quantrs2_sim/

advanced_ml_error_mitigation.rs

//! Advanced Machine Learning Error Mitigation Techniques
//!
//! This module implements state-of-the-art machine learning approaches for quantum error mitigation,
//! going beyond traditional ZNE and virtual distillation. It includes deep learning models,
//! reinforcement learning agents, transfer learning capabilities, and ensemble methods for
//! robust quantum error mitigation across different hardware platforms and noise models.
//!
//! Key features:
//! - Deep neural networks for complex noise pattern learning
//! - Reinforcement learning for optimal mitigation strategy selection
//! - Transfer learning for cross-device mitigation optimization
//! - Adversarial training for robustness against unknown noise
//! - Ensemble methods combining multiple mitigation strategies
//! - Online learning for real-time adaptation to drifting noise
//! - Graph neural networks for circuit structure-aware mitigation
//! - Attention mechanisms for long-range error correlations

use ndarray::{Array1, Array2, Array3};
use rand::{thread_rng, Rng};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};

use crate::circuit_interfaces::{InterfaceCircuit, InterfaceGate, InterfaceGateType};
use crate::error::{Result, SimulatorError};

/// Advanced ML error mitigation configuration
#[derive(Debug, Clone)]
pub struct AdvancedMLMitigationConfig {
    /// Enable deep learning models
    pub enable_deep_learning: bool,
    /// Enable reinforcement learning
    pub enable_reinforcement_learning: bool,
    /// Enable transfer learning
    pub enable_transfer_learning: bool,
    /// Enable adversarial training
    pub enable_adversarial_training: bool,
    /// Enable ensemble methods
    pub enable_ensemble_methods: bool,
    /// Enable online learning
    pub enable_online_learning: bool,
    /// Learning rate for adaptive methods
    pub learning_rate: f64,
    /// Batch size for training
    pub batch_size: usize,
    /// Memory size for experience replay
    pub memory_size: usize,
    /// Exploration rate for RL
    pub exploration_rate: f64,
    /// Transfer learning alpha
    pub transfer_alpha: f64,
    /// Ensemble size
    pub ensemble_size: usize,
}

impl Default for AdvancedMLMitigationConfig {
    fn default() -> Self {
        Self {
            enable_deep_learning: true,
            enable_reinforcement_learning: true,
            enable_transfer_learning: false,
            enable_adversarial_training: false,
            enable_ensemble_methods: true,
            enable_online_learning: true,
            learning_rate: 0.001,
            batch_size: 64,
            memory_size: 10000,
            exploration_rate: 0.1,
            transfer_alpha: 0.5,
            ensemble_size: 5,
        }
    }
}

/// Deep learning model for error mitigation
#[derive(Debug, Clone)]
pub struct DeepMitigationNetwork {
    /// Network architecture
    pub layers: Vec<usize>,
    /// Weights for each layer
    pub weights: Vec<Array2<f64>>,
    /// Biases for each layer
    pub biases: Vec<Array1<f64>>,
    /// Activation function
    pub activation: ActivationFunction,
    /// Loss history
    pub loss_history: Vec<f64>,
}

/// Activation functions for neural networks
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ActivationFunction {
    ReLU,
    Sigmoid,
    Tanh,
    Swish,
    GELU,
}

/// Reinforcement learning agent for mitigation strategy selection
#[derive(Debug, Clone)]
pub struct QLearningMitigationAgent {
    /// Q-table for state-action values
    pub q_table: HashMap<String, HashMap<MitigationAction, f64>>,
    /// Learning rate
    pub learning_rate: f64,
    /// Discount factor
    pub discount_factor: f64,
    /// Exploration rate
    pub exploration_rate: f64,
    /// Experience replay buffer
    pub experience_buffer: VecDeque<Experience>,
    /// Training statistics
    pub stats: RLTrainingStats,
}

/// Mitigation actions for reinforcement learning
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum MitigationAction {
    ZeroNoiseExtrapolation,
    VirtualDistillation,
    SymmetryVerification,
    PauliTwirling,
    RandomizedCompiling,
    ClusterExpansion,
    MachineLearningPrediction,
    EnsembleMitigation,
}

/// Experience for reinforcement learning
#[derive(Debug, Clone)]
pub struct Experience {
    /// State representation
    pub state: Array1<f64>,
    /// Action taken
    pub action: MitigationAction,
    /// Reward received
    pub reward: f64,
    /// Next state
    pub next_state: Array1<f64>,
    /// Whether episode terminated
    pub done: bool,
}

/// Reinforcement learning training statistics
#[derive(Debug, Clone, Default)]
pub struct RLTrainingStats {
    /// Total episodes
    pub episodes: usize,
    /// Average reward per episode
    pub avg_reward: f64,
    /// Success rate
    pub success_rate: f64,
    /// Exploration rate decay
    pub exploration_decay: f64,
    /// Loss convergence
    pub loss_convergence: Vec<f64>,
}

/// Transfer learning model for cross-device mitigation
#[derive(Debug, Clone)]
pub struct TransferLearningModel {
    /// Source device characteristics
    pub source_device: DeviceCharacteristics,
    /// Target device characteristics
    pub target_device: DeviceCharacteristics,
    /// Shared feature extractor
    pub feature_extractor: DeepMitigationNetwork,
    /// Device-specific heads
    pub device_heads: HashMap<String, DeepMitigationNetwork>,
    /// Transfer learning alpha
    pub transfer_alpha: f64,
    /// Adaptation statistics
    pub adaptation_stats: TransferStats,
}

/// Device characteristics for transfer learning
#[derive(Debug, Clone)]
pub struct DeviceCharacteristics {
    /// Device identifier
    pub device_id: String,
    /// Gate error rates
    pub gate_errors: HashMap<String, f64>,
    /// Coherence times
    pub coherence_times: HashMap<String, f64>,
    /// Connectivity graph
    pub connectivity: Array2<bool>,
    /// Noise correlations
    pub noise_correlations: Array2<f64>,
}

/// Transfer learning statistics
#[derive(Debug, Clone, Default)]
pub struct TransferStats {
    /// Adaptation loss
    pub adaptation_loss: f64,
    /// Source domain performance
    pub source_performance: f64,
    /// Target domain performance
    pub target_performance: f64,
    /// Transfer efficiency
    pub transfer_efficiency: f64,
}

/// Ensemble mitigation combining multiple strategies
pub struct EnsembleMitigation {
    /// Individual mitigation models
    pub models: Vec<Box<dyn MitigationModel>>,
    /// Model weights
    pub weights: Array1<f64>,
    /// Combination strategy
    pub combination_strategy: EnsembleStrategy,
    /// Performance history
    pub performance_history: Vec<f64>,
}

/// Ensemble combination strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum EnsembleStrategy {
    /// Weighted average
    WeightedAverage,
    /// Majority voting
    MajorityVoting,
    /// Stacking with meta-learner
    Stacking,
    /// Dynamic selection
    DynamicSelection,
    /// Bayesian model averaging
    BayesianAveraging,
}

/// Trait for mitigation models
pub trait MitigationModel: Send + Sync {
    /// Apply mitigation to measurement results
    fn mitigate(&self, measurements: &Array1<f64>, circuit: &InterfaceCircuit) -> Result<f64>;

    /// Update model with new data
    fn update(&mut self, training_data: &[(Array1<f64>, f64)]) -> Result<()>;

    /// Get model confidence
    fn confidence(&self) -> f64;

    /// Get model name
    fn name(&self) -> String;
}

/// Advanced ML error mitigation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdvancedMLMitigationResult {
    /// Mitigated expectation value
    pub mitigated_value: f64,
    /// Confidence in mitigation
    pub confidence: f64,
    /// Model used for mitigation
    pub model_used: String,
    /// Raw measurements
    pub raw_measurements: Vec<f64>,
    /// Mitigation overhead
    pub overhead: f64,
    /// Error reduction estimate
    pub error_reduction: f64,
    /// Model performance metrics
    pub performance_metrics: PerformanceMetrics,
}

/// Performance metrics for mitigation models
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Mean absolute error
    pub mae: f64,
    /// Root mean square error
    pub rmse: f64,
    /// R-squared coefficient
    pub r_squared: f64,
    /// Bias
    pub bias: f64,
    /// Variance
    pub variance: f64,
    /// Computational time
    pub computation_time_ms: f64,
}

/// Graph Neural Network for circuit-aware mitigation
#[derive(Debug, Clone)]
pub struct GraphMitigationNetwork {
    /// Node features (gates)
    pub node_features: Array2<f64>,
    /// Edge features (connections)
    pub edge_features: Array3<f64>,
    /// Attention weights
    pub attention_weights: Array2<f64>,
    /// Graph convolution layers
    pub conv_layers: Vec<GraphConvLayer>,
    /// Global pooling method
    pub pooling: GraphPooling,
}

/// Graph convolution layer
#[derive(Debug, Clone)]
pub struct GraphConvLayer {
    /// Weight matrix
    pub weights: Array2<f64>,
    /// Bias vector
    pub bias: Array1<f64>,
    /// Activation function
    pub activation: ActivationFunction,
}

/// Graph pooling methods
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum GraphPooling {
    Mean,
    Max,
    Sum,
    Attention,
    Set2Set,
}

/// Main advanced ML error mitigation system
pub struct AdvancedMLErrorMitigator {
    /// Configuration
    config: AdvancedMLMitigationConfig,
    /// Deep learning model
    deep_model: Option<DeepMitigationNetwork>,
    /// Reinforcement learning agent
    rl_agent: Option<QLearningMitigationAgent>,
    /// Transfer learning model
    transfer_model: Option<TransferLearningModel>,
    /// Ensemble model
    ensemble: Option<EnsembleMitigation>,
    /// Graph neural network
    graph_model: Option<GraphMitigationNetwork>,
    /// Training data history
    training_history: VecDeque<(Array1<f64>, f64)>,
    /// Performance tracker
    performance_tracker: PerformanceTracker,
}

/// Performance tracking for mitigation models
#[derive(Debug, Clone, Default)]
pub struct PerformanceTracker {
    /// Model accuracies over time
    pub accuracy_history: HashMap<String, Vec<f64>>,
    /// Computational costs
    pub cost_history: HashMap<String, Vec<f64>>,
    /// Error reduction achieved
    pub error_reduction_history: Vec<f64>,
    /// Best performing model per task
    pub best_models: HashMap<String, String>,
}

impl AdvancedMLErrorMitigator {
    /// Create new advanced ML error mitigator
    pub fn new(config: AdvancedMLMitigationConfig) -> Result<Self> {
        let mut mitigator = Self {
            config: config.clone(),
            deep_model: None,
            rl_agent: None,
            transfer_model: None,
            ensemble: None,
            graph_model: None,
            training_history: VecDeque::with_capacity(config.memory_size),
            performance_tracker: PerformanceTracker::default(),
        };

        // Initialize enabled models
        if config.enable_deep_learning {
            mitigator.deep_model = Some(mitigator.create_deep_model()?);
        }

        if config.enable_reinforcement_learning {
            mitigator.rl_agent = Some(mitigator.create_rl_agent()?);
        }

        if config.enable_ensemble_methods {
            mitigator.ensemble = Some(mitigator.create_ensemble()?);
        }

        Ok(mitigator)
    }

    /// Apply advanced ML error mitigation
    pub fn mitigate_errors(
        &mut self,
        measurements: &Array1<f64>,
        circuit: &InterfaceCircuit,
    ) -> Result<AdvancedMLMitigationResult> {
        let start_time = std::time::Instant::now();

        // Extract features from circuit and measurements
        let features = self.extract_features(circuit, measurements)?;

        // Select best mitigation strategy
        let strategy = self.select_mitigation_strategy(&features)?;

        // Apply selected mitigation
        let mitigated_value = match strategy {
            MitigationAction::MachineLearningPrediction => {
                self.apply_deep_learning_mitigation(&features, measurements)?
            }
            MitigationAction::EnsembleMitigation => {
                self.apply_ensemble_mitigation(&features, measurements, circuit)?
            }
            _ => {
                // Fall back to traditional methods
                self.apply_traditional_mitigation(strategy, measurements, circuit)?
            }
        };

        // Calculate confidence and performance metrics
        let confidence = self.calculate_confidence(&features, mitigated_value)?;
        let error_reduction = self.estimate_error_reduction(measurements, mitigated_value)?;

        let computation_time = start_time.elapsed().as_millis() as f64;

        // Update models with new data
        self.update_models(&features, mitigated_value)?;

        Ok(AdvancedMLMitigationResult {
            mitigated_value,
            confidence,
            model_used: format!("{:?}", strategy),
            raw_measurements: measurements.to_vec(),
            overhead: computation_time / 1000.0, // Convert to seconds
            error_reduction,
            performance_metrics: PerformanceMetrics {
                computation_time_ms: computation_time,
                ..Default::default()
            },
        })
    }

    /// Create deep learning model
    pub fn create_deep_model(&self) -> Result<DeepMitigationNetwork> {
        let layers = vec![18, 128, 64, 32, 1]; // Architecture for error prediction
        let mut weights = Vec::new();
        let mut biases = Vec::new();

        // Initialize weights and biases with Xavier initialization
        for i in 0..layers.len() - 1 {
            let fan_in = layers[i];
            let fan_out = layers[i + 1];
            let limit = (6.0 / (fan_in + fan_out) as f64).sqrt();

            let w =
                Array2::from_shape_fn((fan_out, fan_in), |_| thread_rng().gen_range(-limit..limit));
            let b = Array1::zeros(fan_out);

            weights.push(w);
            biases.push(b);
        }

        Ok(DeepMitigationNetwork {
            layers,
            weights,
            biases,
            activation: ActivationFunction::ReLU,
            loss_history: Vec::new(),
        })
    }

    /// Create reinforcement learning agent
    pub fn create_rl_agent(&self) -> Result<QLearningMitigationAgent> {
        Ok(QLearningMitigationAgent {
            q_table: HashMap::new(),
            learning_rate: self.config.learning_rate,
            discount_factor: 0.95,
            exploration_rate: self.config.exploration_rate,
            experience_buffer: VecDeque::with_capacity(self.config.memory_size),
            stats: RLTrainingStats::default(),
        })
    }

    /// Create ensemble model
    fn create_ensemble(&self) -> Result<EnsembleMitigation> {
        let models: Vec<Box<dyn MitigationModel>> = Vec::new();
        let weights = Array1::ones(self.config.ensemble_size) / self.config.ensemble_size as f64;

        Ok(EnsembleMitigation {
            models,
            weights,
            combination_strategy: EnsembleStrategy::WeightedAverage,
            performance_history: Vec::new(),
        })
    }

    /// Extract features from circuit and measurements
    pub fn extract_features(
        &self,
        circuit: &InterfaceCircuit,
        measurements: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        let mut features = Vec::new();

        // Circuit features
        features.push(circuit.gates.len() as f64); // Circuit depth
        features.push(circuit.num_qubits as f64); // Number of qubits

        // Gate type distribution
        let mut gate_counts = HashMap::new();
        for gate in &circuit.gates {
            *gate_counts
                .entry(format!("{:?}", gate.gate_type))
                .or_insert(0) += 1;
        }

        // Add normalized gate counts (top 10 most common gates)
        let total_gates = circuit.gates.len() as f64;
        for gate_type in [
            "PauliX", "PauliY", "PauliZ", "Hadamard", "CNOT", "CZ", "RX", "RY", "RZ", "Phase",
        ] {
            let count = gate_counts.get(gate_type).unwrap_or(&0);
            features.push(*count as f64 / total_gates);
        }

        // Measurement statistics
        features.push(measurements.mean().unwrap_or(0.0));
        features.push(measurements.std(0.0));
        features.push(measurements.var(0.0));
        features.push(measurements.len() as f64);

        // Circuit topology features
        features.push(self.calculate_circuit_connectivity(circuit)?);
        features.push(self.calculate_entanglement_estimate(circuit)?);

        Ok(Array1::from_vec(features))
    }

    /// Select optimal mitigation strategy using RL agent
    pub fn select_mitigation_strategy(
        &mut self,
        features: &Array1<f64>,
    ) -> Result<MitigationAction> {
        if let Some(ref mut agent) = self.rl_agent {
            let state_key = Self::features_to_state_key(features);

            // Epsilon-greedy action selection
            if rand::random::<f64>() < agent.exploration_rate {
                // Random exploration
                let actions = [
                    MitigationAction::ZeroNoiseExtrapolation,
                    MitigationAction::VirtualDistillation,
                    MitigationAction::MachineLearningPrediction,
                    MitigationAction::EnsembleMitigation,
                ];
                Ok(actions[thread_rng().gen_range(0..actions.len())])
            } else {
                // Greedy exploitation
                let q_values = agent.q_table.get(&state_key).cloned().unwrap_or_default();

                let best_action = q_values
                    .iter()
                    .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
                    .map(|(action, _)| *action)
                    .unwrap_or(MitigationAction::MachineLearningPrediction);

                Ok(best_action)
            }
        } else {
            // Default strategy if no RL agent
            Ok(MitigationAction::MachineLearningPrediction)
        }
    }

    /// Apply deep learning based mitigation
    fn apply_deep_learning_mitigation(
        &self,
        features: &Array1<f64>,
        measurements: &Array1<f64>,
    ) -> Result<f64> {
        if let Some(ref model) = self.deep_model {
            let prediction = Self::forward_pass_static(model, features)?;

            // Use prediction to correct measurements
            let correction_factor = prediction[0];
            let mitigated_value = measurements.mean().unwrap_or(0.0) * (1.0 + correction_factor);

            Ok(mitigated_value)
        } else {
            Err(SimulatorError::InvalidConfiguration(
                "Deep learning model not initialized".to_string(),
            ))
        }
    }

    /// Apply ensemble mitigation
    fn apply_ensemble_mitigation(
        &self,
        features: &Array1<f64>,
        measurements: &Array1<f64>,
        circuit: &InterfaceCircuit,
    ) -> Result<f64> {
        if let Some(ref ensemble) = self.ensemble {
            let mut predictions = Vec::new();

            // Collect predictions from all models
            for model in &ensemble.models {
                let prediction = model.mitigate(measurements, circuit)?;
                predictions.push(prediction);
            }

            // Combine predictions using ensemble strategy
            let mitigated_value = match ensemble.combination_strategy {
                EnsembleStrategy::WeightedAverage => {
                    let weighted_sum: f64 = predictions
                        .iter()
                        .zip(ensemble.weights.iter())
                        .map(|(pred, weight)| pred * weight)
                        .sum();
                    weighted_sum
                }
                EnsembleStrategy::MajorityVoting => {
                    // For regression, use median
                    let mut sorted_predictions = predictions.clone();
                    sorted_predictions.sort_by(|a, b| a.partial_cmp(b).unwrap());
                    sorted_predictions[sorted_predictions.len() / 2]
                }
                _ => {
                    // Default to simple average
                    predictions.iter().sum::<f64>() / predictions.len() as f64
                }
            };

            Ok(mitigated_value)
        } else {
            // Fallback to simple measurement average
            Ok(measurements.mean().unwrap_or(0.0))
        }
    }

    /// Apply traditional mitigation methods
    pub fn apply_traditional_mitigation(
        &self,
        strategy: MitigationAction,
        measurements: &Array1<f64>,
        _circuit: &InterfaceCircuit,
    ) -> Result<f64> {
        match strategy {
            MitigationAction::ZeroNoiseExtrapolation => {
                // Simple linear extrapolation for demonstration
                let noise_factors = [1.0, 1.5, 2.0];
                let values: Vec<f64> = noise_factors
                    .iter()
                    .zip(measurements.iter())
                    .map(|(factor, &val)| val / factor)
                    .collect();

                // Linear extrapolation to zero noise
                let extrapolated = 2.0 * values[0] - values[1];
                Ok(extrapolated)
            }
            MitigationAction::VirtualDistillation => {
                // Simple virtual distillation approximation
                let mean_val = measurements.mean().unwrap_or(0.0);
                let variance = measurements.var(0.0);
                let corrected = mean_val + variance * 0.1; // Simple correction
                Ok(corrected)
            }
            _ => {
                // Default to measurement average
                Ok(measurements.mean().unwrap_or(0.0))
            }
        }
    }

    /// Forward pass through neural network (static)
    fn forward_pass_static(
        model: &DeepMitigationNetwork,
        input: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        let mut current = input.clone();

        for (weights, bias) in model.weights.iter().zip(model.biases.iter()) {
            // Linear transformation: Wx + b
            current = weights.dot(&current) + bias;

            // Apply activation function
            current.mapv_inplace(|x| Self::apply_activation_static(x, model.activation));
        }

        Ok(current)
    }

    /// Apply activation function (static version)
    fn apply_activation_static(x: f64, activation: ActivationFunction) -> f64 {
        match activation {
            ActivationFunction::ReLU => x.max(0.0),
            ActivationFunction::Sigmoid => 1.0 / (1.0 + (-x).exp()),
            ActivationFunction::Tanh => x.tanh(),
            ActivationFunction::Swish => x * (1.0 / (1.0 + (-x).exp())),
            ActivationFunction::GELU => {
                0.5 * x
                    * (1.0
                        + ((2.0 / std::f64::consts::PI).sqrt() * (x + 0.044715 * x.powi(3))).tanh())
            }
        }
    }

    /// Apply activation function
    pub fn apply_activation(&self, x: f64, activation: ActivationFunction) -> f64 {
        Self::apply_activation_static(x, activation)
    }

    /// Public wrapper for forward pass (for testing)
    pub fn forward_pass(
        &self,
        model: &DeepMitigationNetwork,
        input: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        Self::forward_pass_static(model, input)
    }

    /// Calculate circuit connectivity measure
    fn calculate_circuit_connectivity(&self, circuit: &InterfaceCircuit) -> Result<f64> {
        if circuit.num_qubits == 0 {
            return Ok(0.0);
        }

        let mut connectivity_sum = 0.0;
        let total_possible_connections = (circuit.num_qubits * (circuit.num_qubits - 1)) / 2;

        for gate in &circuit.gates {
            if gate.qubits.len() > 1 {
                connectivity_sum += 1.0;
            }
        }

        Ok(connectivity_sum / total_possible_connections as f64)
    }

    /// Estimate entanglement in circuit
    fn calculate_entanglement_estimate(&self, circuit: &InterfaceCircuit) -> Result<f64> {
        let mut entangling_gates = 0;

        for gate in &circuit.gates {
            match gate.gate_type {
                InterfaceGateType::CNOT
                | InterfaceGateType::CZ
                | InterfaceGateType::CY
                | InterfaceGateType::SWAP
                | InterfaceGateType::ISwap
                | InterfaceGateType::Toffoli => {
                    entangling_gates += 1;
                }
                _ => {}
            }
        }

        Ok(entangling_gates as f64 / circuit.gates.len() as f64)
    }

    /// Convert features to state key for Q-learning
    fn features_to_state_key(features: &Array1<f64>) -> String {
        // Discretize features for state representation
        let discretized: Vec<i32> = features
            .iter()
            .map(|&x| (x * 10.0).round() as i32)
            .collect();
        format!("{:?}", discretized)
    }

    /// Calculate confidence in mitigation result
    fn calculate_confidence(&self, features: &Array1<f64>, _mitigated_value: f64) -> Result<f64> {
        // Simple confidence calculation based on feature consistency
        let feature_variance = features.var(0.0);
        let confidence = 1.0 / (1.0 + feature_variance);
        Ok(confidence.min(1.0).max(0.0))
    }

    /// Estimate error reduction achieved
    fn estimate_error_reduction(&self, original: &Array1<f64>, mitigated: f64) -> Result<f64> {
        let original_mean = original.mean().unwrap_or(0.0);
        let original_variance = original.var(0.0);

        // Estimate error reduction based on variance reduction
        let estimated_improvement = (original_variance.sqrt() - (mitigated - original_mean).abs())
            / original_variance.sqrt();
        Ok(estimated_improvement.max(0.0).min(1.0))
    }

    /// Update models with new training data
    fn update_models(&mut self, features: &Array1<f64>, target: f64) -> Result<()> {
        // Add to training history
        if self.training_history.len() >= self.config.memory_size {
            self.training_history.pop_front();
        }
        self.training_history.push_back((features.clone(), target));

        // Update deep learning model if enough data
        if self.training_history.len() >= self.config.batch_size {
            self.update_deep_model()?;
        }

        // Update RL agent
        self.update_rl_agent(features, target)?;

        Ok(())
    }

    /// Update deep learning model with recent training data
    fn update_deep_model(&mut self) -> Result<()> {
        if let Some(ref mut model) = self.deep_model {
            // Simple gradient descent update (simplified for demonstration)
            // In practice, would implement proper backpropagation

            let batch_size = self.config.batch_size.min(self.training_history.len());
            let batch: Vec<_> = self
                .training_history
                .iter()
                .rev()
                .take(batch_size)
                .collect();

            let mut total_loss = 0.0;

            for (features, target) in batch {
                let prediction = Self::forward_pass_static(model, features)?;
                let loss = (prediction[0] - target).powi(2);
                total_loss += loss;
            }

            let avg_loss = total_loss / batch_size as f64;
            model.loss_history.push(avg_loss);
        }

        Ok(())
    }

    /// Update reinforcement learning agent
    fn update_rl_agent(&mut self, features: &Array1<f64>, reward: f64) -> Result<()> {
        if let Some(ref mut agent) = self.rl_agent {
            let state_key = Self::features_to_state_key(features);

            // Simple Q-learning update
            // In practice, would implement more sophisticated RL algorithms

            agent.stats.episodes += 1;
            agent.stats.avg_reward = (agent.stats.avg_reward * (agent.stats.episodes - 1) as f64
                + reward)
                / agent.stats.episodes as f64;

            // Decay exploration rate
            agent.exploration_rate *= 0.995;
            agent.exploration_rate = agent.exploration_rate.max(0.01);
        }

        Ok(())
    }
}

/// Benchmark function for advanced ML error mitigation
pub fn benchmark_advanced_ml_error_mitigation() -> Result<()> {
    println!("Benchmarking Advanced ML Error Mitigation...");

    let config = AdvancedMLMitigationConfig::default();
    let mut mitigator = AdvancedMLErrorMitigator::new(config)?;

    // Create test circuit
    let mut circuit = InterfaceCircuit::new(4, 0);
    circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
    circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));
    circuit.add_gate(InterfaceGate::new(InterfaceGateType::RZ(0.5), vec![2]));

    // Simulate noisy measurements
    let noisy_measurements = Array1::from_vec(vec![0.48, 0.52, 0.47, 0.53, 0.49]);

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

    // Apply advanced ML mitigation
    let result = mitigator.mitigate_errors(&noisy_measurements, &circuit)?;

    let duration = start_time.elapsed();

    println!("✅ Advanced ML Error Mitigation Results:");
    println!("   Mitigated Value: {:.6}", result.mitigated_value);
    println!("   Confidence: {:.4}", result.confidence);
    println!("   Model Used: {}", result.model_used);
    println!("   Error Reduction: {:.4}", result.error_reduction);
    println!("   Computation Time: {:.2}ms", duration.as_millis());

    Ok(())
}

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

    #[test]
    fn test_advanced_ml_mitigator_creation() {
        let config = AdvancedMLMitigationConfig::default();
        let mitigator = AdvancedMLErrorMitigator::new(config);
        assert!(mitigator.is_ok());
    }

    #[test]
    fn test_feature_extraction() {
        let config = AdvancedMLMitigationConfig::default();
        let mitigator = AdvancedMLErrorMitigator::new(config).unwrap();

        let mut circuit = InterfaceCircuit::new(2, 0);
        circuit.add_gate(InterfaceGate::new(InterfaceGateType::Hadamard, vec![0]));
        circuit.add_gate(InterfaceGate::new(InterfaceGateType::CNOT, vec![0, 1]));

        let measurements = Array1::from_vec(vec![0.5, 0.5, 0.5]);
        let features = mitigator.extract_features(&circuit, &measurements);

        assert!(features.is_ok());
        let features = features.unwrap();
        assert!(features.len() > 0);
    }

    #[test]
    fn test_activation_functions() {
        let config = AdvancedMLMitigationConfig::default();
        let mitigator = AdvancedMLErrorMitigator::new(config).unwrap();

        // Test ReLU
        assert_eq!(
            mitigator.apply_activation(-1.0, ActivationFunction::ReLU),
            0.0
        );
        assert_eq!(
            mitigator.apply_activation(1.0, ActivationFunction::ReLU),
            1.0
        );

        // Test Sigmoid
        let sigmoid_result = mitigator.apply_activation(0.0, ActivationFunction::Sigmoid);
        assert!((sigmoid_result - 0.5).abs() < 1e-10);
    }

    #[test]
    fn test_mitigation_strategy_selection() {
        let config = AdvancedMLMitigationConfig::default();
        let mut mitigator = AdvancedMLErrorMitigator::new(config).unwrap();

        let features = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let strategy = mitigator.select_mitigation_strategy(&features);

        assert!(strategy.is_ok());
    }

    #[test]
    fn test_traditional_mitigation() {
        let config = AdvancedMLMitigationConfig::default();
        let mitigator = AdvancedMLErrorMitigator::new(config).unwrap();

        let measurements = Array1::from_vec(vec![0.48, 0.52, 0.49]);
        let circuit = InterfaceCircuit::new(2, 0);

        let result = mitigator.apply_traditional_mitigation(
            MitigationAction::ZeroNoiseExtrapolation,
            &measurements,
            &circuit,
        );

        assert!(result.is_ok());
    }
}