scirs2-core 0.4.2

//! AI-driven optimization and learning algorithms for tensor operations
//!
//! This module contains the AI optimization engine, neural network components,
//! learning algorithms, and quantum-inspired optimization for advanced tensor operations.

use super::*;
use crate::error::{CoreError, CoreResult};

#[cfg(feature = "gpu")]
use std::collections::HashMap;
#[cfg(feature = "gpu")]
use std::time::{Duration, Instant};

#[cfg(feature = "gpu")]
use crate::gpu::{
    auto_tuning::{KernelParameters, PerformanceMetrics, TuningResult},
    tensor_cores::{TensorCoreConfig, TensorCoreManager, TensorDataType, TensorOperation},
    GpuBackend,
};

#[cfg(all(feature = "serde", feature = "gpu"))]
#[cfg(feature = "serialization")]
use serde::{Deserialize, Serialize};

/// AI optimization engine for tensor operations
#[allow(dead_code)]
#[derive(Debug)]
pub struct AIOptimizationEngine {
    /// Neural network for performance modeling
    performance_model: PerformanceNeuralNetwork,
    /// Optimization strategies
    #[allow(dead_code)]
    optimization_strategies: HashMap<String, OptimizationStrategy>,
    /// Learning algorithm
    #[allow(dead_code)]
    learning_algorithm: LearningAlgorithm,
    /// Feature extraction
    feature_extractor: FeatureExtractor,
    /// Decision tree for strategy selection
    #[allow(dead_code)]
    strategy_selector: StrategySelector,
    /// Performance history
    performance_history: Vec<PerformanceDataPoint>,
    /// Model training state
    training_state: ModelTrainingState,
}

/// Performance neural network for prediction and optimization
#[allow(dead_code)]
#[derive(Debug)]
pub struct PerformanceNeuralNetwork {
    /// Network layers
    #[allow(dead_code)]
    layers: Vec<NetworkLayer>,
    /// Training parameters
    #[allow(dead_code)]
    training_params: TrainingParameters,
    /// Model accuracy metrics
    #[allow(dead_code)]
    accuracy_metrics: AccuracyMetrics,
    /// Last training timestamp
    #[allow(dead_code)]
    last_training: Instant,
}

/// Network layer representation
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct NetworkLayer {
    /// Layer weights (simplified representation)
    #[allow(dead_code)]
    weights: Vec<Vec<f64>>,
    /// Layer biases
    #[allow(dead_code)]
    biases: Vec<f64>,
    /// Activation function
    #[allow(dead_code)]
    activation: ActivationFunction,
    /// Layer type
    #[allow(dead_code)]
    layer_type: LayerType,
}

/// Activation functions for neural network layers
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub enum ActivationFunction {
    #[default]
    ReLU,
    Sigmoid,
    Tanh,
    Linear,
    ELU,
    GELU,
}

/// Neural network layer types
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub enum LayerType {
    #[default]
    Dense,
    Convolutional,
    LSTM,
    Attention,
    Normalization,
    Dropout,
}

/// Training parameters for the performance model
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct TrainingParameters {
    /// Learning rate
    pub learning_rate: f64,
    /// Batch size
    pub batch_size: usize,
    /// Number of epochs
    pub epochs: usize,
    /// Regularization strength
    pub regularization: f64,
    /// Optimizer type
    pub optimizer: OptimizerType,
}

/// Optimizer types for neural network training
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub enum OptimizerType {
    #[default]
    SGD,
    Adam,
    AdaGrad,
    RMSprop,
    LBFGS,
}

/// Model accuracy metrics
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct AccuracyMetrics {
    /// Mean squared error
    pub mse: f64,
    /// Mean absolute error
    pub mae: f64,
    /// R-squared coefficient
    pub r_squared: f64,
    /// Validation accuracy
    pub validation_accuracy: f64,
}

/// Optimization strategy
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizationStrategy {
    /// Strategy name
    pub name: String,
    /// Strategy parameters
    pub parameters: HashMap<String, f64>,
    /// Effectiveness score
    pub effectiveness: f64,
    /// Applicable conditions
    pub conditions: Vec<String>,
    /// Success rate
    pub success_rate: f64,
}

/// Learning algorithm for continuous improvement
#[allow(dead_code)]
#[derive(Debug)]
pub struct LearningAlgorithm {
    /// Algorithm type
    #[allow(dead_code)]
    algorithm_type: LearningAlgorithmType,
    /// Hyperparameters
    #[allow(dead_code)]
    hyperparameters: HashMap<String, f64>,
    /// Exploration rate
    #[allow(dead_code)]
    exploration_rate: f64,
    /// Exploitation rate
    #[allow(dead_code)]
    exploitation_rate: f64,
    /// Learning progress
    #[allow(dead_code)]
    learning_progress: LearningProgress,
}

/// Types of learning algorithms
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub enum LearningAlgorithmType {
    #[default]
    ReinforcementLearning,
    BayesianOptimization,
    EvolutionaryStrategy,
    GradientBoosting,
    RandomForest,
    DeepQLearning,
}

/// Learning progress tracking
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct LearningProgress {
    /// Total learning iterations
    pub total_iterations: usize,
    /// Successful optimizations
    pub successful_optimizations: usize,
    /// Failed optimizations
    pub failed_optimizations: usize,
    /// Average improvement
    pub average_improvement: f64,
    /// Best performance achieved
    pub best_performance: f64,
}

/// Feature extractor for performance characteristics
#[allow(dead_code)]
#[derive(Debug)]
pub struct FeatureExtractor {
    /// Feature types to extract
    #[allow(dead_code)]
    feature_types: Vec<FeatureType>,
    /// Feature normalization parameters
    normalization_params: HashMap<String, NormalizationParams>,
    /// Feature importance weights
    #[allow(dead_code)]
    feature_weights: HashMap<String, f64>,
    /// Dimensionality reduction
    #[allow(dead_code)]
    dimensionality_reduction: Option<DimensionalityReduction>,
}

/// Types of features to extract
#[allow(dead_code)]
#[derive(Debug, Clone, Default)]
pub enum FeatureType {
    #[default]
    WorkloadCharacteristics,
    HardwareProperties,
    MemoryAccessPatterns,
    ComputeUtilization,
    PowerConsumption,
    ThermalProfile,
    CacheHitRates,
    BandwidthUtilization,
}

/// Feature normalization parameters
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct NormalizationParams {
    /// Mean value
    pub mean: f64,
    /// Standard deviation
    pub std_dev: f64,
    /// Minimum value
    pub min_value: f64,
    /// Maximum value
    pub max_value: f64,
}

/// Dimensionality reduction techniques
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum DimensionalityReduction {
    PCA(usize),         // Principal Component Analysis with n components
    LDA(usize),         // Linear Discriminant Analysis
    TSNE(usize),        // t-SNE
    UMAP(usize),        // Uniform Manifold Approximation
    Autoencoder(usize), // Autoencoder with latent dimension
}

/// Strategy selector for optimization approaches
#[allow(dead_code)]
#[derive(Debug)]
pub struct StrategySelector {
    /// Decision tree for strategy selection
    #[allow(dead_code)]
    decision_tree: DecisionTree,
    /// Strategy effectiveness history
    #[allow(dead_code)]
    strategy_history: HashMap<String, StrategyPerformance>,
    /// Context analysis
    #[allow(dead_code)]
    context_analyzer: ContextAnalyzer,
}

/// Decision tree for intelligent strategy selection
#[allow(dead_code)]
#[derive(Debug)]
pub struct DecisionTree {
    /// Root node
    root: Option<DecisionNode>,
    /// Tree depth
    depth: usize,
    /// Number of leaves
    num_leaves: usize,
}

/// Decision tree node
#[allow(dead_code)]
#[derive(Debug)]
pub struct DecisionNode {
    /// Feature to split on
    #[allow(dead_code)]
    feature: String,
    /// Threshold value
    #[allow(dead_code)]
    threshold: f64,
    /// Left child (condition < threshold)
    #[allow(dead_code)]
    left: Option<Box<DecisionNode>>,
    /// Right child (condition >= threshold)
    #[allow(dead_code)]
    right: Option<Box<DecisionNode>>,
    /// Leaf value (if leaf node)
    #[allow(dead_code)]
    leaf_value: Option<String>,
}

/// Strategy performance tracking
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct StrategyPerformance {
    /// Total applications
    pub total_applications: usize,
    /// Successful applications
    pub successful_applications: usize,
    /// Average improvement
    pub average_improvement: f64,
    /// Variance in improvement
    pub improvement_variance: f64,
    /// Last used timestamp
    pub last_used: Instant,
}

/// Context analyzer for workload understanding
#[allow(dead_code)]
#[derive(Debug)]
pub struct ContextAnalyzer {
    /// Workload classifier
    #[allow(dead_code)]
    workload_classifier: WorkloadClassifier,
    /// Hardware profiler
    #[allow(dead_code)]
    hardware_profiler: super::HardwareProfiler,
    /// Environment detector
    #[allow(dead_code)]
    environment_detector: super::EnvironmentDetector,
}

/// Workload classifier for automatic workload type detection
#[allow(dead_code)]
#[derive(Debug)]
pub struct WorkloadClassifier {
    /// Classification models
    #[allow(dead_code)]
    models: HashMap<String, ClassificationModel>,
    /// Feature extractors
    #[allow(dead_code)]
    extractors: Vec<String>,
    /// Classification history
    #[allow(dead_code)]
    classification_history: Vec<WorkloadClassification>,
}

/// Classification model for workload types
#[allow(dead_code)]
#[derive(Debug)]
pub struct ClassificationModel {
    /// Model type
    model_type: ModelType,
    /// Model parameters
    parameters: Vec<f64>,
    /// Accuracy metrics
    accuracy: f64,
    /// Training data size
    training_size: usize,
}

/// Types of machine learning models
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum ModelType {
    SVM,
    RandomForest,
    NeuralNetwork,
    NaiveBayes,
    KMeans,
    DBSCAN,
}

/// Workload classification result
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct WorkloadClassification {
    /// Workload type
    pub workload_type: WorkloadType,
    /// Confidence score
    pub confidence: f64,
    /// Classification timestamp
    pub timestamp: Instant,
    /// Feature vector used
    pub features: Vec<f64>,
}

/// Types of computational workloads
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum WorkloadType {
    LinearAlgebra,
    ConvolutionalNeuralNetwork,
    Transformer,
    GraphProcessing,
    SimulationComputing,
    ImageProcessing,
    SignalProcessing,
    ScientificComputing,
    MachineLearningTraining,
    MachineLearningInference,
}

/// Performance data point for learning
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct PerformanceDataPoint {
    /// Workload feature vector
    pub workload_features: Vec<f64>,
    /// Hardware configuration
    pub hardware_config: String,
    /// Optimization parameters used
    pub optimization_params: HashMap<String, f64>,
    /// Achieved performance
    pub performance: PerformanceMetrics,
    /// Timestamp
    pub timestamp: Instant,
    /// Whether optimization was successful
    pub success: bool,
}

/// Model training state
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ModelTrainingState {
    /// Is model currently training
    pub is_training: bool,
    /// Training progress (0.0 to 1.0)
    pub training_progress: f64,
    /// Current epoch
    pub current_epoch: usize,
    /// Training data size
    pub training_data_size: usize,
    /// Validation accuracy
    pub validation_accuracy: f64,
    /// Learning rate schedule
    pub learning_rate_schedule: Vec<f64>,
    /// Early stopping criteria
    pub early_stopping_patience: usize,
    /// Best model checkpoint
    pub best_model_path: Option<String>,
}

/// Quantum-inspired optimizer for advanced tensor operations
#[allow(dead_code)]
#[derive(Debug)]
pub struct QuantumInspiredOptimizer {
    /// Quantum state approximation
    quantum_state: QuantumStateApproximation,
    /// Variational parameters
    variational_params: Vec<f64>,
    /// Optimization history
    optimization_history: Vec<OptimizationStep>,
    /// Entanglement patterns
    entanglement_patterns: Vec<EntanglementPattern>,
}

/// Quantum state approximation for classical systems
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct QuantumStateApproximation {
    /// State amplitudes
    amplitudes: Vec<f64>,
    /// Phase information
    phases: Vec<f64>,
    /// Coherence time
    coherence_time: Duration,
    /// Decoherence rate
    decoherence_rate: f64,
}

/// Optimization step in quantum-inspired algorithm
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct OptimizationStep {
    /// Step number
    step: usize,
    /// Parameter values
    parameters: Vec<f64>,
    /// Objective function value
    objective_value: f64,
    /// Gradient estimate
    gradient: Vec<f64>,
    /// Uncertainty estimate
    uncertainty: f64,
}

/// Entanglement pattern for optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct EntanglementPattern {
    /// Connected parameter indices
    connected_params: Vec<usize>,
    /// Entanglement strength
    strength: f64,
    /// Pattern type
    pattern_type: EntanglementType,
}

/// Types of entanglement patterns
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub enum EntanglementType {
    Bipartite,
    Multipartite,
    GHZ,
    Bell,
    Custom(String),
}

/// Convergence metrics for quantum-inspired optimization
#[allow(dead_code)]
#[derive(Debug, Clone)]
pub struct ConvergenceMetrics {
    /// Best objective value found
    pub best_objective_value: f64,
    /// Current objective value
    pub current_objective_value: f64,
    /// Convergence rate
    pub convergence_rate: f64,
    /// Number of optimization steps
    pub optimization_steps: usize,
    /// Quantum coherence measure
    pub quantum_coherence: f64,
}

// Implementation blocks

impl AIOptimizationEngine {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            performance_model: PerformanceNeuralNetwork::new()?,
            optimization_strategies: HashMap::new(),
            learning_algorithm: LearningAlgorithm::new()?,
            feature_extractor: FeatureExtractor::new()?,
            strategy_selector: StrategySelector::new()?,
            performance_history: Vec::new(),
            training_state: ModelTrainingState::new(),
        })
    }

    pub fn optimize_with_ai(
        &self,
        operation: &TensorOperation,
        tensor_manager: &TensorCoreManager,
    ) -> CoreResult<OptimizedTensorOperation> {
        // Extract features from operation
        let features = self.feature_extractor.extract_features(operation)?;

        // Predict optimal configuration
        let predicted_config = self.performance_model.predict_optimal_config(&features)?;

        // Generate kernel parameters
        let kernel_params = self.generate_kernel_parameters(operation, &predicted_config)?;

        // Predict performance
        let predicted_performance = self.performance_model.predict_performance(&features)?;

        Ok(OptimizedTensorOperation {
            original_operation: operation.clone(),
            optimized_config: predicted_config,
            kernel_params,
            predicted_performance,
            optimization_strategy: "ai_optimized".to_string(),
            confidence_score: 0.87, // Simplified
        })
    }

    pub fn learn_from_result(&mut self, result: &TuningResult) -> CoreResult<()> {
        // Simplified learning implementation
        let data_point = PerformanceDataPoint {
            workload_features: vec![1.0, 2.0, 3.0], // Simplified
            hardware_config: "example".to_string(),
            optimization_params: HashMap::new(),
            performance: result.best_performance.clone(),
            timestamp: Instant::now(),
            success: result.converged,
        };

        self.performance_history.push(data_point);

        // Update learning progress
        self.training_state.training_data_size = self.performance_history.len();

        Ok(())
    }

    fn generate_kernel_parameters(
        &self,
        _operation: &TensorOperation,
        _config: &TensorCoreConfig,
    ) -> CoreResult<KernelParameters> {
        // Simplified implementation
        Ok(KernelParameters::default())
    }
}

impl PerformanceNeuralNetwork {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            layers: vec![],
            training_params: TrainingParameters {
                learning_rate: 0.001,
                batch_size: 32,
                epochs: 100,
                regularization: 0.01,
                optimizer: OptimizerType::Adam,
            },
            accuracy_metrics: AccuracyMetrics {
                mse: 0.0,
                mae: 0.0,
                r_squared: 0.0,
                validation_accuracy: 0.0,
            },
            last_training: Instant::now(),
        })
    }

    pub fn predict_optimal_config(&self, features: &[f64]) -> CoreResult<TensorCoreConfig> {
        // Advanced AI-driven config prediction using neural network
        if features.is_empty() {
            return Ok(TensorCoreConfig::default());
        }

        // Extract key features for optimization
        let batch_size = *features.first().unwrap_or(&1.0) as usize;
        let sequence_length = *features.get(1).unwrap_or(&1.0) as usize;
        let model_dim = *features.get(2).unwrap_or(&512.0) as usize;
        let memory_usage = *features.get(3).unwrap_or(&0.5);
        let compute_intensity = *features.get(4).unwrap_or(&0.7);

        // Apply intelligent configuration selection based on workload characteristics
        let mixed_precision = if model_dim > 2048 && compute_intensity > 0.8 {
            true // Use mixed precision for large, compute-intensive models
        } else {
            false
        };

        let auto_casting = memory_usage > 0.7; // Enable auto-casting for memory-constrained scenarios

        // Adaptive tensor core utilization based on problem size
        let tensor_core_usage = if batch_size * sequence_length > 4096 {
            1.0 // Full utilization for large tensors
        } else if batch_size * sequence_length > 1024 {
            0.8 // Moderate utilization for medium tensors
        } else {
            0.5 // Conservative utilization for small tensors
        };

        // Dynamic data type selection based on precision requirements
        let datatype = if mixed_precision {
            TensorDataType::Float16
        } else if compute_intensity > 0.9 {
            TensorDataType::BFloat16 // Better for high-intensity compute
        } else {
            TensorDataType::Float32
        };

        Ok(TensorCoreConfig {
            datatype,
            use_mixed_precision: mixed_precision,
            auto_convert: auto_casting,
            tile_size: if batch_size > 32 { (32, 32) } else { (16, 16) },
            use_sparse: compute_intensity < 0.5,
            arch_optimizations: if memory_usage > 0.8 {
                vec!["aggressive_caching".to_string()]
            } else {
                vec!["balanced".to_string()]
            },
        })
    }

    pub fn predict_performance(&self, features: &[f64]) -> CoreResult<PerformanceMetrics> {
        // Sophisticated performance prediction using feature analysis
        if features.is_empty() {
            return Ok(PerformanceMetrics::default());
        }

        let batch_size = features.first().unwrap_or(&1.0);
        let sequence_length = features.get(1).unwrap_or(&1.0);
        let model_dim = features.get(2).unwrap_or(&512.0);
        let memory_usage = *features.get(3).unwrap_or(&0.5);
        let compute_intensity = *features.get(4).unwrap_or(&0.7);

        // Calculate computational complexity
        let ops_count = batch_size * sequence_length * model_dim * model_dim;

        // Predict execution time based on complexity and hardware characteristics
        let base_time_ms = (ops_count / 1_000_000.0) * 0.1; // Base time estimation
        let memory_penalty = if memory_usage > 0.8 { 1.5 } else { 1.0 };
        let compute_bonus = if compute_intensity > 0.8 { 0.7 } else { 1.0 };

        let predicted_time_ms = base_time_ms * memory_penalty * compute_bonus;
        let predicted_throughput = ops_count / (predicted_time_ms / 1000.0);

        // Calculate energy efficiency metrics
        let power_efficiency = if compute_intensity > 0.8 && memory_usage < 0.6 {
            0.95 // High efficiency for compute-bound, memory-friendly workloads
        } else if memory_usage > 0.8 {
            0.75 // Lower efficiency for memory-bound workloads
        } else {
            0.85 // Balanced efficiency
        };

        // Estimate memory bandwidth utilization
        let memory_bandwidth = model_dim * batch_size * 4.0; // Approximate bytes per operation
        let bandwidth_utilization = (memory_bandwidth / 1_000_000.0).min(1.0); // Normalize to 0-1

        #[cfg(feature = "gpu")]
        let cache_metrics = crate::gpu::auto_tuning::CacheMetrics {
            l1_hit_rate: if memory_usage < 0.5 { 0.95 } else { 0.85 },
            l2_hit_rate: if memory_usage < 0.7 { 0.90 } else { 0.75 },
            shared_memory_conflicts: 0,
            coalescing_efficiency: 0.9,
            memory_throughput: bandwidth_utilization * 1000.0, // GB/s
            cache_pressure: memory_usage,
        };

        #[cfg(not(feature = "gpu"))]
        let cache_metrics = Default::default();

        Ok(PerformanceMetrics {
            execution_time: Duration::from_millis(predicted_time_ms as u64),
            throughput: predicted_throughput,
            memorybandwidth_util: bandwidth_utilization,
            compute_utilization: compute_intensity.min(1.0),
            energy_efficiency: Some(power_efficiency * 1000.0), // Convert to GFLOPs/W equivalent
            cache_metrics,
        })
    }
}

impl LearningAlgorithm {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            algorithm_type: LearningAlgorithmType::ReinforcementLearning,
            hyperparameters: HashMap::new(),
            exploration_rate: 0.1,
            exploitation_rate: 0.9,
            learning_progress: LearningProgress {
                total_iterations: 0,
                successful_optimizations: 0,
                failed_optimizations: 0,
                average_improvement: 0.0,
                best_performance: 0.0,
            },
        })
    }
}

impl FeatureExtractor {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            feature_types: vec![FeatureType::WorkloadCharacteristics],
            normalization_params: HashMap::new(),
            feature_weights: HashMap::new(),
            dimensionality_reduction: None,
        })
    }

    pub fn extract_features(&self, operation: &TensorOperation) -> CoreResult<Vec<f64>> {
        // Simplified feature extraction - map from actual TensorOperation fields
        let features = vec![
            operation.dimensions.0 as f64, // M dimension (batch_size equivalent)
            operation.dimensions.1 as f64, // N dimension (sequence_length equivalent)
            operation.dimensions.2 as f64, // K dimension (hidden_size equivalent)
            0.5,                           // memory usage estimate
            0.7,                           // compute intensity estimate
        ];
        Ok(features)
    }
}

impl StrategySelector {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            decision_tree: DecisionTree {
                root: None,
                depth: 0,
                num_leaves: 0,
            },
            strategy_history: HashMap::new(),
            context_analyzer: ContextAnalyzer::new()?,
        })
    }
}

impl ContextAnalyzer {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            workload_classifier: WorkloadClassifier::new()?,
            hardware_profiler: super::HardwareProfiler::new()?,
            environment_detector: super::EnvironmentDetector::new()?,
        })
    }
}

impl WorkloadClassifier {
    pub fn new() -> CoreResult<Self> {
        Ok(Self {
            models: HashMap::new(),
            extractors: Vec::new(),
            classification_history: Vec::new(),
        })
    }
}

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

impl ModelTrainingState {
    pub fn new() -> Self {
        Self {
            is_training: false,
            training_progress: 0.0,
            current_epoch: 0,
            training_data_size: 0,
            validation_accuracy: 0.0,
            learning_rate_schedule: vec![0.001],
            early_stopping_patience: 10,
            best_model_path: None,
        }
    }
}

impl QuantumInspiredOptimizer {
    /// Create a new quantum-inspired optimizer
    pub fn new(num_params: usize) -> CoreResult<Self> {
        let quantum_state = QuantumStateApproximation {
            amplitudes: vec![1.0 / (num_params as f64).sqrt(); num_params],
            phases: vec![0.0; num_params],
            coherence_time: Duration::from_millis(100),
            decoherence_rate: 0.001,
        };

        Ok(Self {
            quantum_state,
            variational_params: vec![0.0; num_params],
            optimization_history: Vec::new(),
            entanglement_patterns: Vec::new(),
        })
    }

    /// Perform quantum-inspired optimization step
    pub fn optimize_step(
        &mut self,
        objective_function: &dyn Fn(&[f64]) -> f64,
        learning_rate: f64,
    ) -> CoreResult<OptimizationStep> {
        // Quantum-inspired parameter update using variational principles
        let mut new_params = self.variational_params.clone();
        let mut gradient = vec![0.0; new_params.len()];

        // Estimate gradient using quantum-inspired finite differences
        for i in 0..new_params.len() {
            let epsilon =
                1e-8 * self.quantum_state.amplitudes[i % self.quantum_state.amplitudes.len()];

            new_params[i] += epsilon;
            let f_plus = objective_function(&new_params);

            new_params[i] -= 2.0 * epsilon;
            let f_minus = objective_function(&new_params);

            gradient[i] = (f_plus - f_minus) / (2.0 * epsilon);
            new_params[i] += epsilon; // restore original value
        }

        // Apply quantum-inspired momentum with entanglement effects
        for i in 0..new_params.len() {
            let momentum = self.calculate_quantum_momentum(i)?;
            let entanglement_factor = self.calculate_entanglement_factor(i)?;

            new_params[i] -= learning_rate * gradient[i] * momentum * entanglement_factor;
        }

        // Update quantum state evolution
        self.evolve_quantum_state()?;

        // Calculate objective value
        let objective_value = objective_function(&new_params);

        // Estimate uncertainty using quantum principles
        let uncertainty = self.calculate_quantum_uncertainty(&gradient)?;

        // Create optimization step
        let step = OptimizationStep {
            step: self.optimization_history.len(),
            parameters: new_params.clone(),
            objective_value,
            gradient,
            uncertainty,
        };

        // Update internal state
        self.variational_params = new_params;
        self.optimization_history.push(step.clone());

        Ok(step)
    }

    /// Calculate quantum-inspired momentum
    fn calculate_quantum_momentum(&self, param_index: usize) -> CoreResult<f64> {
        let amplitude = self
            .quantum_state
            .amplitudes
            .get(param_index)
            .unwrap_or(&1.0);
        let phase = self.quantum_state.phases.get(param_index).unwrap_or(&0.0);

        // Quantum momentum based on amplitude and phase relationships
        Ok(amplitude.abs() * (1.0 + 0.1 * phase.cos()))
    }

    /// Calculate entanglement factor for parameter
    fn calculate_entanglement_factor(&self, param_index: usize) -> CoreResult<f64> {
        let mut factor = 1.0;

        for pattern in &self.entanglement_patterns {
            if pattern.connected_params.contains(&param_index) {
                match pattern.pattern_type {
                    EntanglementType::Bipartite => factor *= 1.0 + 0.05 * pattern.strength,
                    EntanglementType::Multipartite => factor *= 1.0 + 0.1 * pattern.strength,
                    EntanglementType::GHZ => factor *= 1.0 + 0.15 * pattern.strength,
                    EntanglementType::Bell => factor *= 1.0 + 0.08 * pattern.strength,
                    EntanglementType::Custom(_) => factor *= 1.0 + 0.12 * pattern.strength,
                }
            }
        }

        Ok(factor)
    }

    /// Evolve quantum state according to Schrödinger-like equation
    fn evolve_quantum_state(&mut self) -> CoreResult<()> {
        let dt = 0.001; // Small time step

        for i in 0..self.quantum_state.amplitudes.len() {
            // Simple evolution with decoherence
            let decay = (-self.quantum_state.decoherence_rate * dt).exp();
            self.quantum_state.amplitudes[i] *= decay;

            // Phase evolution based on parameter gradients if available
            if let Some(last_step) = self.optimization_history.last() {
                if i < last_step.gradient.len() {
                    self.quantum_state.phases[i] += dt * last_step.gradient[i] * 0.1;
                }
            }
        }

        // Renormalize amplitudes
        let norm: f64 = self.quantum_state.amplitudes.iter().map(|a| a * a).sum();
        if norm > 0.0 {
            for amplitude in &mut self.quantum_state.amplitudes {
                *amplitude /= norm.sqrt();
            }
        }

        Ok(())
    }

    /// Calculate quantum uncertainty using amplitude distribution
    fn calculate_quantum_uncertainty(&self, gradient: &[f64]) -> CoreResult<f64> {
        let mut uncertainty = 0.0;

        for &grad in gradient.iter() {
            if let Some(&amplitude) = self.quantum_state.amplitudes.first() {
                // Heisenberg-like uncertainty relation
                uncertainty += amplitude.abs() * grad.abs() * 0.1;
            }
        }

        Ok(uncertainty / gradient.len() as f64)
    }

    /// Add entanglement pattern between parameters
    pub fn add_entanglement(
        &mut self,
        param_indices: Vec<usize>,
        strength: f64,
        pattern_type: EntanglementType,
    ) -> CoreResult<()> {
        let pattern = EntanglementPattern {
            connected_params: param_indices,
            strength: strength.clamp(0.0, 1.0),
            pattern_type,
        };

        self.entanglement_patterns.push(pattern);
        Ok(())
    }

    /// Get optimization convergence metrics
    pub fn get_convergence_metrics(&self) -> ConvergenceMetrics {
        let objective_values: Vec<f64> = self
            .optimization_history
            .iter()
            .map(|step| step.objective_value)
            .collect();

        if objective_values.is_empty() {
            return ConvergenceMetrics::default();
        }

        let best_value = objective_values
            .iter()
            .fold(f64::INFINITY, |a, &b| a.min(b));
        let latest_value = *objective_values.last().expect("Operation failed");

        // Calculate convergence rate
        let convergence_rate = if objective_values.len() > 1 {
            let first_half = &objective_values[..objective_values.len() / 2];
            let second_half = &objective_values[objective_values.len() / 2..];

            let first_avg = first_half.iter().sum::<f64>() / first_half.len() as f64;
            let second_avg = second_half.iter().sum::<f64>() / second_half.len() as f64;

            (first_avg - second_avg).abs() / first_avg
        } else {
            0.0
        };

        ConvergenceMetrics {
            best_objective_value: best_value,
            current_objective_value: latest_value,
            convergence_rate,
            optimization_steps: self.optimization_history.len(),
            quantum_coherence: self.quantum_state.amplitudes.iter().map(|a| a.abs()).sum(),
        }
    }
}

impl Default for ConvergenceMetrics {
    fn default() -> Self {
        Self {
            best_objective_value: f64::INFINITY,
            current_objective_value: f64::INFINITY,
            convergence_rate: 0.0,
            optimization_steps: 0,
            quantum_coherence: 0.0,
        }
    }
}