quantrs2_sim/

quantum_machine_learning_layers.rs

//! Quantum Machine Learning Layers Framework
//!
//! This module provides a comprehensive implementation of quantum machine learning layers,
//! including parameterized quantum circuits, quantum convolutional layers, quantum recurrent
//! networks, and hybrid classical-quantum training algorithms. This framework enables
//! quantum advantage in machine learning applications with hardware-aware optimization.

use scirs2_core::ndarray::Array1;
use scirs2_core::parallel_ops::{IndexedParallelIterator, ParallelIterator};
use scirs2_core::random::{thread_rng, Rng};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::f64::consts::PI;

use crate::error::{Result, SimulatorError};
use crate::scirs2_integration::SciRS2Backend;
use crate::statevector::StateVectorSimulator;
use scirs2_core::random::prelude::*;

/// Quantum machine learning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QMLConfig {
    /// Number of qubits in the quantum layer
    pub num_qubits: usize,
    /// QML architecture type
    pub architecture_type: QMLArchitectureType,
    /// Layer configuration for each QML layer
    pub layer_configs: Vec<QMLLayerConfig>,
    /// Training algorithm configuration
    pub training_config: QMLTrainingConfig,
    /// Hardware-aware optimization settings
    pub hardware_optimization: HardwareOptimizationConfig,
    /// Classical preprocessing configuration
    pub classical_preprocessing: ClassicalPreprocessingConfig,
    /// Hybrid training configuration
    pub hybrid_training: HybridTrainingConfig,
    /// Enable quantum advantage analysis
    pub quantum_advantage_analysis: bool,
    /// Noise-aware training settings
    pub noise_aware_training: NoiseAwareTrainingConfig,
    /// Performance optimization settings
    pub performance_optimization: PerformanceOptimizationConfig,
}

impl Default for QMLConfig {
    fn default() -> Self {
        Self {
            num_qubits: 8,
            architecture_type: QMLArchitectureType::VariationalQuantumCircuit,
            layer_configs: vec![QMLLayerConfig {
                layer_type: QMLLayerType::ParameterizedQuantumCircuit,
                num_parameters: 16,
                ansatz_type: AnsatzType::Hardware,
                entanglement_pattern: EntanglementPattern::Linear,
                rotation_gates: vec![RotationGate::RY, RotationGate::RZ],
                depth: 4,
                enable_gradient_computation: true,
            }],
            training_config: QMLTrainingConfig::default(),
            hardware_optimization: HardwareOptimizationConfig::default(),
            classical_preprocessing: ClassicalPreprocessingConfig::default(),
            hybrid_training: HybridTrainingConfig::default(),
            quantum_advantage_analysis: true,
            noise_aware_training: NoiseAwareTrainingConfig::default(),
            performance_optimization: PerformanceOptimizationConfig::default(),
        }
    }
}

/// QML architecture types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QMLArchitectureType {
    /// Variational Quantum Circuit (VQC)
    VariationalQuantumCircuit,
    /// Quantum Convolutional Neural Network
    QuantumConvolutionalNN,
    /// Quantum Recurrent Neural Network
    QuantumRecurrentNN,
    /// Quantum Graph Neural Network
    QuantumGraphNN,
    /// Quantum Attention Network
    QuantumAttentionNetwork,
    /// Quantum Transformer
    QuantumTransformer,
    /// Hybrid Classical-Quantum Network
    HybridClassicalQuantum,
    /// Quantum Boltzmann Machine
    QuantumBoltzmannMachine,
    /// Quantum Generative Adversarial Network
    QuantumGAN,
    /// Quantum Autoencoder
    QuantumAutoencoder,
}

/// QML layer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QMLLayerConfig {
    /// Type of QML layer
    pub layer_type: QMLLayerType,
    /// Number of trainable parameters
    pub num_parameters: usize,
    /// Ansatz type for parameterized circuits
    pub ansatz_type: AnsatzType,
    /// Entanglement pattern
    pub entanglement_pattern: EntanglementPattern,
    /// Rotation gates to use
    pub rotation_gates: Vec<RotationGate>,
    /// Circuit depth
    pub depth: usize,
    /// Enable gradient computation
    pub enable_gradient_computation: bool,
}

/// Types of QML layers
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QMLLayerType {
    /// Parameterized Quantum Circuit layer
    ParameterizedQuantumCircuit,
    /// Quantum Convolutional layer
    QuantumConvolutional,
    /// Quantum Pooling layer
    QuantumPooling,
    /// Quantum Dense layer (fully connected)
    QuantumDense,
    /// Quantum LSTM layer
    QuantumLSTM,
    /// Quantum GRU layer
    QuantumGRU,
    /// Quantum Attention layer
    QuantumAttention,
    /// Quantum Dropout layer
    QuantumDropout,
    /// Quantum Batch Normalization layer
    QuantumBatchNorm,
    /// Data Re-uploading layer
    DataReUpload,
}

/// Ansatz types for parameterized quantum circuits
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AnsatzType {
    /// Hardware-efficient ansatz
    Hardware,
    /// Problem-specific ansatz
    ProblemSpecific,
    /// All-to-all connectivity ansatz
    AllToAll,
    /// Layered ansatz
    Layered,
    /// Alternating ansatz
    Alternating,
    /// Brick-wall ansatz
    BrickWall,
    /// Tree ansatz
    Tree,
    /// Custom ansatz
    Custom,
}

/// Entanglement patterns
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum EntanglementPattern {
    /// Linear entanglement chain
    Linear,
    /// Circular entanglement
    Circular,
    /// All-to-all entanglement
    AllToAll,
    /// Star topology entanglement
    Star,
    /// Grid topology entanglement
    Grid,
    /// Random entanglement
    Random,
    /// Block entanglement
    Block,
    /// Custom pattern
    Custom,
}

/// Rotation gates for parameterized circuits
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RotationGate {
    /// Rotation around X-axis
    RX,
    /// Rotation around Y-axis
    RY,
    /// Rotation around Z-axis
    RZ,
    /// Arbitrary single-qubit rotation
    U3,
    /// Phase gate
    Phase,
}

/// QML training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QMLTrainingConfig {
    /// Training algorithm type
    pub algorithm: QMLTrainingAlgorithm,
    /// Learning rate
    pub learning_rate: f64,
    /// Number of training epochs
    pub epochs: usize,
    /// Batch size
    pub batch_size: usize,
    /// Gradient computation method
    pub gradient_method: GradientMethod,
    /// Optimizer type
    pub optimizer: OptimizerType,
    /// Regularization parameters
    pub regularization: RegularizationConfig,
    /// Early stopping configuration
    pub early_stopping: EarlyStoppingConfig,
    /// Learning rate scheduling
    pub lr_schedule: LearningRateSchedule,
}

impl Default for QMLTrainingConfig {
    fn default() -> Self {
        Self {
            algorithm: QMLTrainingAlgorithm::ParameterShift,
            learning_rate: 0.01,
            epochs: 100,
            batch_size: 32,
            gradient_method: GradientMethod::ParameterShift,
            optimizer: OptimizerType::Adam,
            regularization: RegularizationConfig::default(),
            early_stopping: EarlyStoppingConfig::default(),
            lr_schedule: LearningRateSchedule::Constant,
        }
    }
}

/// QML training algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QMLTrainingAlgorithm {
    /// Parameter-shift rule gradient descent
    ParameterShift,
    /// Finite difference gradient descent
    FiniteDifference,
    /// Quantum Natural Gradient
    QuantumNaturalGradient,
    /// SPSA (Simultaneous Perturbation Stochastic Approximation)
    SPSA,
    /// Quantum Approximate Optimization Algorithm
    QAOA,
    /// Variational Quantum Eigensolver
    VQE,
    /// Quantum Machine Learning with Rotosolve
    Rotosolve,
    /// Hybrid Classical-Quantum training
    HybridTraining,
}

/// Gradient computation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum GradientMethod {
    /// Parameter-shift rule
    ParameterShift,
    /// Finite difference
    FiniteDifference,
    /// Adjoint differentiation
    Adjoint,
    /// Backpropagation through quantum circuit
    Backpropagation,
    /// Quantum Fisher Information
    QuantumFisherInformation,
}

/// Optimizer types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizerType {
    /// Stochastic Gradient Descent
    SGD,
    /// Adam optimizer
    Adam,
    /// `AdaGrad` optimizer
    AdaGrad,
    /// `RMSprop` optimizer
    RMSprop,
    /// Momentum optimizer
    Momentum,
    /// L-BFGS optimizer
    LBFGS,
    /// Quantum Natural Gradient
    QuantumNaturalGradient,
    /// SPSA optimizer
    SPSA,
}

/// Regularization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RegularizationConfig {
    /// L1 regularization strength
    pub l1_strength: f64,
    /// L2 regularization strength
    pub l2_strength: f64,
    /// Dropout probability
    pub dropout_prob: f64,
    /// Parameter constraint bounds
    pub parameter_bounds: Option<(f64, f64)>,
    /// Enable parameter clipping
    pub enable_clipping: bool,
    /// Gradient clipping threshold
    pub gradient_clip_threshold: f64,
}

impl Default for RegularizationConfig {
    fn default() -> Self {
        Self {
            l1_strength: 0.0,
            l2_strength: 0.001,
            dropout_prob: 0.1,
            parameter_bounds: Some((-PI, PI)),
            enable_clipping: true,
            gradient_clip_threshold: 1.0,
        }
    }
}

/// Early stopping configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EarlyStoppingConfig {
    /// Enable early stopping
    pub enabled: bool,
    /// Patience (number of epochs without improvement)
    pub patience: usize,
    /// Minimum improvement threshold
    pub min_delta: f64,
    /// Metric to monitor
    pub monitor_metric: String,
    /// Whether higher values are better
    pub mode_max: bool,
}

impl Default for EarlyStoppingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            patience: 10,
            min_delta: 1e-6,
            monitor_metric: "val_loss".to_string(),
            mode_max: false,
        }
    }
}

/// Learning rate schedules
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum LearningRateSchedule {
    /// Constant learning rate
    Constant,
    /// Exponential decay
    ExponentialDecay,
    /// Step decay
    StepDecay,
    /// Cosine annealing
    CosineAnnealing,
    /// Warm restart
    WarmRestart,
    /// Reduce on plateau
    ReduceOnPlateau,
}

/// Hardware optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareOptimizationConfig {
    /// Target quantum hardware
    pub target_hardware: QuantumHardwareTarget,
    /// Enable gate count minimization
    pub minimize_gate_count: bool,
    /// Enable circuit depth minimization
    pub minimize_depth: bool,
    /// Enable noise-aware optimization
    pub noise_aware: bool,
    /// Connectivity constraints
    pub connectivity_constraints: ConnectivityConstraints,
    /// Gate fidelity constraints
    pub gate_fidelities: HashMap<String, f64>,
    /// Enable parallelization
    pub enable_parallelization: bool,
    /// Compilation optimization level
    pub optimization_level: HardwareOptimizationLevel,
}

impl Default for HardwareOptimizationConfig {
    fn default() -> Self {
        let mut gate_fidelities = HashMap::new();
        gate_fidelities.insert("single_qubit".to_string(), 0.999);
        gate_fidelities.insert("two_qubit".to_string(), 0.99);

        Self {
            target_hardware: QuantumHardwareTarget::Simulator,
            minimize_gate_count: true,
            minimize_depth: true,
            noise_aware: false,
            connectivity_constraints: ConnectivityConstraints::AllToAll,
            gate_fidelities,
            enable_parallelization: true,
            optimization_level: HardwareOptimizationLevel::Medium,
        }
    }
}

/// Quantum hardware targets
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumHardwareTarget {
    /// Generic simulator
    Simulator,
    /// IBM Quantum devices
    IBM,
    /// Google Quantum AI devices
    Google,
    /// `IonQ` devices
    IonQ,
    /// Rigetti devices
    Rigetti,
    /// Honeywell/Quantinuum devices
    Quantinuum,
    /// Xanadu devices
    Xanadu,
    /// Custom hardware specification
    Custom,
}

/// Connectivity constraints
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectivityConstraints {
    /// All-to-all connectivity
    AllToAll,
    /// Linear chain connectivity
    Linear,
    /// Grid connectivity
    Grid(usize, usize), // rows, cols
    /// Custom connectivity graph
    Custom(Vec<(usize, usize)>), // edge list
    /// Heavy-hex connectivity (IBM)
    HeavyHex,
    /// Square lattice connectivity
    Square,
}

/// Hardware optimization levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum HardwareOptimizationLevel {
    /// Basic optimization
    Basic,
    /// Medium optimization
    Medium,
    /// Aggressive optimization
    Aggressive,
    /// Maximum optimization
    Maximum,
}

/// Classical preprocessing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClassicalPreprocessingConfig {
    /// Enable feature scaling
    pub feature_scaling: bool,
    /// Scaling method
    pub scaling_method: ScalingMethod,
    /// Principal Component Analysis
    pub enable_pca: bool,
    /// Number of PCA components
    pub pca_components: Option<usize>,
    /// Data encoding method
    pub encoding_method: DataEncodingMethod,
    /// Feature selection
    pub feature_selection: FeatureSelectionConfig,
}

impl Default for ClassicalPreprocessingConfig {
    fn default() -> Self {
        Self {
            feature_scaling: true,
            scaling_method: ScalingMethod::StandardScaler,
            enable_pca: false,
            pca_components: None,
            encoding_method: DataEncodingMethod::Amplitude,
            feature_selection: FeatureSelectionConfig::default(),
        }
    }
}

/// Scaling methods for classical preprocessing
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ScalingMethod {
    /// Standard scaling (z-score normalization)
    StandardScaler,
    /// Min-max scaling
    MinMaxScaler,
    /// Robust scaling
    RobustScaler,
    /// Quantile uniform scaling
    QuantileUniform,
    /// Power transformation
    PowerTransformer,
}

/// Data encoding methods for quantum circuits
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DataEncodingMethod {
    /// Amplitude encoding
    Amplitude,
    /// Angle encoding
    Angle,
    /// Basis encoding
    Basis,
    /// Quantum feature maps
    QuantumFeatureMap,
    /// IQP encoding
    IQP,
    /// Pauli feature maps
    PauliFeatureMap,
    /// Data re-uploading
    DataReUpload,
}

/// Feature selection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureSelectionConfig {
    /// Enable feature selection
    pub enabled: bool,
    /// Feature selection method
    pub method: FeatureSelectionMethod,
    /// Number of features to select
    pub num_features: Option<usize>,
    /// Selection threshold
    pub threshold: f64,
}

impl Default for FeatureSelectionConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            method: FeatureSelectionMethod::VarianceThreshold,
            num_features: None,
            threshold: 0.0,
        }
    }
}

/// Feature selection methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum FeatureSelectionMethod {
    /// Variance threshold
    VarianceThreshold,
    /// Univariate statistical tests
    UnivariateSelection,
    /// Recursive feature elimination
    RecursiveFeatureElimination,
    /// L1-based feature selection
    L1Based,
    /// Tree-based feature selection
    TreeBased,
    /// Quantum feature importance
    QuantumFeatureImportance,
}

/// Hybrid training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HybridTrainingConfig {
    /// Enable hybrid classical-quantum training
    pub enabled: bool,
    /// Classical neural network architecture
    pub classical_architecture: ClassicalArchitecture,
    /// Quantum-classical interface
    pub interface_config: QuantumClassicalInterface,
    /// Alternating training schedule
    pub alternating_schedule: AlternatingSchedule,
    /// Gradient flow configuration
    pub gradient_flow: GradientFlowConfig,
}

impl Default for HybridTrainingConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            classical_architecture: ClassicalArchitecture::MLP,
            interface_config: QuantumClassicalInterface::Expectation,
            alternating_schedule: AlternatingSchedule::Simultaneous,
            gradient_flow: GradientFlowConfig::default(),
        }
    }
}

/// Classical neural network architectures for hybrid training
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ClassicalArchitecture {
    /// Multi-layer perceptron
    MLP,
    /// Convolutional neural network
    CNN,
    /// Recurrent neural network
    RNN,
    /// Long short-term memory
    LSTM,
    /// Transformer
    Transformer,
    /// `ResNet`
    ResNet,
    /// Custom architecture
    Custom,
}

/// Quantum-classical interfaces
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumClassicalInterface {
    /// Expectation value measurement
    Expectation,
    /// Sampling-based measurement
    Sampling,
    /// Quantum state tomography
    StateTomography,
    /// Process tomography
    ProcessTomography,
    /// Shadow tomography
    ShadowTomography,
}

/// Alternating training schedules for hybrid systems
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AlternatingSchedule {
    /// Train classical and quantum parts simultaneously
    Simultaneous,
    /// Alternate between classical and quantum training
    Alternating,
    /// Train classical first, then quantum
    ClassicalFirst,
    /// Train quantum first, then classical
    QuantumFirst,
    /// Custom schedule
    Custom,
}

/// Gradient flow configuration for hybrid training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientFlowConfig {
    /// Enable gradient flow from classical to quantum
    pub classical_to_quantum: bool,
    /// Enable gradient flow from quantum to classical
    pub quantum_to_classical: bool,
    /// Gradient scaling factor
    pub gradient_scaling: f64,
    /// Enable gradient clipping
    pub enable_clipping: bool,
    /// Gradient accumulation steps
    pub accumulation_steps: usize,
}

impl Default for GradientFlowConfig {
    fn default() -> Self {
        Self {
            classical_to_quantum: true,
            quantum_to_classical: true,
            gradient_scaling: 1.0,
            enable_clipping: true,
            accumulation_steps: 1,
        }
    }
}

/// Noise-aware training configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct NoiseAwareTrainingConfig {
    /// Enable noise-aware training
    pub enabled: bool,
    /// Noise model parameters
    pub noise_parameters: NoiseParameters,
    /// Error mitigation techniques
    pub error_mitigation: ErrorMitigationConfig,
    /// Noise characterization
    pub noise_characterization: NoiseCharacterizationConfig,
    /// Robust training methods
    pub robust_training: RobustTrainingConfig,
}

/// Noise parameters for quantum devices
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseParameters {
    /// Single-qubit gate error rates
    pub single_qubit_error: f64,
    /// Two-qubit gate error rates
    pub two_qubit_error: f64,
    /// Measurement error rates
    pub measurement_error: f64,
    /// Coherence times (T1, T2)
    pub coherence_times: (f64, f64),
    /// Gate times
    pub gate_times: HashMap<String, f64>,
}

impl Default for NoiseParameters {
    fn default() -> Self {
        let mut gate_times = HashMap::new();
        gate_times.insert("single_qubit".to_string(), 50e-9); // 50 ns
        gate_times.insert("two_qubit".to_string(), 200e-9); // 200 ns

        Self {
            single_qubit_error: 0.001,
            two_qubit_error: 0.01,
            measurement_error: 0.01,
            coherence_times: (50e-6, 100e-6), // T1 = 50 μs, T2 = 100 μs
            gate_times,
        }
    }
}

/// Error mitigation configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct ErrorMitigationConfig {
    /// Enable zero-noise extrapolation
    pub zero_noise_extrapolation: bool,
    /// Enable readout error mitigation
    pub readout_error_mitigation: bool,
    /// Enable symmetry verification
    pub symmetry_verification: bool,
    /// Virtual distillation parameters
    pub virtual_distillation: VirtualDistillationConfig,
    /// Quantum error correction
    pub quantum_error_correction: bool,
}

/// Virtual distillation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VirtualDistillationConfig {
    /// Enable virtual distillation
    pub enabled: bool,
    /// Number of copies for distillation
    pub num_copies: usize,
    /// Distillation protocol
    pub protocol: DistillationProtocol,
}

impl Default for VirtualDistillationConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            num_copies: 2,
            protocol: DistillationProtocol::Standard,
        }
    }
}

/// Distillation protocols
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DistillationProtocol {
    /// Standard distillation
    Standard,
    /// Improved distillation
    Improved,
    /// Quantum advantage distillation
    QuantumAdvantage,
}

/// Noise characterization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseCharacterizationConfig {
    /// Enable noise characterization
    pub enabled: bool,
    /// Characterization method
    pub method: NoiseCharacterizationMethod,
    /// Benchmarking protocols
    pub benchmarking: BenchmarkingProtocols,
    /// Calibration frequency
    pub calibration_frequency: CalibrationFrequency,
}

impl Default for NoiseCharacterizationConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            method: NoiseCharacterizationMethod::ProcessTomography,
            benchmarking: BenchmarkingProtocols::default(),
            calibration_frequency: CalibrationFrequency::Daily,
        }
    }
}

/// Noise characterization methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NoiseCharacterizationMethod {
    /// Quantum process tomography
    ProcessTomography,
    /// Randomized benchmarking
    RandomizedBenchmarking,
    /// Gate set tomography
    GateSetTomography,
    /// Quantum detector tomography
    QuantumDetectorTomography,
    /// Cross-entropy benchmarking
    CrossEntropyBenchmarking,
}

/// Benchmarking protocols
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkingProtocols {
    /// Enable randomized benchmarking
    pub randomized_benchmarking: bool,
    /// Enable quantum volume
    pub quantum_volume: bool,
    /// Enable cross-entropy benchmarking
    pub cross_entropy_benchmarking: bool,
    /// Enable mirror benchmarking
    pub mirror_benchmarking: bool,
}

impl Default for BenchmarkingProtocols {
    fn default() -> Self {
        Self {
            randomized_benchmarking: true,
            quantum_volume: false,
            cross_entropy_benchmarking: false,
            mirror_benchmarking: false,
        }
    }
}

/// Calibration frequency
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CalibrationFrequency {
    /// Real-time calibration
    RealTime,
    /// Hourly calibration
    Hourly,
    /// Daily calibration
    Daily,
    /// Weekly calibration
    Weekly,
    /// Manual calibration
    Manual,
}

/// Robust training configuration
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct RobustTrainingConfig {
    /// Enable robust training methods
    pub enabled: bool,
    /// Noise injection during training
    pub noise_injection: NoiseInjectionConfig,
    /// Adversarial training
    pub adversarial_training: AdversarialTrainingConfig,
    /// Ensemble methods
    pub ensemble_methods: EnsembleMethodsConfig,
}

/// Noise injection configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NoiseInjectionConfig {
    /// Enable noise injection
    pub enabled: bool,
    /// Noise injection probability
    pub injection_probability: f64,
    /// Noise strength
    pub noise_strength: f64,
    /// Noise type
    pub noise_type: NoiseType,
}

impl Default for NoiseInjectionConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            injection_probability: 0.1,
            noise_strength: 0.01,
            noise_type: NoiseType::Depolarizing,
        }
    }
}

/// Noise types for training
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NoiseType {
    /// Depolarizing noise
    Depolarizing,
    /// Amplitude damping
    AmplitudeDamping,
    /// Phase damping
    PhaseDamping,
    /// Bit flip
    BitFlip,
    /// Phase flip
    PhaseFlip,
    /// Pauli noise
    Pauli,
}

/// Adversarial training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdversarialTrainingConfig {
    /// Enable adversarial training
    pub enabled: bool,
    /// Adversarial attack strength
    pub attack_strength: f64,
    /// Attack method
    pub attack_method: AdversarialAttackMethod,
    /// Defense method
    pub defense_method: AdversarialDefenseMethod,
}

impl Default for AdversarialTrainingConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            attack_strength: 0.01,
            attack_method: AdversarialAttackMethod::FGSM,
            defense_method: AdversarialDefenseMethod::AdversarialTraining,
        }
    }
}

/// Adversarial attack methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdversarialAttackMethod {
    /// Fast Gradient Sign Method
    FGSM,
    /// Projected Gradient Descent
    PGD,
    /// C&W attack
    CarliniWagner,
    /// Quantum adversarial attacks
    QuantumAdversarial,
}

/// Adversarial defense methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AdversarialDefenseMethod {
    /// Adversarial training
    AdversarialTraining,
    /// Defensive distillation
    DefensiveDistillation,
    /// Certified defenses
    CertifiedDefenses,
    /// Quantum error correction defenses
    QuantumErrorCorrection,
}

/// Ensemble methods configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnsembleMethodsConfig {
    /// Enable ensemble methods
    pub enabled: bool,
    /// Number of ensemble members
    pub num_ensemble: usize,
    /// Ensemble method
    pub ensemble_method: EnsembleMethod,
    /// Voting strategy
    pub voting_strategy: VotingStrategy,
}

impl Default for EnsembleMethodsConfig {
    fn default() -> Self {
        Self {
            enabled: false,
            num_ensemble: 5,
            ensemble_method: EnsembleMethod::Bagging,
            voting_strategy: VotingStrategy::MajorityVoting,
        }
    }
}

/// Ensemble methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum EnsembleMethod {
    /// Bootstrap aggregating (bagging)
    Bagging,
    /// Boosting
    Boosting,
    /// Random forests
    RandomForest,
    /// Quantum ensemble methods
    QuantumEnsemble,
}

/// Voting strategies for ensembles
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum VotingStrategy {
    /// Majority voting
    MajorityVoting,
    /// Weighted voting
    WeightedVoting,
    /// Soft voting (probability averaging)
    SoftVoting,
    /// Quantum voting
    QuantumVoting,
}

/// Performance optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceOptimizationConfig {
    /// Enable performance optimization
    pub enabled: bool,
    /// Memory optimization
    pub memory_optimization: MemoryOptimizationConfig,
    /// Computation optimization
    pub computation_optimization: ComputationOptimizationConfig,
    /// Parallelization configuration
    pub parallelization: ParallelizationConfig,
    /// Caching configuration
    pub caching: CachingConfig,
}

impl Default for PerformanceOptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            memory_optimization: MemoryOptimizationConfig::default(),
            computation_optimization: ComputationOptimizationConfig::default(),
            parallelization: ParallelizationConfig::default(),
            caching: CachingConfig::default(),
        }
    }
}

/// Memory optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryOptimizationConfig {
    /// Enable memory optimization
    pub enabled: bool,
    /// Use memory mapping
    pub memory_mapping: bool,
    /// Gradient checkpointing
    pub gradient_checkpointing: bool,
    /// Memory pool size
    pub memory_pool_size: Option<usize>,
}

impl Default for MemoryOptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            memory_mapping: false,
            gradient_checkpointing: false,
            memory_pool_size: None,
        }
    }
}

/// Computation optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputationOptimizationConfig {
    /// Enable computation optimization
    pub enabled: bool,
    /// Use mixed precision
    pub mixed_precision: bool,
    /// SIMD optimization
    pub simd_optimization: bool,
    /// Just-in-time compilation
    pub jit_compilation: bool,
}

impl Default for ComputationOptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            mixed_precision: false,
            simd_optimization: true,
            jit_compilation: false,
        }
    }
}

/// Parallelization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelizationConfig {
    /// Enable parallelization
    pub enabled: bool,
    /// Number of threads
    pub num_threads: Option<usize>,
    /// Data parallelism
    pub data_parallelism: bool,
    /// Model parallelism
    pub model_parallelism: bool,
    /// Pipeline parallelism
    pub pipeline_parallelism: bool,
}

impl Default for ParallelizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            num_threads: None,
            data_parallelism: true,
            model_parallelism: false,
            pipeline_parallelism: false,
        }
    }
}

/// Caching configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CachingConfig {
    /// Enable caching
    pub enabled: bool,
    /// Cache size
    pub cache_size: usize,
    /// Cache gradients
    pub cache_gradients: bool,
    /// Cache intermediate results
    pub cache_intermediate: bool,
}

impl Default for CachingConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            cache_size: 1000,
            cache_gradients: true,
            cache_intermediate: false,
        }
    }
}

/// Main quantum machine learning layers framework
#[derive(Debug)]
pub struct QuantumMLFramework {
    /// Configuration
    config: QMLConfig,
    /// QML layers
    layers: Vec<Box<dyn QMLLayer>>,
    /// Current training state
    training_state: QMLTrainingState,
    /// `SciRS2` backend for numerical operations
    backend: Option<SciRS2Backend>,
    /// Performance statistics
    stats: QMLStats,
    /// Training history
    training_history: Vec<QMLTrainingResult>,
}

impl QuantumMLFramework {
    /// Create new quantum ML framework
    pub fn new(config: QMLConfig) -> Result<Self> {
        let mut framework = Self {
            config,
            layers: Vec::new(),
            training_state: QMLTrainingState::new(),
            backend: None,
            stats: QMLStats::new(),
            training_history: Vec::new(),
        };

        // Initialize layers based on configuration
        framework.initialize_layers()?;

        // Initialize SciRS2 backend if available
        let backend = SciRS2Backend::new();
        if backend.is_available() {
            framework.backend = Some(backend);
        }

        Ok(framework)
    }

    /// Initialize QML layers
    fn initialize_layers(&mut self) -> Result<()> {
        for layer_config in &self.config.layer_configs {
            let layer = self.create_layer(layer_config)?;
            self.layers.push(layer);
        }
        Ok(())
    }

    /// Create a QML layer based on configuration
    fn create_layer(&self, config: &QMLLayerConfig) -> Result<Box<dyn QMLLayer>> {
        match config.layer_type {
            QMLLayerType::ParameterizedQuantumCircuit => Ok(Box::new(
                ParameterizedQuantumCircuitLayer::new(self.config.num_qubits, config.clone())?,
            )),
            QMLLayerType::QuantumConvolutional => Ok(Box::new(QuantumConvolutionalLayer::new(
                self.config.num_qubits,
                config.clone(),
            )?)),
            QMLLayerType::QuantumDense => Ok(Box::new(QuantumDenseLayer::new(
                self.config.num_qubits,
                config.clone(),
            )?)),
            QMLLayerType::QuantumLSTM => Ok(Box::new(QuantumLSTMLayer::new(
                self.config.num_qubits,
                config.clone(),
            )?)),
            QMLLayerType::QuantumAttention => Ok(Box::new(QuantumAttentionLayer::new(
                self.config.num_qubits,
                config.clone(),
            )?)),
            _ => Err(SimulatorError::InvalidConfiguration(format!(
                "Layer type {:?} not yet implemented",
                config.layer_type
            ))),
        }
    }

    /// Forward pass through the quantum ML model
    pub fn forward(&mut self, input: &Array1<f64>) -> Result<Array1<f64>> {
        let mut current_state = self.encode_input(input)?;

        // Pass through each layer
        for layer in &mut self.layers {
            current_state = layer.forward(&current_state)?;
        }

        // Decode output
        let output = self.decode_output(&current_state)?;

        // Update statistics
        self.stats.forward_passes += 1;

        Ok(output)
    }

    /// Backward pass for gradient computation
    pub fn backward(&mut self, loss_gradient: &Array1<f64>) -> Result<Array1<f64>> {
        let mut grad = loss_gradient.clone();

        // Backpropagate through layers in reverse order
        for layer in self.layers.iter_mut().rev() {
            grad = layer.backward(&grad)?;
        }

        // Update statistics
        self.stats.backward_passes += 1;

        Ok(grad)
    }

    /// Train the quantum ML model
    pub fn train(
        &mut self,
        training_data: &[(Array1<f64>, Array1<f64>)],
        validation_data: Option<&[(Array1<f64>, Array1<f64>)]>,
    ) -> Result<QMLTrainingResult> {
        let mut best_validation_loss = f64::INFINITY;
        let mut patience_counter = 0;
        let mut training_metrics = Vec::new();

        let training_start = std::time::Instant::now();

        for epoch in 0..self.config.training_config.epochs {
            let epoch_start = std::time::Instant::now();

            // Training phase
            let mut epoch_loss = 0.0;
            let mut num_batches = 0;

            for batch in training_data.chunks(self.config.training_config.batch_size) {
                let batch_loss = self.train_batch(batch)?;
                epoch_loss += batch_loss;
                num_batches += 1;
            }

            epoch_loss /= f64::from(num_batches);

            // Validation phase
            let validation_loss = if let Some(val_data) = validation_data {
                self.evaluate(val_data)?
            } else {
                epoch_loss
            };

            let epoch_time = epoch_start.elapsed();

            let metrics = QMLEpochMetrics {
                epoch,
                training_loss: epoch_loss,
                validation_loss,
                epoch_time,
                learning_rate: self.get_current_learning_rate(epoch),
            };

            training_metrics.push(metrics.clone());

            // Early stopping check
            if self.config.training_config.early_stopping.enabled {
                if validation_loss
                    < best_validation_loss - self.config.training_config.early_stopping.min_delta
                {
                    best_validation_loss = validation_loss;
                    patience_counter = 0;
                } else {
                    patience_counter += 1;
                    if patience_counter >= self.config.training_config.early_stopping.patience {
                        println!("Early stopping triggered at epoch {epoch}");
                        break;
                    }
                }
            }

            // Update learning rate
            self.update_learning_rate(epoch, validation_loss);

            // Print progress
            if epoch % 10 == 0 {
                println!(
                    "Epoch {}: train_loss={:.6}, val_loss={:.6}, time={:.2}s",
                    epoch,
                    epoch_loss,
                    validation_loss,
                    epoch_time.as_secs_f64()
                );
            }
        }

        let total_training_time = training_start.elapsed();

        let result = QMLTrainingResult {
            final_training_loss: training_metrics.last().map_or(0.0, |m| m.training_loss),
            final_validation_loss: training_metrics.last().map_or(0.0, |m| m.validation_loss),
            best_validation_loss,
            epochs_trained: training_metrics.len(),
            total_training_time,
            training_metrics,
            quantum_advantage_metrics: self.compute_quantum_advantage_metrics()?,
        };

        self.training_history.push(result.clone());

        Ok(result)
    }

    /// Train a single batch
    fn train_batch(&mut self, batch: &[(Array1<f64>, Array1<f64>)]) -> Result<f64> {
        let mut total_loss = 0.0;
        let mut total_gradients: Vec<Array1<f64>> =
            (0..self.layers.len()).map(|_| Array1::zeros(0)).collect();

        for (input, target) in batch {
            // Forward pass
            let prediction = self.forward(input)?;

            // Compute loss
            let loss = Self::compute_loss(&prediction, target)?;
            total_loss += loss;

            // Compute loss gradient
            let loss_gradient = Self::compute_loss_gradient(&prediction, target)?;

            // Backward pass
            let gradients = self.compute_gradients(&loss_gradient)?;

            // Accumulate gradients
            for (i, grad) in gradients.iter().enumerate() {
                if total_gradients[i].is_empty() {
                    total_gradients[i] = grad.clone();
                } else {
                    total_gradients[i] += grad;
                }
            }
        }

        // Average gradients
        let batch_size = batch.len() as f64;
        for grad in &mut total_gradients {
            *grad /= batch_size;
        }

        // Apply gradients
        self.apply_gradients(&total_gradients)?;

        Ok(total_loss / batch_size)
    }

    /// Evaluate the model on validation data
    pub fn evaluate(&mut self, data: &[(Array1<f64>, Array1<f64>)]) -> Result<f64> {
        let mut total_loss = 0.0;

        for (input, target) in data {
            let prediction = self.forward(input)?;
            let loss = Self::compute_loss(&prediction, target)?;
            total_loss += loss;
        }

        Ok(total_loss / data.len() as f64)
    }

    /// Encode classical input into quantum state
    fn encode_input(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        match self.config.classical_preprocessing.encoding_method {
            DataEncodingMethod::Amplitude => self.encode_amplitude(input),
            DataEncodingMethod::Angle => self.encode_angle(input),
            DataEncodingMethod::Basis => self.encode_basis(input),
            DataEncodingMethod::QuantumFeatureMap => self.encode_quantum_feature_map(input),
            _ => Err(SimulatorError::InvalidConfiguration(
                "Encoding method not implemented".to_string(),
            )),
        }
    }

    /// Amplitude encoding
    fn encode_amplitude(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut state = Array1::zeros(state_size);

        // Normalize input
        let norm = input.iter().map(|x| x * x).sum::<f64>().sqrt();
        if norm == 0.0 {
            return Err(SimulatorError::InvalidState("Zero input norm".to_string()));
        }

        // Encode input as amplitudes
        for (i, &val) in input.iter().enumerate() {
            if i < state_size {
                state[i] = Complex64::new(val / norm, 0.0);
            }
        }

        Ok(state)
    }

    /// Angle encoding
    fn encode_angle(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut state = Array1::zeros(state_size);

        // Initialize |0...0⟩ state
        state[0] = Complex64::new(1.0, 0.0);

        // Apply rotation gates based on input values
        for (i, &angle) in input.iter().enumerate() {
            if i < n_qubits {
                // Apply RY rotation to qubit i
                state = self.apply_ry_rotation(&state, i, angle)?;
            }
        }

        Ok(state)
    }

    /// Basis encoding
    fn encode_basis(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut state = Array1::zeros(state_size);

        // Convert input to binary representation
        let mut binary_index = 0;
        for (i, &val) in input.iter().enumerate() {
            if i < n_qubits && val > 0.5 {
                binary_index |= 1 << i;
            }
        }

        state[binary_index] = Complex64::new(1.0, 0.0);

        Ok(state)
    }

    /// Quantum feature map encoding
    fn encode_quantum_feature_map(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut state = Array1::zeros(state_size);

        // Initialize |+⟩^⊗n state (all qubits in superposition)
        let hadamard_coeff = 1.0 / (n_qubits as f64 / 2.0).exp2();
        for i in 0..state_size {
            state[i] = Complex64::new(hadamard_coeff, 0.0);
        }

        // Apply feature map rotations
        for (i, &feature) in input.iter().enumerate() {
            if i < n_qubits {
                // Apply Z rotation based on feature value
                state = self.apply_rz_rotation(&state, i, feature * PI)?;
            }
        }

        // Apply entangling gates for feature interactions
        for i in 0..(n_qubits - 1) {
            if i + 1 < input.len() {
                let interaction = input[i] * input[i + 1];
                state = self.apply_cnot_interaction(&state, i, i + 1, interaction * PI)?;
            }
        }

        Ok(state)
    }

    /// Apply RY rotation to a specific qubit
    fn apply_ry_rotation(
        &self,
        state: &Array1<Complex64>,
        qubit: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut new_state = state.clone();

        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        for i in 0..state_size {
            if i & (1 << qubit) == 0 {
                // |0⟩ component
                let j = i | (1 << qubit); // corresponding |1⟩ state
                if j < state_size {
                    let state_0 = state[i];
                    let state_1 = state[j];

                    new_state[i] = Complex64::new(cos_half, 0.0) * state_0
                        - Complex64::new(sin_half, 0.0) * state_1;
                    new_state[j] = Complex64::new(sin_half, 0.0) * state_0
                        + Complex64::new(cos_half, 0.0) * state_1;
                }
            }
        }

        Ok(new_state)
    }

    /// Apply RZ rotation to a specific qubit
    fn apply_rz_rotation(
        &self,
        state: &Array1<Complex64>,
        qubit: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut new_state = state.clone();

        let phase_0 = Complex64::from_polar(1.0, -angle / 2.0);
        let phase_1 = Complex64::from_polar(1.0, angle / 2.0);

        for i in 0..state_size {
            if i & (1 << qubit) == 0 {
                new_state[i] *= phase_0;
            } else {
                new_state[i] *= phase_1;
            }
        }

        Ok(new_state)
    }

    /// Apply CNOT with interaction term
    fn apply_cnot_interaction(
        &self,
        state: &Array1<Complex64>,
        control: usize,
        target: usize,
        interaction: f64,
    ) -> Result<Array1<Complex64>> {
        let n_qubits = self.config.num_qubits;
        let state_size = 1 << n_qubits;
        let mut new_state = state.clone();

        // Apply interaction-dependent phase
        let phase = Complex64::from_polar(1.0, interaction);

        for i in 0..state_size {
            if (i & (1 << control)) != 0 && (i & (1 << target)) != 0 {
                // Both control and target are |1⟩
                new_state[i] *= phase;
            }
        }

        Ok(new_state)
    }

    /// Decode quantum state to classical output
    fn decode_output(&self, state: &Array1<Complex64>) -> Result<Array1<f64>> {
        // For now, use expectation values of Pauli-Z measurements
        let n_qubits = self.config.num_qubits;
        let mut output = Array1::zeros(n_qubits);

        for qubit in 0..n_qubits {
            let expectation = Self::measure_pauli_z_expectation(state, qubit)?;
            output[qubit] = expectation;
        }

        Ok(output)
    }

    /// Measure Pauli-Z expectation value for a specific qubit
    fn measure_pauli_z_expectation(state: &Array1<Complex64>, qubit: usize) -> Result<f64> {
        let state_size = state.len();
        let mut expectation = 0.0;

        for i in 0..state_size {
            let probability = state[i].norm_sqr();
            if i & (1 << qubit) == 0 {
                expectation += probability; // |0⟩ contributes +1
            } else {
                expectation -= probability; // |1⟩ contributes -1
            }
        }

        Ok(expectation)
    }

    /// Compute loss function
    fn compute_loss(prediction: &Array1<f64>, target: &Array1<f64>) -> Result<f64> {
        // Check shape compatibility
        if prediction.shape() != target.shape() {
            return Err(SimulatorError::InvalidInput(format!(
                "Shape mismatch: prediction shape {:?} != target shape {:?}",
                prediction.shape(),
                target.shape()
            )));
        }

        // Mean squared error
        let diff = prediction - target;
        let mse = diff.iter().map(|x| x * x).sum::<f64>() / diff.len() as f64;
        Ok(mse)
    }

    /// Compute loss gradient
    fn compute_loss_gradient(
        prediction: &Array1<f64>,
        target: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Gradient of MSE
        let diff = prediction - target;
        let grad = 2.0 * &diff / diff.len() as f64;
        Ok(grad)
    }

    /// Compute gradients using parameter-shift rule
    fn compute_gradients(&mut self, loss_gradient: &Array1<f64>) -> Result<Vec<Array1<f64>>> {
        let mut gradients = Vec::new();

        for layer_idx in 0..self.layers.len() {
            let layer_gradient = match self.config.training_config.gradient_method {
                GradientMethod::ParameterShift => {
                    self.compute_parameter_shift_gradient(layer_idx, loss_gradient)?
                }
                GradientMethod::FiniteDifference => {
                    self.compute_finite_difference_gradient(layer_idx, loss_gradient)?
                }
                _ => {
                    return Err(SimulatorError::InvalidConfiguration(
                        "Gradient method not implemented".to_string(),
                    ))
                }
            };
            gradients.push(layer_gradient);
        }

        Ok(gradients)
    }

    /// Compute gradients using parameter-shift rule
    fn compute_parameter_shift_gradient(
        &mut self,
        layer_idx: usize,
        loss_gradient: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        let layer = &self.layers[layer_idx];
        let parameters = layer.get_parameters();
        let mut gradient = Array1::zeros(parameters.len());

        let shift = PI / 2.0; // Parameter shift amount

        for (param_idx, &param_val) in parameters.iter().enumerate() {
            // Forward shift
            let mut params_plus = parameters.clone();
            params_plus[param_idx] = param_val + shift;
            self.layers[layer_idx].set_parameters(&params_plus);
            let output_plus = self.forward_layer(layer_idx, loss_gradient)?;

            // Backward shift
            let mut params_minus = parameters.clone();
            params_minus[param_idx] = param_val - shift;
            self.layers[layer_idx].set_parameters(&params_minus);
            let output_minus = self.forward_layer(layer_idx, loss_gradient)?;

            // Compute gradient
            gradient[param_idx] = (output_plus.sum() - output_minus.sum()) / 2.0;

            // Restore original parameters
            self.layers[layer_idx].set_parameters(&parameters);
        }

        Ok(gradient)
    }

    /// Compute gradients using finite differences
    fn compute_finite_difference_gradient(
        &mut self,
        layer_idx: usize,
        loss_gradient: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        let layer = &self.layers[layer_idx];
        let parameters = layer.get_parameters();
        let mut gradient = Array1::zeros(parameters.len());

        let eps = 1e-6; // Small perturbation

        for (param_idx, &param_val) in parameters.iter().enumerate() {
            // Forward perturbation
            let mut params_plus = parameters.clone();
            params_plus[param_idx] = param_val + eps;
            self.layers[layer_idx].set_parameters(&params_plus);
            let output_plus = self.forward_layer(layer_idx, loss_gradient)?;

            // Backward perturbation
            let mut params_minus = parameters.clone();
            params_minus[param_idx] = param_val - eps;
            self.layers[layer_idx].set_parameters(&params_minus);
            let output_minus = self.forward_layer(layer_idx, loss_gradient)?;

            // Compute gradient
            gradient[param_idx] = (output_plus.sum() - output_minus.sum()) / (2.0 * eps);

            // Restore original parameters
            self.layers[layer_idx].set_parameters(&parameters);
        }

        Ok(gradient)
    }

    /// Forward pass through a specific layer
    fn forward_layer(&mut self, layer_idx: usize, input: &Array1<f64>) -> Result<Array1<f64>> {
        // This is a simplified version - in practice, we'd need to track intermediate states
        self.forward(input)
    }

    /// Apply gradients to update parameters
    fn apply_gradients(&mut self, gradients: &[Array1<f64>]) -> Result<()> {
        for (layer_idx, gradient) in gradients.iter().enumerate() {
            let layer = &mut self.layers[layer_idx];
            let mut parameters = layer.get_parameters();

            // Apply gradient update based on optimizer
            match self.config.training_config.optimizer {
                OptimizerType::SGD => {
                    for (param, grad) in parameters.iter_mut().zip(gradient.iter()) {
                        *param -= self.config.training_config.learning_rate * grad;
                    }
                }
                OptimizerType::Adam => {
                    // Simplified Adam update (would need to track momentum terms)
                    for (param, grad) in parameters.iter_mut().zip(gradient.iter()) {
                        *param -= self.config.training_config.learning_rate * grad;
                    }
                }
                _ => {
                    // Default to SGD
                    for (param, grad) in parameters.iter_mut().zip(gradient.iter()) {
                        *param -= self.config.training_config.learning_rate * grad;
                    }
                }
            }

            // Apply parameter constraints
            if let Some((min_val, max_val)) =
                self.config.training_config.regularization.parameter_bounds
            {
                for param in &mut parameters {
                    *param = param.clamp(min_val, max_val);
                }
            }

            layer.set_parameters(&parameters);
        }

        Ok(())
    }

    /// Get current learning rate (with scheduling)
    fn get_current_learning_rate(&self, epoch: usize) -> f64 {
        let base_lr = self.config.training_config.learning_rate;

        match self.config.training_config.lr_schedule {
            LearningRateSchedule::Constant => base_lr,
            LearningRateSchedule::ExponentialDecay => base_lr * 0.95_f64.powi(epoch as i32),
            LearningRateSchedule::StepDecay => {
                if epoch % 50 == 0 && epoch > 0 {
                    base_lr * 0.5_f64.powi((epoch / 50) as i32)
                } else {
                    base_lr
                }
            }
            LearningRateSchedule::CosineAnnealing => {
                let progress = epoch as f64 / self.config.training_config.epochs as f64;
                base_lr * 0.5 * (1.0 + (PI * progress).cos())
            }
            _ => base_lr,
        }
    }

    /// Update learning rate
    fn update_learning_rate(&mut self, epoch: usize, validation_loss: f64) {
        // This would update internal optimizer state for learning rate scheduling
        // For now, just track the current learning rate
        let current_lr = self.get_current_learning_rate(epoch);
        self.training_state.current_learning_rate = current_lr;
    }

    /// Compute quantum advantage metrics
    fn compute_quantum_advantage_metrics(&self) -> Result<QuantumAdvantageMetrics> {
        // Placeholder for quantum advantage analysis
        Ok(QuantumAdvantageMetrics {
            quantum_volume: 0.0,
            classical_simulation_cost: 0.0,
            quantum_speedup_factor: 1.0,
            circuit_depth: self.layers.iter().map(|l| l.get_depth()).sum(),
            gate_count: self.layers.iter().map(|l| l.get_gate_count()).sum(),
            entanglement_measure: 0.0,
        })
    }

    /// Get training statistics
    #[must_use]
    pub const fn get_stats(&self) -> &QMLStats {
        &self.stats
    }

    /// Get training history
    #[must_use]
    pub fn get_training_history(&self) -> &[QMLTrainingResult] {
        &self.training_history
    }

    /// Get layers reference
    #[must_use]
    pub fn get_layers(&self) -> &[Box<dyn QMLLayer>] {
        &self.layers
    }

    /// Get config reference
    #[must_use]
    pub const fn get_config(&self) -> &QMLConfig {
        &self.config
    }

    /// Encode amplitude (public version)
    pub fn encode_amplitude_public(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        self.encode_amplitude(input)
    }

    /// Encode angle (public version)
    pub fn encode_angle_public(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        self.encode_angle(input)
    }

    /// Encode basis (public version)
    pub fn encode_basis_public(&self, input: &Array1<f64>) -> Result<Array1<Complex64>> {
        self.encode_basis(input)
    }

    /// Encode quantum feature map (public version)
    pub fn encode_quantum_feature_map_public(
        &self,
        input: &Array1<f64>,
    ) -> Result<Array1<Complex64>> {
        self.encode_quantum_feature_map(input)
    }

    /// Measure Pauli Z expectation (public version)
    pub fn measure_pauli_z_expectation_public(
        &self,
        state: &Array1<Complex64>,
        qubit: usize,
    ) -> Result<f64> {
        Self::measure_pauli_z_expectation(state, qubit)
    }

    /// Get current learning rate (public version)
    #[must_use]
    pub fn get_current_learning_rate_public(&self, epoch: usize) -> f64 {
        self.get_current_learning_rate(epoch)
    }

    /// Compute loss (public version)
    pub fn compute_loss_public(
        &self,
        prediction: &Array1<f64>,
        target: &Array1<f64>,
    ) -> Result<f64> {
        Self::compute_loss(prediction, target)
    }

    /// Compute loss gradient (public version)
    pub fn compute_loss_gradient_public(
        &self,
        prediction: &Array1<f64>,
        target: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        Self::compute_loss_gradient(prediction, target)
    }
}

/// Trait for QML layers
pub trait QMLLayer: std::fmt::Debug + Send + Sync {
    /// Forward pass through the layer
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>>;

    /// Backward pass through the layer
    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>>;

    /// Get layer parameters
    fn get_parameters(&self) -> Array1<f64>;

    /// Set layer parameters
    fn set_parameters(&mut self, parameters: &Array1<f64>);

    /// Get circuit depth
    fn get_depth(&self) -> usize;

    /// Get gate count
    fn get_gate_count(&self) -> usize;

    /// Get number of parameters
    fn get_num_parameters(&self) -> usize;
}

/// Parameterized Quantum Circuit Layer
#[derive(Debug)]
pub struct ParameterizedQuantumCircuitLayer {
    /// Number of qubits
    num_qubits: usize,
    /// Layer configuration
    config: QMLLayerConfig,
    /// Parameters (rotation angles)
    parameters: Array1<f64>,
    /// Circuit structure
    circuit_structure: Vec<PQCGate>,
    /// Internal state vector simulator
    simulator: StateVectorSimulator,
}

impl ParameterizedQuantumCircuitLayer {
    /// Create new PQC layer
    pub fn new(num_qubits: usize, config: QMLLayerConfig) -> Result<Self> {
        let mut layer = Self {
            num_qubits,
            config: config.clone(),
            parameters: Array1::zeros(config.num_parameters),
            circuit_structure: Vec::new(),
            simulator: StateVectorSimulator::new(),
        };

        // Initialize parameters randomly
        layer.initialize_parameters();

        // Build circuit structure
        layer.build_circuit_structure()?;

        Ok(layer)
    }

    /// Initialize parameters randomly
    fn initialize_parameters(&mut self) {
        let mut rng = thread_rng();
        for param in &mut self.parameters {
            *param = rng.gen_range(-PI..PI);
        }
    }

    /// Build circuit structure based on ansatz
    fn build_circuit_structure(&mut self) -> Result<()> {
        match self.config.ansatz_type {
            AnsatzType::Hardware => self.build_hardware_efficient_ansatz(),
            AnsatzType::Layered => self.build_layered_ansatz(),
            AnsatzType::BrickWall => self.build_brick_wall_ansatz(),
            _ => Err(SimulatorError::InvalidConfiguration(
                "Ansatz type not implemented".to_string(),
            )),
        }
    }

    /// Build hardware-efficient ansatz
    fn build_hardware_efficient_ansatz(&mut self) -> Result<()> {
        let mut param_idx = 0;

        for layer in 0..self.config.depth {
            // Single-qubit rotations
            for qubit in 0..self.num_qubits {
                for &gate_type in &self.config.rotation_gates {
                    if param_idx < self.parameters.len() {
                        self.circuit_structure.push(PQCGate {
                            gate_type: PQCGateType::SingleQubit(gate_type),
                            qubits: vec![qubit],
                            parameter_index: Some(param_idx),
                        });
                        param_idx += 1;
                    }
                }
            }

            // Entangling gates
            self.add_entangling_gates(&param_idx);
        }

        Ok(())
    }

    /// Build layered ansatz
    fn build_layered_ansatz(&mut self) -> Result<()> {
        // Similar to hardware-efficient but with different structure
        self.build_hardware_efficient_ansatz()
    }

    /// Build brick-wall ansatz
    fn build_brick_wall_ansatz(&mut self) -> Result<()> {
        let mut param_idx = 0;

        for layer in 0..self.config.depth {
            // Alternating CNOT pattern (brick-wall)
            let offset = layer % 2;
            for i in (offset..self.num_qubits - 1).step_by(2) {
                self.circuit_structure.push(PQCGate {
                    gate_type: PQCGateType::TwoQubit(TwoQubitGate::CNOT),
                    qubits: vec![i, i + 1],
                    parameter_index: None,
                });
            }

            // Single-qubit rotations
            for qubit in 0..self.num_qubits {
                if param_idx < self.parameters.len() {
                    self.circuit_structure.push(PQCGate {
                        gate_type: PQCGateType::SingleQubit(RotationGate::RY),
                        qubits: vec![qubit],
                        parameter_index: Some(param_idx),
                    });
                    param_idx += 1;
                }
            }
        }

        Ok(())
    }

    /// Add entangling gates based on entanglement pattern
    fn add_entangling_gates(&mut self, param_idx: &usize) {
        match self.config.entanglement_pattern {
            EntanglementPattern::Linear => {
                for i in 0..(self.num_qubits - 1) {
                    self.circuit_structure.push(PQCGate {
                        gate_type: PQCGateType::TwoQubit(TwoQubitGate::CNOT),
                        qubits: vec![i, i + 1],
                        parameter_index: None,
                    });
                }
            }
            EntanglementPattern::Circular => {
                for i in 0..self.num_qubits {
                    let next = (i + 1) % self.num_qubits;
                    self.circuit_structure.push(PQCGate {
                        gate_type: PQCGateType::TwoQubit(TwoQubitGate::CNOT),
                        qubits: vec![i, next],
                        parameter_index: None,
                    });
                }
            }
            EntanglementPattern::AllToAll => {
                for i in 0..self.num_qubits {
                    for j in (i + 1)..self.num_qubits {
                        self.circuit_structure.push(PQCGate {
                            gate_type: PQCGateType::TwoQubit(TwoQubitGate::CNOT),
                            qubits: vec![i, j],
                            parameter_index: None,
                        });
                    }
                }
            }
            _ => {
                // Default to linear
                for i in 0..(self.num_qubits - 1) {
                    self.circuit_structure.push(PQCGate {
                        gate_type: PQCGateType::TwoQubit(TwoQubitGate::CNOT),
                        qubits: vec![i, i + 1],
                        parameter_index: None,
                    });
                }
            }
        }
    }
}

impl QMLLayer for ParameterizedQuantumCircuitLayer {
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>> {
        let mut state = input.clone();

        // Apply each gate in the circuit
        for gate in &self.circuit_structure {
            state = self.apply_gate(&state, gate)?;
        }

        Ok(state)
    }

    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>> {
        // Simplified backward pass - in practice would use automatic differentiation
        Ok(gradient.clone())
    }

    fn get_parameters(&self) -> Array1<f64> {
        self.parameters.clone()
    }

    fn set_parameters(&mut self, parameters: &Array1<f64>) {
        self.parameters = parameters.clone();
    }

    fn get_depth(&self) -> usize {
        self.config.depth
    }

    fn get_gate_count(&self) -> usize {
        self.circuit_structure.len()
    }

    fn get_num_parameters(&self) -> usize {
        self.parameters.len()
    }
}

impl ParameterizedQuantumCircuitLayer {
    /// Apply a single gate to the quantum state
    fn apply_gate(&self, state: &Array1<Complex64>, gate: &PQCGate) -> Result<Array1<Complex64>> {
        match &gate.gate_type {
            PQCGateType::SingleQubit(rotation_gate) => {
                let angle = if let Some(param_idx) = gate.parameter_index {
                    self.parameters[param_idx]
                } else {
                    0.0
                };
                Self::apply_single_qubit_gate(state, gate.qubits[0], *rotation_gate, angle)
            }
            PQCGateType::TwoQubit(two_qubit_gate) => {
                Self::apply_two_qubit_gate(state, gate.qubits[0], gate.qubits[1], *two_qubit_gate)
            }
        }
    }

    /// Apply single-qubit rotation gate
    fn apply_single_qubit_gate(
        state: &Array1<Complex64>,
        qubit: usize,
        gate_type: RotationGate,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = Array1::zeros(state_size);

        match gate_type {
            RotationGate::RX => {
                let cos_half = (angle / 2.0).cos();
                let sin_half = (angle / 2.0).sin();

                for i in 0..state_size {
                    if i & (1 << qubit) == 0 {
                        let j = i | (1 << qubit);
                        if j < state_size {
                            new_state[i] = Complex64::new(cos_half, 0.0) * state[i]
                                + Complex64::new(0.0, -sin_half) * state[j];
                            new_state[j] = Complex64::new(0.0, -sin_half) * state[i]
                                + Complex64::new(cos_half, 0.0) * state[j];
                        }
                    }
                }
            }
            RotationGate::RY => {
                let cos_half = (angle / 2.0).cos();
                let sin_half = (angle / 2.0).sin();

                for i in 0..state_size {
                    if i & (1 << qubit) == 0 {
                        let j = i | (1 << qubit);
                        if j < state_size {
                            new_state[i] = Complex64::new(cos_half, 0.0) * state[i]
                                - Complex64::new(sin_half, 0.0) * state[j];
                            new_state[j] = Complex64::new(sin_half, 0.0) * state[i]
                                + Complex64::new(cos_half, 0.0) * state[j];
                        }
                    }
                }
            }
            RotationGate::RZ => {
                let phase_0 = Complex64::from_polar(1.0, -angle / 2.0);
                let phase_1 = Complex64::from_polar(1.0, angle / 2.0);

                for i in 0..state_size {
                    if i & (1 << qubit) == 0 {
                        new_state[i] = phase_0 * state[i];
                    } else {
                        new_state[i] = phase_1 * state[i];
                    }
                }
            }
            _ => {
                return Err(SimulatorError::InvalidGate(
                    "Gate type not implemented".to_string(),
                ))
            }
        }

        Ok(new_state)
    }

    /// Apply two-qubit gate
    fn apply_two_qubit_gate(
        state: &Array1<Complex64>,
        control: usize,
        target: usize,
        gate_type: TwoQubitGate,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = state.clone();

        match gate_type {
            TwoQubitGate::CNOT => {
                for i in 0..state_size {
                    if (i & (1 << control)) != 0 {
                        // Control qubit is |1⟩, flip target
                        let j = i ^ (1 << target);
                        new_state[i] = state[j];
                    }
                }
            }
            TwoQubitGate::CZ => {
                for i in 0..state_size {
                    if (i & (1 << control)) != 0 && (i & (1 << target)) != 0 {
                        // Both qubits are |1⟩, apply phase
                        new_state[i] = -state[i];
                    }
                }
            }
            TwoQubitGate::SWAP => {
                for i in 0..state_size {
                    let ctrl_bit = (i & (1 << control)) != 0;
                    let targ_bit = (i & (1 << target)) != 0;
                    if ctrl_bit != targ_bit {
                        // Swap the qubits
                        let j = i ^ (1 << control) ^ (1 << target);
                        new_state[i] = state[j];
                    }
                }
            }
            TwoQubitGate::CPhase => {
                for i in 0..state_size {
                    if (i & (1 << control)) != 0 && (i & (1 << target)) != 0 {
                        // Both qubits are |1⟩, apply phase (similar to CZ)
                        new_state[i] = -state[i];
                    }
                }
            }
        }

        Ok(new_state)
    }
}

/// Quantum Convolutional Layer
#[derive(Debug)]
pub struct QuantumConvolutionalLayer {
    /// Number of qubits
    num_qubits: usize,
    /// Layer configuration
    config: QMLLayerConfig,
    /// Parameters
    parameters: Array1<f64>,
    /// Convolutional structure
    conv_structure: Vec<ConvolutionalFilter>,
}

impl QuantumConvolutionalLayer {
    /// Create new quantum convolutional layer
    pub fn new(num_qubits: usize, config: QMLLayerConfig) -> Result<Self> {
        let mut layer = Self {
            num_qubits,
            config: config.clone(),
            parameters: Array1::zeros(config.num_parameters),
            conv_structure: Vec::new(),
        };

        layer.initialize_parameters();
        layer.build_convolutional_structure()?;

        Ok(layer)
    }

    /// Initialize parameters
    fn initialize_parameters(&mut self) {
        let mut rng = thread_rng();
        for param in &mut self.parameters {
            *param = rng.gen_range(-PI..PI);
        }
    }

    /// Build convolutional structure
    fn build_convolutional_structure(&mut self) -> Result<()> {
        // Create sliding window filters
        let filter_size = 2; // 2-qubit filters
        let stride = 1;

        let mut param_idx = 0;
        for start in (0..=(self.num_qubits - filter_size)).step_by(stride) {
            if param_idx + 2 <= self.parameters.len() {
                self.conv_structure.push(ConvolutionalFilter {
                    qubits: vec![start, start + 1],
                    parameter_indices: vec![param_idx, param_idx + 1],
                });
                param_idx += 2;
            }
        }

        Ok(())
    }
}

impl QMLLayer for QuantumConvolutionalLayer {
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>> {
        let mut state = input.clone();

        // Apply convolutional filters
        for filter in &self.conv_structure {
            state = self.apply_convolutional_filter(&state, filter)?;
        }

        Ok(state)
    }

    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>> {
        Ok(gradient.clone())
    }

    fn get_parameters(&self) -> Array1<f64> {
        self.parameters.clone()
    }

    fn set_parameters(&mut self, parameters: &Array1<f64>) {
        self.parameters = parameters.clone();
    }

    fn get_depth(&self) -> usize {
        self.conv_structure.len()
    }

    fn get_gate_count(&self) -> usize {
        self.conv_structure.len() * 4 // Approximate gates per filter
    }

    fn get_num_parameters(&self) -> usize {
        self.parameters.len()
    }
}

impl QuantumConvolutionalLayer {
    /// Apply convolutional filter
    fn apply_convolutional_filter(
        &self,
        state: &Array1<Complex64>,
        filter: &ConvolutionalFilter,
    ) -> Result<Array1<Complex64>> {
        let mut new_state = state.clone();

        // Apply parameterized two-qubit unitaries
        let param1 = self.parameters[filter.parameter_indices[0]];
        let param2 = self.parameters[filter.parameter_indices[1]];

        // Apply RY rotations followed by CNOT
        new_state = Self::apply_ry_to_state(&new_state, filter.qubits[0], param1)?;
        new_state = Self::apply_ry_to_state(&new_state, filter.qubits[1], param2)?;
        new_state = Self::apply_cnot_to_state(&new_state, filter.qubits[0], filter.qubits[1])?;

        Ok(new_state)
    }

    /// Apply RY rotation to state
    fn apply_ry_to_state(
        state: &Array1<Complex64>,
        qubit: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = Array1::zeros(state_size);

        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        for i in 0..state_size {
            if i & (1 << qubit) == 0 {
                let j = i | (1 << qubit);
                if j < state_size {
                    new_state[i] = Complex64::new(cos_half, 0.0) * state[i]
                        - Complex64::new(sin_half, 0.0) * state[j];
                    new_state[j] = Complex64::new(sin_half, 0.0) * state[i]
                        + Complex64::new(cos_half, 0.0) * state[j];
                }
            }
        }

        Ok(new_state)
    }

    /// Apply CNOT to state
    fn apply_cnot_to_state(
        state: &Array1<Complex64>,
        control: usize,
        target: usize,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = state.clone();

        for i in 0..state_size {
            if (i & (1 << control)) != 0 {
                let j = i ^ (1 << target);
                new_state[i] = state[j];
            }
        }

        Ok(new_state)
    }
}

/// Quantum Dense Layer (fully connected)
#[derive(Debug)]
pub struct QuantumDenseLayer {
    /// Number of qubits
    num_qubits: usize,
    /// Layer configuration
    config: QMLLayerConfig,
    /// Parameters
    parameters: Array1<f64>,
    /// Dense layer structure
    dense_structure: Vec<DenseConnection>,
}

impl QuantumDenseLayer {
    /// Create new quantum dense layer
    pub fn new(num_qubits: usize, config: QMLLayerConfig) -> Result<Self> {
        let mut layer = Self {
            num_qubits,
            config: config.clone(),
            parameters: Array1::zeros(config.num_parameters),
            dense_structure: Vec::new(),
        };

        layer.initialize_parameters();
        layer.build_dense_structure()?;

        Ok(layer)
    }

    /// Initialize parameters
    fn initialize_parameters(&mut self) {
        let mut rng = thread_rng();
        for param in &mut self.parameters {
            *param = rng.gen_range(-PI..PI);
        }
    }

    /// Build dense layer structure (all-to-all connectivity)
    fn build_dense_structure(&mut self) -> Result<()> {
        let mut param_idx = 0;

        // Create all-to-all connections
        for i in 0..self.num_qubits {
            for j in (i + 1)..self.num_qubits {
                if param_idx < self.parameters.len() {
                    self.dense_structure.push(DenseConnection {
                        qubit1: i,
                        qubit2: j,
                        parameter_index: param_idx,
                    });
                    param_idx += 1;
                }
            }
        }

        Ok(())
    }
}

impl QMLLayer for QuantumDenseLayer {
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>> {
        let mut state = input.clone();

        // Apply dense connections
        for connection in &self.dense_structure {
            state = self.apply_dense_connection(&state, connection)?;
        }

        Ok(state)
    }

    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>> {
        Ok(gradient.clone())
    }

    fn get_parameters(&self) -> Array1<f64> {
        self.parameters.clone()
    }

    fn set_parameters(&mut self, parameters: &Array1<f64>) {
        self.parameters = parameters.clone();
    }

    fn get_depth(&self) -> usize {
        1 // Dense layer is typically single depth
    }

    fn get_gate_count(&self) -> usize {
        self.dense_structure.len() * 2 // Approximate gates per connection
    }

    fn get_num_parameters(&self) -> usize {
        self.parameters.len()
    }
}

impl QuantumDenseLayer {
    /// Apply dense connection (parameterized two-qubit gate)
    fn apply_dense_connection(
        &self,
        state: &Array1<Complex64>,
        connection: &DenseConnection,
    ) -> Result<Array1<Complex64>> {
        let angle = self.parameters[connection.parameter_index];

        // Apply parameterized two-qubit rotation
        Self::apply_parameterized_two_qubit_gate(state, connection.qubit1, connection.qubit2, angle)
    }

    /// Apply parameterized two-qubit gate
    fn apply_parameterized_two_qubit_gate(
        state: &Array1<Complex64>,
        qubit1: usize,
        qubit2: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = state.clone();

        // Apply controlled rotation
        let cos_val = angle.cos();
        let sin_val = angle.sin();

        for i in 0..state_size {
            if (i & (1 << qubit1)) != 0 && (i & (1 << qubit2)) != 0 {
                // Both qubits are |1⟩
                let phase = Complex64::new(cos_val, sin_val);
                new_state[i] *= phase;
            }
        }

        Ok(new_state)
    }
}

/// Quantum LSTM Layer
#[derive(Debug)]
pub struct QuantumLSTMLayer {
    /// Number of qubits
    num_qubits: usize,
    /// Layer configuration
    config: QMLLayerConfig,
    /// Parameters
    parameters: Array1<f64>,
    /// LSTM gates
    lstm_gates: Vec<LSTMGate>,
    /// Hidden state
    hidden_state: Option<Array1<Complex64>>,
    /// Cell state
    cell_state: Option<Array1<Complex64>>,
}

impl QuantumLSTMLayer {
    /// Create new quantum LSTM layer
    pub fn new(num_qubits: usize, config: QMLLayerConfig) -> Result<Self> {
        let mut layer = Self {
            num_qubits,
            config: config.clone(),
            parameters: Array1::zeros(config.num_parameters),
            lstm_gates: Vec::new(),
            hidden_state: None,
            cell_state: None,
        };

        layer.initialize_parameters();
        layer.build_lstm_structure()?;

        Ok(layer)
    }

    /// Initialize parameters
    fn initialize_parameters(&mut self) {
        let mut rng = thread_rng();
        for param in &mut self.parameters {
            *param = rng.gen_range(-PI..PI);
        }
    }

    /// Build LSTM structure
    fn build_lstm_structure(&mut self) -> Result<()> {
        let params_per_gate = self.parameters.len() / 4; // Forget, input, output, candidate gates

        self.lstm_gates = vec![
            LSTMGate {
                gate_type: LSTMGateType::Forget,
                parameter_start: 0,
                parameter_count: params_per_gate,
            },
            LSTMGate {
                gate_type: LSTMGateType::Input,
                parameter_start: params_per_gate,
                parameter_count: params_per_gate,
            },
            LSTMGate {
                gate_type: LSTMGateType::Output,
                parameter_start: 2 * params_per_gate,
                parameter_count: params_per_gate,
            },
            LSTMGate {
                gate_type: LSTMGateType::Candidate,
                parameter_start: 3 * params_per_gate,
                parameter_count: params_per_gate,
            },
        ];

        Ok(())
    }
}

impl QMLLayer for QuantumLSTMLayer {
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>> {
        // Initialize states if first time
        if self.hidden_state.is_none() {
            let state_size = 1 << self.num_qubits;
            let mut hidden = Array1::zeros(state_size);
            let mut cell = Array1::zeros(state_size);
            // Initialize with |0...0⟩ state
            hidden[0] = Complex64::new(1.0, 0.0);
            cell[0] = Complex64::new(1.0, 0.0);
            self.hidden_state = Some(hidden);
            self.cell_state = Some(cell);
        }

        let mut current_state = input.clone();

        // Apply LSTM gates
        for gate in &self.lstm_gates {
            current_state = self.apply_lstm_gate(&current_state, gate)?;
        }

        // Update internal states
        self.hidden_state = Some(current_state.clone());

        Ok(current_state)
    }

    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>> {
        Ok(gradient.clone())
    }

    fn get_parameters(&self) -> Array1<f64> {
        self.parameters.clone()
    }

    fn set_parameters(&mut self, parameters: &Array1<f64>) {
        self.parameters = parameters.clone();
    }

    fn get_depth(&self) -> usize {
        self.lstm_gates.len()
    }

    fn get_gate_count(&self) -> usize {
        self.parameters.len() // Each parameter corresponds roughly to one gate
    }

    fn get_num_parameters(&self) -> usize {
        self.parameters.len()
    }
}

impl QuantumLSTMLayer {
    /// Apply LSTM gate
    fn apply_lstm_gate(
        &self,
        state: &Array1<Complex64>,
        gate: &LSTMGate,
    ) -> Result<Array1<Complex64>> {
        let mut new_state = state.clone();

        // Apply parameterized unitaries based on gate parameters
        for i in 0..gate.parameter_count {
            let param_idx = gate.parameter_start + i;
            if param_idx < self.parameters.len() {
                let angle = self.parameters[param_idx];
                let qubit = i % self.num_qubits;

                // Apply rotation gate
                new_state = Self::apply_rotation(&new_state, qubit, angle)?;
            }
        }

        Ok(new_state)
    }

    /// Apply rotation gate
    fn apply_rotation(
        state: &Array1<Complex64>,
        qubit: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = Array1::zeros(state_size);

        let cos_half = (angle / 2.0).cos();
        let sin_half = (angle / 2.0).sin();

        for i in 0..state_size {
            if i & (1 << qubit) == 0 {
                let j = i | (1 << qubit);
                if j < state_size {
                    new_state[i] = Complex64::new(cos_half, 0.0) * state[i]
                        - Complex64::new(sin_half, 0.0) * state[j];
                    new_state[j] = Complex64::new(sin_half, 0.0) * state[i]
                        + Complex64::new(cos_half, 0.0) * state[j];
                }
            }
        }

        Ok(new_state)
    }

    /// Get LSTM gates reference
    #[must_use]
    pub fn get_lstm_gates(&self) -> &[LSTMGate] {
        &self.lstm_gates
    }
}

/// Quantum Attention Layer
#[derive(Debug)]
pub struct QuantumAttentionLayer {
    /// Number of qubits
    num_qubits: usize,
    /// Layer configuration
    config: QMLLayerConfig,
    /// Parameters
    parameters: Array1<f64>,
    /// Attention structure
    attention_structure: Vec<AttentionHead>,
}

impl QuantumAttentionLayer {
    /// Create new quantum attention layer
    pub fn new(num_qubits: usize, config: QMLLayerConfig) -> Result<Self> {
        let mut layer = Self {
            num_qubits,
            config: config.clone(),
            parameters: Array1::zeros(config.num_parameters),
            attention_structure: Vec::new(),
        };

        layer.initialize_parameters();
        layer.build_attention_structure()?;

        Ok(layer)
    }

    /// Initialize parameters
    fn initialize_parameters(&mut self) {
        let mut rng = thread_rng();
        for param in &mut self.parameters {
            *param = rng.gen_range(-PI..PI);
        }
    }

    /// Build attention structure
    fn build_attention_structure(&mut self) -> Result<()> {
        let num_heads = 2; // Multi-head attention
        let params_per_head = self.parameters.len() / num_heads;

        for head in 0..num_heads {
            self.attention_structure.push(AttentionHead {
                head_id: head,
                parameter_start: head * params_per_head,
                parameter_count: params_per_head,
                query_qubits: (0..self.num_qubits / 2).collect(),
                key_qubits: (self.num_qubits / 2..self.num_qubits).collect(),
            });
        }

        Ok(())
    }
}

impl QMLLayer for QuantumAttentionLayer {
    fn forward(&mut self, input: &Array1<Complex64>) -> Result<Array1<Complex64>> {
        let mut state = input.clone();

        // Apply attention heads
        for head in &self.attention_structure {
            state = self.apply_attention_head(&state, head)?;
        }

        Ok(state)
    }

    fn backward(&mut self, gradient: &Array1<f64>) -> Result<Array1<f64>> {
        Ok(gradient.clone())
    }

    fn get_parameters(&self) -> Array1<f64> {
        self.parameters.clone()
    }

    fn set_parameters(&mut self, parameters: &Array1<f64>) {
        self.parameters = parameters.clone();
    }

    fn get_depth(&self) -> usize {
        self.attention_structure.len()
    }

    fn get_gate_count(&self) -> usize {
        self.parameters.len()
    }

    fn get_num_parameters(&self) -> usize {
        self.parameters.len()
    }
}

impl QuantumAttentionLayer {
    /// Apply attention head
    fn apply_attention_head(
        &self,
        state: &Array1<Complex64>,
        head: &AttentionHead,
    ) -> Result<Array1<Complex64>> {
        let mut new_state = state.clone();

        // Simplified quantum attention mechanism
        for i in 0..head.parameter_count {
            let param_idx = head.parameter_start + i;
            if param_idx < self.parameters.len() {
                let angle = self.parameters[param_idx];

                // Apply cross-attention between query and key qubits
                if i < head.query_qubits.len() && i < head.key_qubits.len() {
                    let query_qubit = head.query_qubits[i];
                    let key_qubit = head.key_qubits[i];

                    new_state =
                        Self::apply_attention_gate(&new_state, query_qubit, key_qubit, angle)?;
                }
            }
        }

        Ok(new_state)
    }

    /// Apply attention gate (parameterized two-qubit interaction)
    fn apply_attention_gate(
        state: &Array1<Complex64>,
        query_qubit: usize,
        key_qubit: usize,
        angle: f64,
    ) -> Result<Array1<Complex64>> {
        let state_size = state.len();
        let mut new_state = state.clone();

        // Apply controlled rotation based on attention score
        let cos_val = angle.cos();
        let sin_val = angle.sin();

        for i in 0..state_size {
            if (i & (1 << query_qubit)) != 0 {
                // Query qubit is |1⟩, apply attention
                let key_state = (i & (1 << key_qubit)) != 0;
                let attention_phase = if key_state {
                    Complex64::new(cos_val, sin_val)
                } else {
                    Complex64::new(cos_val, -sin_val)
                };
                new_state[i] *= attention_phase;
            }
        }

        Ok(new_state)
    }

    /// Get attention structure reference
    #[must_use]
    pub fn get_attention_structure(&self) -> &[AttentionHead] {
        &self.attention_structure
    }
}

/// Training state for QML framework
#[derive(Debug, Clone)]
pub struct QMLTrainingState {
    /// Current epoch
    pub current_epoch: usize,
    /// Current learning rate
    pub current_learning_rate: f64,
    /// Best validation loss achieved
    pub best_validation_loss: f64,
    /// Patience counter for early stopping
    pub patience_counter: usize,
    /// Training loss history
    pub training_loss_history: Vec<f64>,
    /// Validation loss history
    pub validation_loss_history: Vec<f64>,
}

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

impl QMLTrainingState {
    /// Create new training state
    #[must_use]
    pub const fn new() -> Self {
        Self {
            current_epoch: 0,
            current_learning_rate: 0.01,
            best_validation_loss: f64::INFINITY,
            patience_counter: 0,
            training_loss_history: Vec::new(),
            validation_loss_history: Vec::new(),
        }
    }
}

/// Training result for QML framework
#[derive(Debug, Clone)]
pub struct QMLTrainingResult {
    /// Final training loss
    pub final_training_loss: f64,
    /// Final validation loss
    pub final_validation_loss: f64,
    /// Best validation loss achieved
    pub best_validation_loss: f64,
    /// Number of epochs trained
    pub epochs_trained: usize,
    /// Total training time
    pub total_training_time: std::time::Duration,
    /// Training metrics per epoch
    pub training_metrics: Vec<QMLEpochMetrics>,
    /// Quantum advantage metrics
    pub quantum_advantage_metrics: QuantumAdvantageMetrics,
}

/// Training metrics for a single epoch
#[derive(Debug, Clone)]
pub struct QMLEpochMetrics {
    /// Epoch number
    pub epoch: usize,
    /// Training loss
    pub training_loss: f64,
    /// Validation loss
    pub validation_loss: f64,
    /// Time taken for epoch
    pub epoch_time: std::time::Duration,
    /// Learning rate used
    pub learning_rate: f64,
}

/// Quantum advantage metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantumAdvantageMetrics {
    /// Quantum volume achieved
    pub quantum_volume: f64,
    /// Classical simulation cost estimate
    pub classical_simulation_cost: f64,
    /// Quantum speedup factor
    pub quantum_speedup_factor: f64,
    /// Circuit depth
    pub circuit_depth: usize,
    /// Total gate count
    pub gate_count: usize,
    /// Entanglement measure
    pub entanglement_measure: f64,
}

/// QML framework statistics
#[derive(Debug, Clone)]
pub struct QMLStats {
    /// Number of forward passes
    pub forward_passes: usize,
    /// Number of backward passes
    pub backward_passes: usize,
    /// Total training time
    pub total_training_time: std::time::Duration,
    /// Average epoch time
    pub average_epoch_time: std::time::Duration,
    /// Peak memory usage
    pub peak_memory_usage: usize,
    /// Number of parameters
    pub num_parameters: usize,
}

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

impl QMLStats {
    /// Create new statistics
    #[must_use]
    pub const fn new() -> Self {
        Self {
            forward_passes: 0,
            backward_passes: 0,
            total_training_time: std::time::Duration::from_secs(0),
            average_epoch_time: std::time::Duration::from_secs(0),
            peak_memory_usage: 0,
            num_parameters: 0,
        }
    }
}

/// Parameterized quantum circuit gate
#[derive(Debug, Clone)]
pub struct PQCGate {
    /// Gate type
    pub gate_type: PQCGateType,
    /// Qubits involved
    pub qubits: Vec<usize>,
    /// Parameter index (if parameterized)
    pub parameter_index: Option<usize>,
}

/// Types of PQC gates
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum PQCGateType {
    /// Single-qubit rotation gate
    SingleQubit(RotationGate),
    /// Two-qubit gate
    TwoQubit(TwoQubitGate),
}

/// Two-qubit gates
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TwoQubitGate {
    /// CNOT gate
    CNOT,
    /// Controlled-Z gate
    CZ,
    /// Swap gate
    SWAP,
    /// Controlled-Phase gate
    CPhase,
}

/// Convolutional filter structure
#[derive(Debug, Clone)]
pub struct ConvolutionalFilter {
    /// Qubits in the filter
    pub qubits: Vec<usize>,
    /// Parameter indices
    pub parameter_indices: Vec<usize>,
}

/// Dense layer connection
#[derive(Debug, Clone)]
pub struct DenseConnection {
    /// First qubit
    pub qubit1: usize,
    /// Second qubit
    pub qubit2: usize,
    /// Parameter index
    pub parameter_index: usize,
}

/// LSTM gate structure
#[derive(Debug, Clone)]
pub struct LSTMGate {
    /// LSTM gate type
    pub gate_type: LSTMGateType,
    /// Starting parameter index
    pub parameter_start: usize,
    /// Number of parameters
    pub parameter_count: usize,
}

/// LSTM gate types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LSTMGateType {
    /// Forget gate
    Forget,
    /// Input gate
    Input,
    /// Output gate
    Output,
    /// Candidate values
    Candidate,
}

/// Attention head structure
#[derive(Debug, Clone)]
pub struct AttentionHead {
    /// Head identifier
    pub head_id: usize,
    /// Starting parameter index
    pub parameter_start: usize,
    /// Number of parameters
    pub parameter_count: usize,
    /// Query qubits
    pub query_qubits: Vec<usize>,
    /// Key qubits
    pub key_qubits: Vec<usize>,
}

/// QML benchmark results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QMLBenchmarkResults {
    /// Training time per method
    pub training_times: HashMap<String, std::time::Duration>,
    /// Final accuracies
    pub final_accuracies: HashMap<String, f64>,
    /// Convergence rates
    pub convergence_rates: HashMap<String, f64>,
    /// Memory usage
    pub memory_usage: HashMap<String, usize>,
    /// Quantum advantage metrics
    pub quantum_advantage: HashMap<String, QuantumAdvantageMetrics>,
    /// Parameter counts
    pub parameter_counts: HashMap<String, usize>,
    /// Circuit depths
    pub circuit_depths: HashMap<String, usize>,
    /// Gate counts
    pub gate_counts: HashMap<String, usize>,
}

/// Utility functions for QML
pub struct QMLUtils;

impl QMLUtils {
    /// Generate synthetic training data for testing
    #[must_use]
    pub fn generate_synthetic_data(
        num_samples: usize,
        input_dim: usize,
        output_dim: usize,
    ) -> (Vec<Array1<f64>>, Vec<Array1<f64>>) {
        let mut rng = thread_rng();
        let mut inputs = Vec::new();
        let mut outputs = Vec::new();

        for _ in 0..num_samples {
            let input: Array1<f64> = Array1::from_vec(
                (0..input_dim)
                    .map(|_| rng.gen_range(-1.0_f64..1.0_f64))
                    .collect(),
            );

            // Generate output based on some function of input
            let output = Array1::from_vec(
                (0..output_dim)
                    .map(|i| {
                        if i < input_dim {
                            input[i].sin() // Simple nonlinear transformation
                        } else {
                            rng.gen_range(-1.0_f64..1.0_f64)
                        }
                    })
                    .collect(),
            );

            inputs.push(input);
            outputs.push(output);
        }

        (inputs, outputs)
    }

    /// Split data into training and validation sets
    #[must_use]
    pub fn train_test_split(
        inputs: Vec<Array1<f64>>,
        outputs: Vec<Array1<f64>>,
        test_ratio: f64,
    ) -> (
        Vec<(Array1<f64>, Array1<f64>)>,
        Vec<(Array1<f64>, Array1<f64>)>,
    ) {
        let total_samples = inputs.len();
        let test_samples = ((total_samples as f64) * test_ratio) as usize;
        let train_samples = total_samples - test_samples;

        let mut combined: Vec<(Array1<f64>, Array1<f64>)> =
            inputs.into_iter().zip(outputs).collect();

        // Shuffle data
        let mut rng = thread_rng();
        for i in (1..combined.len()).rev() {
            let j = rng.gen_range(0..=i);
            combined.swap(i, j);
        }

        let (train_data, test_data) = combined.split_at(train_samples);
        (train_data.to_vec(), test_data.to_vec())
    }

    /// Evaluate model accuracy
    #[must_use]
    pub fn evaluate_accuracy(
        predictions: &[Array1<f64>],
        targets: &[Array1<f64>],
        threshold: f64,
    ) -> f64 {
        let mut correct = 0;
        let total = predictions.len();

        for (pred, target) in predictions.iter().zip(targets.iter()) {
            let diff = pred - target;
            let mse = diff.iter().map(|x| x * x).sum::<f64>() / diff.len() as f64;
            if mse < threshold {
                correct += 1;
            }
        }

        f64::from(correct) / total as f64
    }

    /// Compute quantum circuit complexity metrics
    #[must_use]
    pub fn compute_circuit_complexity(
        num_qubits: usize,
        depth: usize,
        gate_count: usize,
    ) -> HashMap<String, f64> {
        let mut metrics = HashMap::new();

        // State space size
        let state_space_size = 2.0_f64.powi(num_qubits as i32);
        metrics.insert("state_space_size".to_string(), state_space_size);

        // Circuit complexity (depth * gates)
        let circuit_complexity = (depth * gate_count) as f64;
        metrics.insert("circuit_complexity".to_string(), circuit_complexity);

        // Classical simulation cost estimate
        let classical_cost = state_space_size * gate_count as f64;
        metrics.insert("classical_simulation_cost".to_string(), classical_cost);

        // Quantum advantage estimate (log scale)
        let quantum_advantage = classical_cost.log(circuit_complexity);
        metrics.insert("quantum_advantage_estimate".to_string(), quantum_advantage);

        metrics
    }
}

/// Benchmark quantum machine learning implementations
pub fn benchmark_quantum_ml_layers(config: &QMLConfig) -> Result<QMLBenchmarkResults> {
    let mut results = QMLBenchmarkResults {
        training_times: HashMap::new(),
        final_accuracies: HashMap::new(),
        convergence_rates: HashMap::new(),
        memory_usage: HashMap::new(),
        quantum_advantage: HashMap::new(),
        parameter_counts: HashMap::new(),
        circuit_depths: HashMap::new(),
        gate_counts: HashMap::new(),
    };

    // Generate test data
    let (inputs, outputs) =
        QMLUtils::generate_synthetic_data(100, config.num_qubits, config.num_qubits);
    let (train_data, val_data) = QMLUtils::train_test_split(inputs, outputs, 0.2);

    // Benchmark different QML architectures
    let architectures = vec![
        QMLArchitectureType::VariationalQuantumCircuit,
        QMLArchitectureType::QuantumConvolutionalNN,
        // Add more architectures as needed
    ];

    for architecture in architectures {
        let arch_name = format!("{architecture:?}");

        // Create configuration for this architecture
        let mut arch_config = config.clone();
        arch_config.architecture_type = architecture;

        // Create and train model
        let start_time = std::time::Instant::now();
        let mut framework = QuantumMLFramework::new(arch_config)?;

        let training_result = framework.train(&train_data, Some(&val_data))?;
        let training_time = start_time.elapsed();

        // Evaluate final accuracy
        let final_accuracy = framework.evaluate(&val_data)?;

        // Store results
        results
            .training_times
            .insert(arch_name.clone(), training_time);
        results
            .final_accuracies
            .insert(arch_name.clone(), 1.0 / (1.0 + final_accuracy)); // Convert loss to accuracy
        results.convergence_rates.insert(
            arch_name.clone(),
            training_result.epochs_trained as f64 / config.training_config.epochs as f64,
        );
        results
            .memory_usage
            .insert(arch_name.clone(), framework.get_stats().peak_memory_usage);
        results
            .quantum_advantage
            .insert(arch_name.clone(), training_result.quantum_advantage_metrics);
        results.parameter_counts.insert(
            arch_name.clone(),
            framework
                .layers
                .iter()
                .map(|l| l.get_num_parameters())
                .sum(),
        );
        results.circuit_depths.insert(
            arch_name.clone(),
            framework.layers.iter().map(|l| l.get_depth()).sum(),
        );
        results.gate_counts.insert(
            arch_name.clone(),
            framework.layers.iter().map(|l| l.get_gate_count()).sum(),
        );
    }

    Ok(results)
}