamiltonian
    pub initial_hamiltonian: InitialHamiltonian,
    /// Final Hamiltonian
    pub final_hamiltonian: FinalHamiltonian,
    /// Annealing schedule
    pub annealing_schedule: AnnealingSchedule,
    /// Adiabatic condition monitoring
    pub monitor_adiabatic_condition: bool,
}

/// Initial Hamiltonian for adiabatic evolution
#[derive(Debug, Clone)]
pub enum InitialHamiltonian {
    TransverseField { strength: f32 },
    RandomField,
    GroundState,
}

/// Final Hamiltonian for adiabatic evolution
#[derive(Debug, Clone)]
pub enum FinalHamiltonian {
    ProblemHamiltonian,
    QuantizationObjective,
    CustomHamiltonian(String),
}

/// Annealing schedule types
#[derive(Debug, Clone)]
pub enum AnnealingSchedule {
    Linear,
    Exponential { decay_rate: f32 },
    Polynomial { power: f32 },
    Adaptive { sensitivity: f32 },
}

/// Quantum Machine Learning configuration
#[derive(Debug, Clone)]
pub struct QuantumMLConfig {
    /// Quantum feature map
    pub feature_map: QuantumFeatureMap,
    /// Quantum kernel method
    pub kernel_method: QuantumKernel,
    /// Variational classifier
    pub classifier_config: VariationalClassifierConfig,
    /// Training configuration
    pub training_config: QuantumTrainingConfig,
}

/// Quantum feature maps
#[derive(Debug, Clone)]
pub enum QuantumFeatureMap {
    ZZFeatureMap { repetitions: usize },
    PauliFeatureMap,
    DataReUploadingMap,
    AmplitudeEmbedding,
}

/// Quantum kernel methods
#[derive(Debug, Clone)]
pub enum QuantumKernel {
    QuantumKernelEstimation,
    FidelityQuantumKernel,
    QuantumKernelSVM,
}

/// Variational quantum classifier configuration
#[derive(Debug, Clone)]
pub struct VariationalClassifierConfig {
    /// Number of qubits
    pub num_qubits: usize,
    /// Ansatz type
    pub ansatz: QuantumAnsatz,
    /// Loss function
    pub loss_function: QuantumLossFunction,
    /// Regularization
    pub regularization: f32,
}

/// Quantum ansatz types
#[derive(Debug, Clone)]
pub enum QuantumAnsatz {
    RealAmplitudes,
    EfficientSU2,
    HardwareEfficient,
    ProblemInspired,
}

/// Quantum loss functions
#[derive(Debug, Clone)]
pub enum QuantumLossFunction {
    CrossEntropy,
    QuantumFidelity,
    QuantumRelativeEntropy,
    CustomQuantumLoss(String),
}

/// Quantum training configuration
#[derive(Debug, Clone)]
pub struct QuantumTrainingConfig {
    /// Learning rate
    pub learning_rate: f32,
    /// Batch size
    pub batch_size: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Gradient estimation method
    pub gradient_method: QuantumGradientMethod,
}

/// Quantum gradient estimation methods
#[derive(Debug, Clone)]
pub enum QuantumGradientMethod {
    ParameterShift,
    FiniteDifference,
    NaturalGradient,
    QuantumNaturalGradient,
}

/// Quantum error correction configuration
#[derive(Debug, Clone)]
pub struct ErrorCorrectionConfig {
    /// Error correction code
    pub code_type: QuantumErrorCode,
    /// Syndrome detection frequency
    pub syndrome_detection_freq: usize,
    /// Error threshold
    pub error_threshold: f32,
    /// Logical qubit encoding
    pub logical_encoding: LogicalQubitEncoding,
}

/// Quantum error correction codes
#[derive(Debug, Clone)]
pub enum QuantumErrorCode {
    SurfaceCode { distance: usize },
    SteaneCode,
    ShorCode,
    ToricCode,
    ColorCode,
}

/// Logical qubit encoding schemes
#[derive(Debug, Clone)]
pub enum LogicalQubitEncoding {
    Standard,
    BiasedNoise,
    AsymmetricNoise,
    CustomEncoding(String),
}

/// Variational quantum circuit implementation
#[derive(Debug, Clone)]
pub struct VariationalQuantumCircuit {
    /// Number of qubits
    pub num_qubits: usize,
    /// Circuit parameters
    pub parameters: Vec<f32>,
    /// Ansatz gates
    pub ansatz_gates: Vec<QuantumGate>,
    /// Measurement operators
    pub measurement_operators: Vec<PauliOperator>,
}

/// Quantum gate definitions
#[derive(Debug, Clone)]
pub enum QuantumGate {
    RY {
        qubit: usize,
        angle: f32,
    },
    RZ {
        qubit: usize,
        angle: f32,
    },
    CNOT {
        control: usize,
        target: usize,
    },
    H {
        qubit: usize,
    },
    CRX {
        control: usize,
        target: usize,
        angle: f32,
    },
    CRY {
        control: usize,
        target: usize,
        angle: f32,
    },
    CRZ {
        control: usize,
        target: usize,
        angle: f32,
    },
}

/// Pauli operators for measurements
#[derive(Debug, Clone)]
pub enum PauliOperator {
    X(usize),
    Y(usize),
    Z(usize),
    I(usize),
    PauliString(Vec<PauliOperator>),
}

/// QAOA optimizer implementation
#[derive(Debug, Clone)]
pub struct QAOAOptimizer {
    /// QAOA parameters (gamma and beta)
    pub parameters: Vec<f32>,
    /// Cost function evaluations
    pub cost_evaluations: Vec<f32>,
    /// Current layer count
    pub current_layers: usize,
    /// Optimization history
    pub optimization_history: Vec<OptimizationStep>,
}

/// Optimization step tracking
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    /// Step number
    pub step: usize,
    /// Parameters at this step
    pub parameters: Vec<f32>,
    /// Cost function value
    pub cost: f32,
    /// Gradient information
    pub gradient: Vec<f32>,
}

/// Adiabatic evolution controller
#[derive(Debug, Clone)]
pub struct AdiabaticController {
    /// Current time parameter
    pub current_time: f32,
    /// Hamiltonian interpolation parameter
    pub lambda: f32,
    /// Energy gap tracking
    pub energy_gaps: Vec<f32>,
    /// Adiabatic condition violations
    pub adiabatic_violations: Vec<f32>,
}

/// Quantum ML model for adaptive learning
#[derive(Debug, Clone)]
pub struct QuantumMLModel {
    /// Quantum feature extractor
    pub feature_extractor: QuantumFeatureExtractor,
    /// Variational quantum classifier
    pub classifier: VariationalQuantumClassifier,
    /// Training data memory
    pub training_memory: Vec<QuantumTrainingExample>,
    /// Model performance metrics
    pub performance_metrics: QuantumMLMetrics,
}

/// Quantum feature extraction
#[derive(Debug, Clone)]
pub struct QuantumFeatureExtractor {
    /// Feature map circuit
    pub feature_map_circuit: VariationalQuantumCircuit,
    /// Extracted features dimension
    pub feature_dimension: usize,
    /// Feature scaling parameters
    pub scaling_parameters: Vec<f32>,
}

/// Variational quantum classifier
#[derive(Debug, Clone)]
pub struct VariationalQuantumClassifier {
    /// Classifier circuit
    pub classifier_circuit: VariationalQuantumCircuit,
    /// Output weights
    pub output_weights: Vec<f32>,
    /// Classification threshold
    pub threshold: f32,
}

/// Quantum training example
#[derive(Debug, Clone)]
pub struct QuantumTrainingExample {
    /// Input quantum state
    pub input_state: Vec<f32>,
    /// Target quantization parameters
    pub target_parameters: QuantumQuantizationParameters,
    /// Quality score achieved
    pub quality_score: f32,
}

/// Quantum quantization parameters
#[derive(Debug, Clone)]
pub struct QuantumQuantizationParameters {
    /// Quantum scale factor
    pub quantum_scale: f32,
    /// Quantum zero point
    pub quantum_zero_point: i32,
    /// Entanglement strength
    pub entanglement_strength: f32,
    /// Error correction level
    pub error_correction_level: u8,
}

/// Multi-qubit entanglement engine
#[derive(Debug, Clone)]
pub struct MultiQubitEntanglementEngine {
    /// Entanglement graph structure
    pub entanglement_graph: EntanglementGraph,
    /// Entanglement measures
    pub entanglement_measures: EntanglementMeasures,
    /// Quantum correlation detector
    pub correlation_detector: QuantumCorrelationDetector,
}

/// Graph representation of qubit entanglement
#[derive(Debug, Clone)]
pub struct EntanglementGraph {
    /// Vertices (qubits)
    pub vertices: Vec<QubitVertex>,
    /// Edges (entanglement connections)
    pub edges: Vec<EntanglementEdge>,
    /// Graph properties
    pub properties: GraphProperties,
}

/// Qubit vertex in entanglement graph
#[derive(Debug, Clone)]
pub struct QubitVertex {
    /// Qubit index
    pub id: usize,
    /// Local quantum state
    pub local_state: [f32; 2],
    /// Degree of entanglement
    pub entanglement_degree: usize,
}

/// Entanglement edge between qubits
#[derive(Debug, Clone)]
pub struct EntanglementEdge {
    /// Source qubit
    pub source: usize,
    /// Target qubit
    pub target: usize,
    /// Entanglement strength
    pub strength: f32,
    /// Entanglement type
    pub entanglement_type: EntanglementType,
}

/// Types of quantum entanglement
#[derive(Debug, Clone)]
pub enum EntanglementType {
    Bell,
    GHZ,
    W,
    Cluster,
    Spin,
}

/// Graph properties for entanglement analysis
#[derive(Debug, Clone)]
pub struct GraphProperties {
    /// Connectivity measure
    pub connectivity: f32,
    /// Clustering coefficient
    pub clustering: f32,
    /// Path length distribution
    pub path_lengths: Vec<usize>,
}

/// Entanglement measures and metrics
#[derive(Debug, Clone)]
pub struct EntanglementMeasures {
    /// Von Neumann entropy
    pub von_neumann_entropy: f32,
    /// Concurrence
    pub concurrence: f32,
    /// Negativity
    pub negativity: f32,
    /// Entanglement of formation
    pub entanglement_of_formation: f32,
    /// Quantum mutual information
    pub quantum_mutual_information: f32,
}

/// Quantum correlation detection
#[derive(Debug, Clone)]
pub struct QuantumCorrelationDetector {
    /// Correlation matrix
    pub correlation_matrix: Vec<Vec<f32>>,
    /// Detection threshold
    pub detection_threshold: f32,
    /// Correlation patterns
    pub patterns: Vec<CorrelationPattern>,
}

/// Detected correlation patterns
#[derive(Debug, Clone)]
pub struct CorrelationPattern {
    /// Pattern identifier
    pub id: String,
    /// Involved qubits
    pub qubits: Vec<usize>,
    /// Correlation strength
    pub strength: f32,
    /// Pattern type
    pub pattern_type: PatternType,
}

/// Types of correlation patterns
#[derive(Debug, Clone)]
pub enum PatternType {
    Linear,
    Nonlinear,
    Quantum,
    Classical,
    Mixed,
}

/// Quantum error correction system
#[derive(Debug, Clone)]
pub struct QuantumErrorCorrection {
    /// Error syndrome history
    pub syndrome_history: Vec<ErrorSyndrome>,
    /// Correction operations applied
    pub corrections_applied: Vec<CorrectionOperation>,
    /// Error rates
    pub error_rates: ErrorRates,
    /// Logical error detection
    pub logical_errors: Vec<LogicalError>,
}

/// Error syndrome information
#[derive(Debug, Clone)]
pub struct ErrorSyndrome {
    /// Syndrome pattern
    pub pattern: Vec<bool>,
    /// Detection time
    pub detection_time: f32,
    /// Confidence level
    pub confidence: f32,
}

/// Correction operation applied
#[derive(Debug, Clone)]
pub struct CorrectionOperation {
    /// Operation type
    pub operation: QuantumGate,
    /// Target qubits
    pub targets: Vec<usize>,
    /// Success probability
    pub success_probability: f32,
}

/// Error rate tracking
#[derive(Debug, Clone)]
pub struct ErrorRates {
    /// Single qubit error rate
    pub single_qubit_error_rate: f32,
    /// Two qubit error rate
    pub two_qubit_error_rate: f32,
    /// Measurement error rate
    pub measurement_error_rate: f32,
    /// Logical error rate
    pub logical_error_rate: f32,
}

/// Logical error information
#[derive(Debug, Clone)]
pub struct LogicalError {
    /// Error type
    pub error_type: LogicalErrorType,
    /// Affected logical qubits
    pub affected_qubits: Vec<usize>,
    /// Error weight
    pub weight: usize,
}

/// Types of logical errors
#[derive(Debug, Clone)]
pub enum LogicalErrorType {
    X,
    Z,
    Y,
    Unknown,
}

/// Enhanced quantum metrics
#[derive(Debug, Clone)]
pub struct QuantumEnhancedMetrics {
    /// Base quantum metrics
    pub base_metrics: crate::quantum::QuantumMetrics,
    /// VQE convergence metrics
    pub vqe_metrics: VQEMetrics,
    /// QAOA performance metrics
    pub qaoa_metrics: QAOAMetrics,
    /// Adiabatic evolution metrics
    pub adiabatic_metrics: AdiabaticMetrics,
    /// Quantum ML metrics
    pub qml_metrics: QuantumMLMetrics,
    /// Error correction metrics
    pub error_correction_metrics: ErrorCorrectionMetrics,
}

/// VQE performance metrics
#[derive(Debug, Clone)]
pub struct VQEMetrics {
    /// Ground state energy estimate
    pub ground_state_energy: f32,
    /// Number of iterations to convergence
    pub iterations_to_convergence: usize,
    /// Parameter optimization trajectory
    pub parameter_trajectory: Vec<Vec<f32>>,
    /// Energy variance
    pub energy_variance: f32,
}

/// QAOA performance metrics
#[derive(Debug, Clone)]
pub struct QAOAMetrics {
    /// Approximation ratio achieved
    pub approximation_ratio: f32,
    /// Optimal parameters found
    pub optimal_parameters: Vec<f32>,
    /// Cost function evaluations
    pub function_evaluations: usize,
    /// Success probability
    pub success_probability: f32,
}

/// Adiabatic evolution metrics
#[derive(Debug, Clone)]
pub struct AdiabaticMetrics {
    /// Final ground state probability
    pub ground_state_probability: f32,
    /// Maximum energy gap encountered
    pub max_energy_gap: f32,
    /// Minimum energy gap
    pub min_energy_gap: f32,
    /// Adiabatic condition violations
    pub adiabatic_violations: usize,
}

/// Quantum ML performance metrics
#[derive(Debug, Clone)]
pub struct QuantumMLMetrics {
    /// Classification accuracy
    pub classification_accuracy: f32,
    /// Quantum feature map fidelity
    pub feature_map_fidelity: f32,
    /// Training convergence rate
    pub convergence_rate: f32,
    /// Quantum advantage factor
    pub quantum_advantage: f32,
}

/// Error correction performance metrics
#[derive(Debug, Clone)]
pub struct ErrorCorrectionMetrics {
    /// Syndrome detection accuracy
    pub syndrome_accuracy: f32,
    /// Correction success rate
    pub correction_success_rate: f32,
    /// Logical error suppression factor
    pub error_suppression_factor: f32,
    /// Overhead factor
    pub overhead_factor: f32,
}

/// Enhanced quantization result with quantum information
#[derive(Debug, Clone)]
pub struct QuantumEnhancedResult {
    /// Base quantum result
    pub base_result: crate::quantum::QuantumQuantizationResult,
    /// VQE optimal parameters
    pub vqe_parameters: Vec<f32>,
    /// QAOA solution
    pub qaoa_solution: Vec<bool>,
    /// Adiabatic final state
    pub adiabatic_state: Vec<f32>,
    /// Quantum ML predictions
    pub qml_predictions: Vec<f32>,
    /// Entanglement structure
    pub entanglement_structure: EntanglementGraph,
    /// Error correction status
    pub error_correction_status: ErrorCorrectionStatus,
    /// Enhanced metrics
    pub enhanced_metrics: QuantumEnhancedMetrics,
}

/// Error correction status
#[derive(Debug, Clone)]
pub struct ErrorCorrectionStatus {
    /// Number of errors detected
    pub errors_detected: usize,
    /// Number of errors corrected
    pub errors_corrected: usize,
    /// Remaining error estimate
    pub remaining_errors: f32,
    /// Correction confidence
    pub correction_confidence: f32,
}

impl QuantumEnhancedQuantizer {
    /// Create new enhanced quantum quantizer
    pub fn new(config: QuantumEnhancedConfig) -> Self {
        Self {
            vqe_circuit: VariationalQuantumCircuit::new(config.base_config.num_qubits),
            qaoa_optimizer: QAOAOptimizer::new(&config.qaoa_config),
            adiabatic_controller: AdiabaticController::new(&config.adiabatic_config),
            quantum_ml_model: QuantumMLModel::new(&config.qml_config),
            entanglement_engine: MultiQubitEntanglementEngine::new(
                config.base_config.max_entanglement_distance,
            ),
            error_correction: QuantumErrorCorrection::new(&config.error_correction_config),
            enhanced_metrics: QuantumEnhancedMetrics::new(),
            config,
        }
    }

    /// Perform enhanced quantum-inspired quantization
    pub fn quantize_enhanced(&mut self, tensor: &Tensor) -> TorshResult<QuantumEnhancedResult> {
        let data = tensor.data()?;

        // Phase 1: VQE optimization for optimal parameters
        let vqe_parameters = self.run_vqe_optimization(&data)?;

        // Phase 2: QAOA for compression optimization
        let qaoa_solution = self.run_qaoa_optimization(&data)?;

        // Phase 3: Adiabatic evolution for gradual quantization
        let adiabatic_state = self.run_adiabatic_evolution(&data)?;

        // Phase 4: Quantum ML for adaptive prediction
        let qml_predictions = self.quantum_ml_predict(&data)?;

        // Phase 5: Multi-qubit entanglement analysis
        let entanglement_structure = self.analyze_entanglement(&data)?;

        // Phase 6: Quantum error correction
        let error_correction_status = self.apply_error_correction(&data)?;

        // Combine results using quantum interference principles
        let combined_result = self.combine_quantum_results(
            &vqe_parameters,
            &qaoa_solution,
            &adiabatic_state,
            &qml_predictions,
        )?;

        Ok(QuantumEnhancedResult {
            base_result: combined_result,
            vqe_parameters,
            qaoa_solution,
            adiabatic_state,
            qml_predictions,
            entanglement_structure,
            error_correction_status,
            enhanced_metrics: self.enhanced_metrics.clone(),
        })
    }

    /// Run VQE optimization for parameter finding
    fn run_vqe_optimization(&mut self, data: &[f32]) -> TorshResult<Vec<f32>> {
        let mut parameters = vec![0.0; self.config.vqe_config.ansatz_layers * 2];
        let _rng = scirs2_core::random::thread_rng();

        // Initialize parameters randomly
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};
        for (i, param) in parameters.iter_mut().enumerate() {
            let mut hasher = DefaultHasher::new();
            i.hash(&mut hasher);
            let rand_val = (hasher.finish() as f32) / (u64::MAX as f32);
            *param = rand_val * 2.0 * PI;
        }

        // VQE optimization loop
        for iteration in 0..self.config.vqe_config.max_iterations {
            // Calculate expectation value
            let expectation = self.calculate_expectation_value(&parameters, data)?;

            // Update parameters using chosen optimizer
            match &self.config.vqe_config.optimizer {
                VQEOptimizer::GradientDescent { learning_rate } => {
                    let gradients = self.calculate_parameter_gradients(&parameters, data)?;
                    for (param, grad) in parameters.iter_mut().zip(gradients.iter()) {
                        *param -= learning_rate * grad;
                    }
                }
                VQEOptimizer::Adam {
                    learning_rate,
                    beta1: _,
                    beta2: _,
                } => {
                    // Simplified Adam optimizer implementation
                    let gradients = self.calculate_parameter_gradients(&parameters, data)?;
                    for (param, grad) in parameters.iter_mut().zip(gradients.iter()) {
                        // Simplified Adam update (would need momentum terms in real implementation)
                        *param -= learning_rate * grad;
                    }
                }
                _ => {
                    // Other optimizers (COBYLA, BFGS) would require more complex implementation
                    return Err(TorshError::InvalidArgument(
                        "Optimizer not implemented".to_string(),
                    ));
                }
            }

            // Check convergence
            if expectation.abs() < self.config.vqe_config.convergence_threshold {
                self.enhanced_metrics.vqe_metrics.iterations_to_convergence = iteration;
                break;
            }
        }

        self.enhanced_metrics.vqe_metrics.ground_state_energy =
            self.calculate_expectation_value(&parameters, data)?;

        Ok(parameters)
    }

    /// Run QAOA for compression optimization
    fn run_qaoa_optimization(&mut self, data: &[f32]) -> TorshResult<Vec<bool>> {
        let num_variables = data.len().min(64); // Limit for computational feasibility
        let mut solution = vec![false; num_variables];

        // QAOA parameter optimization
        let mut gamma_beta_params = vec![0.0; 2 * self.config.qaoa_config.layers];
        let _rng = scirs2_core::random::thread_rng();

        // Initialize QAOA parameters
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};
        for (i, param) in gamma_beta_params.iter_mut().enumerate() {
            let mut hasher = DefaultHasher::new();
            (i + 100).hash(&mut hasher);
            let rand_val = (hasher.finish() as f32) / (u64::MAX as f32);
            *param = rand_val * PI;
        }

        // Optimize QAOA parameters
        let mut best_cost = f32::INFINITY;
        for _iteration in 0..self.config.qaoa_config.max_opt_iterations {
            let cost = self.evaluate_qaoa_cost(&gamma_beta_params, data)?;
            if cost < best_cost {
                best_cost = cost;
                solution = self.sample_qaoa_solution(&gamma_beta_params, data)?;
            }

            // Simple parameter update (would use sophisticated optimizer in practice)
            for (i, param) in gamma_beta_params.iter_mut().enumerate() {
                let mut hasher = DefaultHasher::new();
                (i + _iteration * 1000).hash(&mut hasher);
                let rand_val = (hasher.finish() as f32) / (u64::MAX as f32);
                *param += (rand_val - 0.5) * 0.1;
            }
        }

        self.enhanced_metrics.qaoa_metrics.optimal_parameters = gamma_beta_params;
        self.enhanced_metrics.qaoa_metrics.approximation_ratio = 1.0 / (1.0 + best_cost);

        Ok(solution)
    }

    /// Run adiabatic evolution for gradual quantization
    fn run_adiabatic_evolution(&mut self, data: &[f32]) -> TorshResult<Vec<f32>> {
        let mut state = data.to_vec();
        let dt = self.config.adiabatic_config.total_time
            / self.config.adiabatic_config.time_steps as f32;

        for step in 0..self.config.adiabatic_config.time_steps {
            let t = step as f32 * dt;
            let lambda = t / self.config.adiabatic_config.total_time;

            // Update lambda for Hamiltonian interpolation
            self.adiabatic_controller.lambda = lambda;
            self.adiabatic_controller.current_time = t;

            // Apply time evolution operator (simplified)
            state = self.apply_time_evolution(&state, dt, lambda)?;

            // Monitor adiabatic condition
            if self.config.adiabatic_config.monitor_adiabatic_condition {
                let energy_gap = self.calculate_energy_gap(&state)?;
                self.adiabatic_controller.energy_gaps.push(energy_gap);

                if energy_gap < 0.01 {
                    // Potential adiabatic condition violation
                    self.adiabatic_controller.adiabatic_violations.push(lambda);
                }
            }
        }

        // Calculate final metrics
        if let Some(&min_gap) = self
            .adiabatic_controller
            .energy_gaps
            .iter()
            .min_by(|a, b| a.partial_cmp(b).unwrap())
        {
            self.enhanced_metrics.adiabatic_metrics.min_energy_gap = min_gap;
        }
        if let Some(&max_gap) = self
            .adiabatic_controller
            .energy_gaps
            .iter()
            .max_by(|a, b| a.partial_cmp(b).unwrap())
        {
            self.enhanced_metrics.adiabatic_metrics.max_energy_gap = max_gap;
        }

        Ok(state)
    }

    /// Quantum ML prediction for adaptive parameters
    fn quantum_ml_predict(&mut self, data: &[f32]) -> TorshResult<Vec<f32>> {
        // Extract quantum features
        let quantum_features = self
            .quantum_ml_model
            .feature_extractor
            .extract_features(data)?;

        // Apply variational quantum classifier
        let predictions = self
            .quantum_ml_model
            .classifier
            .predict(&quantum_features)?;

        // Update ML metrics
        self.enhanced_metrics.qml_metrics.feature_map_fidelity =
            self.calculate_feature_map_fidelity(&quantum_features)?;

        Ok(predictions)
    }

    /// Analyze multi-qubit entanglement structure
    fn analyze_entanglement(&mut self, data: &[f32]) -> TorshResult<EntanglementGraph> {
        let mut graph = EntanglementGraph {
            vertices: Vec::new(),
            edges: Vec::new(),
            properties: GraphProperties {
                connectivity: 0.0,
                clustering: 0.0,
                path_lengths: Vec::new(),
            },
        };

        // Create vertices for each data point (limited for computational feasibility)
        let num_qubits = data.len().min(16);
        for (i, &data_point) in data.iter().enumerate().take(num_qubits) {
            graph.vertices.push(QubitVertex {
                id: i,
                local_state: [data_point.cos(), data_point.sin()],
                entanglement_degree: 0,
            });
        }

        // Detect entanglement edges
        for i in 0..num_qubits {
            for j in (i + 1)..num_qubits {
                let correlation = self.calculate_quantum_correlation(data[i], data[j])?;
                if correlation > 0.5 {
                    graph.edges.push(EntanglementEdge {
                        source: i,
                        target: j,
                        strength: correlation,
                        entanglement_type: EntanglementType::Bell, // Simplified
                    });

                    graph.vertices[i].entanglement_degree += 1;
                    graph.vertices[j].entanglement_degree += 1;
                }
            }
        }

        // Calculate graph properties
        graph.properties.connectivity =
            graph.edges.len() as f32 / (num_qubits * (num_qubits - 1) / 2) as f32;

        self.entanglement_engine.entanglement_graph = graph.clone();

        Ok(graph)
    }

    /// Apply quantum error correction
    fn apply_error_correction(&mut self, data: &[f32]) -> TorshResult<ErrorCorrectionStatus> {
        let mut status = ErrorCorrectionStatus {
            errors_detected: 0,
            errors_corrected: 0,
            remaining_errors: 0.0,
            correction_confidence: 1.0,
        };

        // Simplified error detection and correction
        for &value in data.iter() {
            // Detect potential errors (simplified threshold-based detection)
            if value.abs() > 10.0 || value.is_nan() || value.is_infinite() {
                status.errors_detected += 1;

                // Apply correction (simplified)
                let corrected = value.clamp(-1.0, 1.0);
                if (corrected - value).abs() < 0.1 {
                    status.errors_corrected += 1;
                } else {
                    status.remaining_errors += 1.0;
                }
            }
        }

        status.correction_confidence = if status.errors_detected > 0 {
            status.errors_corrected as f32 / status.errors_detected as f32
        } else {
            1.0
        };

        // Update error correction metrics
        self.enhanced_metrics
            .error_correction_metrics
            .syndrome_accuracy = status.correction_confidence;
        self.enhanced_metrics
            .error_correction_metrics
            .correction_success_rate = status.correction_confidence;

        Ok(status)
    }

    // Helper methods with simplified implementations

    fn calculate_expectation_value(&self, _parameters: &[f32], _data: &[f32]) -> TorshResult<f32> {
        // Simplified expectation value calculation
        Ok(0.5) // Placeholder
    }

    fn calculate_parameter_gradients(
        &self,
        _parameters: &[f32],
        _data: &[f32],
    ) -> TorshResult<Vec<f32>> {
        // Simplified gradient calculation
        Ok(vec![0.01; _parameters.len()]) // Placeholder
    }

    fn evaluate_qaoa_cost(&self, _parameters: &[f32], _data: &[f32]) -> TorshResult<f32> {
        // Simplified QAOA cost evaluation
        Ok(1.0) // Placeholder
    }

    fn sample_qaoa_solution(&self, _parameters: &[f32], data: &[f32]) -> TorshResult<Vec<bool>> {
        // Simplified solution sampling
        Ok(vec![true; data.len().min(64)]) // Placeholder
    }

    fn apply_time_evolution(&self, state: &[f32], _dt: f32, _lambda: f32) -> TorshResult<Vec<f32>> {
        // Simplified time evolution
        Ok(state.iter().map(|&x| x * 0.99).collect()) // Placeholder
    }

    fn calculate_energy_gap(&self, _state: &[f32]) -> TorshResult<f32> {
        // Simplified energy gap calculation
        Ok(0.1) // Placeholder
    }

    fn calculate_quantum_correlation(&self, a: f32, b: f32) -> TorshResult<f32> {
        // Simplified quantum correlation
        Ok((a * b).abs()) // Placeholder
    }

    fn calculate_feature_map_fidelity(&self, _features: &[f32]) -> TorshResult<f32> {
        // Simplified fidelity calculation
        Ok(0.95) // Placeholder
    }

    fn combine_quantum_results(
        &self,
        _vqe_params: &[f32],
        _qaoa_solution: &[bool],
        _adiabatic_state: &[f32],
        _qml_predictions: &[f32],
    ) -> TorshResult<crate::quantum::QuantumQuantizationResult> {
        // Simplified result combination - would implement quantum interference

        Ok(crate::quantum::QuantumQuantizationResult {
            quantum_data: vec![0u8; 64], // Placeholder
            classical_backup: vec![],
            quantum_states: vec![],
            entanglement_info: crate::quantum::EntanglementInfo {
                max_correlation: 0.5,
                num_entangled_pairs: 10,
                entanglement_entropy: 1.2,
            },
            metrics: crate::quantum::QuantumMetrics {
                fidelity: 0.95,
                entanglement_entropy: 1.2,
                compression_ratio: 2.0,
                quantum_ops_count: 1000,
                error_correction_overhead: 0.1,
            },
        })
    }
}

// Implementation stubs for complex components

impl VariationalQuantumCircuit {
    fn new(num_qubits: usize) -> Self {
        Self {
            num_qubits,
            parameters: vec![0.0; num_qubits * 4], // Simplified parameter count
            ansatz_gates: vec![],
            measurement_operators: vec![],
        }
    }
}

impl QAOAOptimizer {
    fn new(_config: &QAOAConfig) -> Self {
        Self {
            parameters: vec![],
            cost_evaluations: vec![],
            current_layers: 0,
            optimization_history: vec![],
        }
    }
}

impl AdiabaticController {
    fn new(_config: &AdiabaticConfig) -> Self {
        Self {
            current_time: 0.0,
            lambda: 0.0,
            energy_gaps: vec![],
            adiabatic_violations: vec![],
        }
    }
}

impl QuantumMLModel {
    fn new(_config: &QuantumMLConfig) -> Self {
        Self {
            feature_extractor: QuantumFeatureExtractor::new(),
            classifier: VariationalQuantumClassifier::new(),
            training_memory: vec![],
            performance_metrics: QuantumMLMetrics {
                classification_accuracy: 0.0,
                feature_map_fidelity: 0.0,
                convergence_rate: 0.0,
                quantum_advantage: 0.0,
            },
        }
    }
}

impl QuantumFeatureExtractor {
    fn new() -> Self {
        Self {
            feature_map_circuit: VariationalQuantumCircuit::new(8),
            feature_dimension: 16,
            scaling_parameters: vec![1.0; 16],
        }
    }

    fn extract_features(&self, data: &[f32]) -> TorshResult<Vec<f32>> {
        // Simplified feature extraction
        let mut features = vec![0.0; self.feature_dimension];
        for (i, &value) in data.iter().take(self.feature_dimension).enumerate() {
            features[i] = value * self.scaling_parameters[i];
        }
        Ok(features)
    }
}

impl VariationalQuantumClassifier {
    fn new() -> Self {
        Self {
            classifier_circuit: VariationalQuantumCircuit::new(4),
            output_weights: vec![1.0; 4],
            threshold: 0.5,
        }
    }

    fn predict(&self, features: &[f32]) -> TorshResult<Vec<f32>> {
        // Simplified prediction
        let prediction = features
            .iter()
            .zip(self.output_weights.iter())
            .map(|(f, w)| f * w)
            .sum::<f32>()
            .tanh();
        Ok(vec![prediction])
    }
}

impl MultiQubitEntanglementEngine {
    fn new(_max_distance: usize) -> Self {
        Self {
            entanglement_graph: EntanglementGraph {
                vertices: vec![],
                edges: vec![],
                properties: GraphProperties {
                    connectivity: 0.0,
                    clustering: 0.0,
                    path_lengths: vec![],
                },
            },
            entanglement_measures: EntanglementMeasures {
                von_neumann_entropy: 0.0,
                concurrence: 0.0,
                negativity: 0.0,
                entanglement_of_formation: 0.0,
                quantum_mutual_information: 0.0,
            },
            correlation_detector: QuantumCorrelationDetector {
                correlation_matrix: vec![],
                detection_threshold: 0.5,
                patterns: vec![],
            },
        }
    }
}

impl QuantumErrorCorrection {
    fn new(_config: &ErrorCorrectionConfig) -> Self {
        Self {
            syndrome_history: vec![],
            corrections_applied: vec![],
            error_rates: ErrorRates {
                single_qubit_error_rate: 0.001,
                two_qubit_error_rate: 0.01,
                measurement_error_rate: 0.001,
                logical_error_rate: 0.0001,
            },
            logical_errors: vec![],
        }
    }
}

impl QuantumEnhancedMetrics {
    fn new() -> Self {
        Self {
            base_metrics: crate::quantum::QuantumMetrics {
                fidelity: 1.0,
                entanglement_entropy: 0.0,
                compression_ratio: 1.0,
                quantum_ops_count: 0,
                error_correction_overhead: 0.0,
            },
            vqe_metrics: VQEMetrics {
                ground_state_energy: 0.0,
                iterations_to_convergence: 0,
                parameter_trajectory: vec![],
                energy_variance: 0.0,
            },
            qaoa_metrics: QAOAMetrics {
                approximation_ratio: 0.0,
                optimal_parameters: vec![],
                function_evaluations: 0,
                success_probability: 0.0,
            },
            adiabatic_metrics: AdiabaticMetrics {
                ground_state_probability: 0.0,
                max_energy_gap: 0.0,
                min_energy_gap: 0.0,
                adiabatic_violations: 0,
            },
            qml_metrics: QuantumMLMetrics {
                classification_accuracy: 0.0,
                feature_map_fidelity: 0.0,
                convergence_rate: 0.0,
                quantum_advantage: 0.0,
            },
            error_correction_metrics: ErrorCorrectionMetrics {
                syndrome_accuracy: 0.0,
                correction_success_rate: 0.0,
                error_suppression_factor: 1.0,
                overhead_factor: 1.0,
            },
        }
    }
}

impl Default for QuantumEnhancedConfig {
    fn default() -> Self {
        Self {
            base_config: crate::quantum::QuantumConfig::default(),
            vqe_config: VQEConfig {
                ansatz_layers: 4,
                optimizer: VQEOptimizer::GradientDescent {
                    learning_rate: 0.01,
                },
                convergence_threshold: 1e-6,
                max_iterations: 1000,
                parameter_bounds: (-PI, PI),
            },
            qaoa_config: QAOAConfig {
                layers: 3,
                cost_hamiltonian: CostHamiltonian::CompressionObjective,
                mixer_hamiltonian: MixerHamiltonian::StandardX,
                parameter_optimization: ParameterOptimization::NelderMead,
                max_opt_iterations: 100,
            },
            adiabatic_config: AdiabaticConfig {
                total_time: 10.0,
                time_steps: 100,
                initial_hamiltonian: InitialHamiltonian::TransverseField { strength: 1.0 },
                final_hamiltonian: FinalHamiltonian::QuantizationObjective,
                annealing_schedule: AnnealingSchedule::Linear,
                monitor_adiabatic_condition: true,
            },
            qml_config: QuantumMLConfig {
                feature_map: QuantumFeatureMap::ZZFeatureMap { repetitions: 2 },
                kernel_method: QuantumKernel::QuantumKernelEstimation,
                classifier_config: VariationalClassifierConfig {
                    num_qubits: 4,
                    ansatz: QuantumAnsatz::RealAmplitudes,
                    loss_function: QuantumLossFunction::CrossEntropy,
                    regularization: 0.01,
                },
                training_config: QuantumTrainingConfig {
                    learning_rate: 0.01,
                    batch_size: 32,
                    epochs: 100,
                    gradient_method: QuantumGradientMethod::ParameterShift,
                },
            },
            error_correction_config: ErrorCorrectionConfig {
                code_type: QuantumErrorCode::SurfaceCode { distance: 3 },
                syndrome_detection_freq: 10,
                error_threshold: 0.01,
                logical_encoding: LogicalQubitEncoding::Standard,
            },
            enable_quantum_speedup: true,
            hybrid_processing: true,
        }
    }
}