quantrs2_ml/

error_mitigation.rs

//! Advanced Error Mitigation for Quantum Machine Learning
//!
//! This module provides comprehensive error mitigation techniques specifically designed
//! for quantum machine learning applications, including noise-aware training,
//! error correction protocols, and adaptive mitigation strategies.

use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayD, Axis};
use std::collections::HashMap;
use std::sync::Arc;

/// Advanced error mitigation framework for quantum ML
pub struct QuantumMLErrorMitigator {
    pub mitigation_strategy: MitigationStrategy,
    pub noise_model: NoiseModel,
    pub calibration_data: CalibrationData,
    pub adaptive_config: AdaptiveConfig,
    pub performance_metrics: PerformanceMetrics,
}

/// Performance tracker for mitigation strategies
#[derive(Debug, Clone)]
pub struct PerformanceTracker {
    /// Performance metrics over time
    pub metrics_history: Vec<PerformanceMetrics>,
    /// Current performance
    pub current_performance: PerformanceMetrics,
}

/// Error mitigation strategies for quantum ML
#[derive(Debug, Clone)]
pub enum MitigationStrategy {
    /// Zero Noise Extrapolation
    ZNE {
        scale_factors: Vec<f64>,
        extrapolation_method: ExtrapolationMethod,
        circuit_folding: CircuitFoldingMethod,
    },
    /// Readout Error Mitigation
    ReadoutErrorMitigation {
        calibration_matrix: Array2<f64>,
        correction_method: ReadoutCorrectionMethod,
        regularization: f64,
    },
    /// Clifford Data Regression
    CDR {
        training_circuits: Vec<CliffordCircuit>,
        regression_model: CDRModel,
        feature_extraction: FeatureExtractionMethod,
    },
    /// Symmetry Verification
    SymmetryVerification {
        symmetry_groups: Vec<SymmetryGroup>,
        verification_circuits: Vec<VerificationCircuit>,
        post_selection: bool,
    },
    /// Virtual Distillation
    VirtualDistillation {
        distillation_rounds: usize,
        entanglement_protocol: EntanglementProtocol,
        purification_threshold: f64,
    },
    /// Machine Learning-based Mitigation
    MLMitigation {
        noise_predictor: NoisePredictorModel,
        correction_network: CorrectionNetwork,
        training_data: TrainingDataSet,
    },
    /// Hybrid Classical-Quantum Error Correction
    HybridErrorCorrection {
        classical_preprocessing: ClassicalPreprocessor,
        quantum_correction: QuantumErrorCorrector,
        post_processing: ClassicalPostprocessor,
    },
    /// Adaptive Multi-Strategy
    AdaptiveMultiStrategy {
        strategies: Vec<MitigationStrategy>,
        selection_policy: StrategySelectionPolicy,
        performance_tracker: PerformanceTracker,
    },
}

/// Noise models for quantum devices
#[derive(Debug, Clone)]
pub struct NoiseModel {
    pub gate_errors: HashMap<String, GateErrorModel>,
    pub measurement_errors: MeasurementErrorModel,
    pub coherence_times: CoherenceTimeModel,
    pub crosstalk_matrix: Array2<f64>,
    pub temporal_correlations: TemporalCorrelationModel,
}

/// Gate error models
#[derive(Debug, Clone)]
pub struct GateErrorModel {
    pub error_rate: f64,
    pub error_type: ErrorType,
    pub coherence_limited: bool,
    pub gate_time: f64,
    pub fidelity_model: FidelityModel,
}

/// Types of quantum errors
#[derive(Debug, Clone)]
pub enum ErrorType {
    Depolarizing {
        strength: f64,
    },
    Amplitude {
        damping_rate: f64,
    },
    Phase {
        dephasing_rate: f64,
    },
    Pauli {
        px: f64,
        py: f64,
        pz: f64,
    },
    Coherent {
        rotation_angle: f64,
        rotation_axis: Array1<f64>,
    },
    Correlated {
        correlation_matrix: Array2<f64>,
    },
}

/// Measurement error model
#[derive(Debug, Clone)]
pub struct MeasurementErrorModel {
    pub readout_fidelity: f64,
    pub assignment_matrix: Array2<f64>,
    pub state_preparation_errors: Array1<f64>,
    pub measurement_crosstalk: Array2<f64>,
}

/// Coherence time parameters
#[derive(Debug, Clone)]
pub struct CoherenceTimeModel {
    pub t1_times: Array1<f64>,      // Relaxation times
    pub t2_times: Array1<f64>,      // Dephasing times
    pub t2_echo_times: Array1<f64>, // Echo dephasing times
    pub temporal_fluctuations: TemporalFluctuation,
}

/// Temporal correlation model for noise
#[derive(Debug, Clone)]
pub struct TemporalCorrelationModel {
    pub correlation_function: CorrelationFunction,
    pub correlation_time: f64,
    pub noise_spectrum: NoiseSpectrum,
}

/// Calibration data for error mitigation
#[derive(Debug, Clone)]
pub struct CalibrationData {
    pub process_tomography: HashMap<String, ProcessMatrix>,
    pub state_tomography: HashMap<String, StateMatrix>,
    pub randomized_benchmarking: RBData,
    pub gate_set_tomography: GSTData,
    pub noise_spectroscopy: SpectroscopyData,
}

/// Adaptive configuration for dynamic error mitigation
#[derive(Debug, Clone)]
pub struct AdaptiveConfig {
    pub adaptation_frequency: usize,
    pub performance_threshold: f64,
    pub strategy_switching_policy: SwitchingPolicy,
    pub online_calibration: bool,
    pub feedback_mechanism: FeedbackMechanism,
}

impl QuantumMLErrorMitigator {
    /// Create a new error mitigation framework
    pub fn new(mitigation_strategy: MitigationStrategy, noise_model: NoiseModel) -> Result<Self> {
        let calibration_data = CalibrationData::default();
        let adaptive_config = AdaptiveConfig::default();
        let performance_metrics = PerformanceMetrics::new();

        Ok(Self {
            mitigation_strategy,
            noise_model,
            calibration_data,
            adaptive_config,
            performance_metrics,
        })
    }

    /// Apply error mitigation to quantum ML training
    pub fn mitigate_training_errors(
        &mut self,
        circuit: &QuantumCircuit,
        parameters: &Array1<f64>,
        measurement_results: &Array2<f64>,
        gradient_estimates: &Array1<f64>,
    ) -> Result<MitigatedTrainingData> {
        // Update noise model based on current measurements
        self.update_noise_model(measurement_results)?;

        // Apply mitigation strategy
        let mitigated_measurements =
            self.apply_measurement_mitigation(circuit, measurement_results)?;

        let mitigated_gradients =
            self.apply_gradient_mitigation(circuit, parameters, gradient_estimates)?;

        // Update performance metrics
        self.performance_metrics
            .update(&mitigated_measurements, &mitigated_gradients)?;

        // Adaptive strategy adjustment
        if self.should_adapt_strategy()? {
            self.adapt_mitigation_strategy()?;
        }

        Ok(MitigatedTrainingData {
            measurements: mitigated_measurements,
            gradients: mitigated_gradients,
            confidence_scores: self.compute_confidence_scores(circuit)?,
            mitigation_overhead: self.performance_metrics.mitigation_overhead,
        })
    }

    /// Apply error mitigation to quantum ML inference
    pub fn mitigate_inference_errors(
        &mut self,
        circuit: &QuantumCircuit,
        measurement_results: &Array2<f64>,
    ) -> Result<MitigatedInferenceData> {
        let mitigated_measurements =
            self.apply_measurement_mitigation(circuit, measurement_results)?;

        let uncertainty_estimates =
            self.compute_uncertainty_estimates(circuit, &mitigated_measurements)?;

        Ok(MitigatedInferenceData {
            measurements: mitigated_measurements,
            uncertainty: uncertainty_estimates,
            reliability_score: self.compute_reliability_score(circuit)?,
        })
    }

    /// Apply measurement error mitigation
    fn apply_measurement_mitigation(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        match &self.mitigation_strategy {
            MitigationStrategy::ZNE {
                scale_factors,
                extrapolation_method,
                ..
            } => self.apply_zne_mitigation(
                circuit,
                measurements,
                scale_factors,
                extrapolation_method,
            ),
            MitigationStrategy::ReadoutErrorMitigation {
                calibration_matrix,
                correction_method,
                ..
            } => self.apply_readout_error_mitigation(
                measurements,
                calibration_matrix,
                correction_method,
            ),
            MitigationStrategy::CDR {
                training_circuits,
                regression_model,
                ..
            } => self.apply_cdr_mitigation(
                circuit,
                measurements,
                training_circuits,
                regression_model,
            ),
            MitigationStrategy::SymmetryVerification {
                symmetry_groups, ..
            } => self.apply_symmetry_verification(circuit, measurements, symmetry_groups),
            MitigationStrategy::VirtualDistillation {
                distillation_rounds,
                ..
            } => self.apply_virtual_distillation(circuit, measurements, *distillation_rounds),
            MitigationStrategy::MLMitigation {
                noise_predictor,
                correction_network,
                ..
            } => {
                self.apply_ml_mitigation(circuit, measurements, noise_predictor, correction_network)
            }
            MitigationStrategy::HybridErrorCorrection {
                classical_preprocessing,
                quantum_correction,
                post_processing,
            } => self.apply_hybrid_error_correction(
                circuit,
                measurements,
                classical_preprocessing,
                quantum_correction,
                post_processing,
            ),
            MitigationStrategy::AdaptiveMultiStrategy {
                strategies,
                selection_policy,
                ..
            } => self.apply_adaptive_multi_strategy(
                circuit,
                measurements,
                strategies,
                selection_policy,
            ),
        }
    }

    /// Apply Zero Noise Extrapolation
    fn apply_zne_mitigation(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        scale_factors: &[f64],
        extrapolation_method: &ExtrapolationMethod,
    ) -> Result<Array2<f64>> {
        let mut scaled_results = Vec::new();

        for &scale_factor in scale_factors {
            let scaled_circuit = self.scale_circuit_noise(circuit, scale_factor)?;
            let scaled_measurements = self.execute_scaled_circuit(&scaled_circuit)?;
            scaled_results.push((scale_factor, scaled_measurements));
        }

        // Extrapolate to zero noise
        self.extrapolate_to_zero_noise(&scaled_results, extrapolation_method)
    }

    /// Apply readout error mitigation
    fn apply_readout_error_mitigation(
        &self,
        measurements: &Array2<f64>,
        calibration_matrix: &Array2<f64>,
        correction_method: &ReadoutCorrectionMethod,
    ) -> Result<Array2<f64>> {
        match correction_method {
            ReadoutCorrectionMethod::MatrixInversion => {
                self.apply_matrix_inversion_correction(measurements, calibration_matrix)
            }
            ReadoutCorrectionMethod::ConstrainedLeastSquares => {
                self.apply_constrained_least_squares_correction(measurements, calibration_matrix)
            }
            ReadoutCorrectionMethod::IterativeMaximumLikelihood => {
                self.apply_ml_correction(measurements, calibration_matrix)
            }
        }
    }

    /// Apply Clifford Data Regression
    fn apply_cdr_mitigation(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        training_circuits: &[CliffordCircuit],
        regression_model: &CDRModel,
    ) -> Result<Array2<f64>> {
        // Extract features from the circuit
        let circuit_features = self.extract_circuit_features(circuit)?;

        // Generate training data from Clifford circuits
        let training_features = self.generate_training_features(training_circuits)?;
        let training_labels = self.execute_clifford_circuits(training_circuits)?;

        // Train regression model
        let trained_model = regression_model.train(&training_features, &training_labels)?;

        // Predict error-free expectation values
        let predicted_values = trained_model.predict(&circuit_features)?;

        // Apply correction
        self.apply_cdr_correction(measurements, &predicted_values)
    }

    /// Apply symmetry verification
    fn apply_symmetry_verification(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        symmetry_groups: &[SymmetryGroup],
    ) -> Result<Array2<f64>> {
        let mut verified_measurements = measurements.clone();

        for symmetry_group in symmetry_groups {
            let symmetry_violations =
                self.detect_symmetry_violations(circuit, &verified_measurements, symmetry_group)?;

            verified_measurements = self.apply_symmetry_constraints(
                &verified_measurements,
                &symmetry_violations,
                symmetry_group,
            )?;
        }

        Ok(verified_measurements)
    }

    /// Apply virtual distillation
    fn apply_virtual_distillation(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        distillation_rounds: usize,
    ) -> Result<Array2<f64>> {
        let mut distilled_measurements = measurements.clone();

        for _ in 0..distillation_rounds {
            // Create virtual copies with different random states
            let virtual_copies = self.create_virtual_copies(circuit, 2)?;

            // Measure entanglement between copies
            let entanglement_measures = self.measure_virtual_entanglement(&virtual_copies)?;

            // Apply distillation protocol
            distilled_measurements =
                self.apply_distillation_protocol(&distilled_measurements, &entanglement_measures)?;
        }

        Ok(distilled_measurements)
    }

    /// Apply ML-based error mitigation
    fn apply_ml_mitigation(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        noise_predictor: &NoisePredictorModel,
        correction_network: &CorrectionNetwork,
    ) -> Result<Array2<f64>> {
        // Predict noise characteristics
        let circuit_features = self.extract_circuit_features(circuit)?;
        let predicted_noise = noise_predictor.predict(&circuit_features)?;

        // Apply neural network correction
        let correction_input = self.prepare_correction_input(measurements, &predicted_noise)?;
        let corrections = correction_network.forward(&correction_input)?;

        // Apply corrections to measurements
        Ok(measurements + &corrections)
    }

    /// Apply hybrid error correction
    fn apply_hybrid_error_correction(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        classical_preprocessing: &ClassicalPreprocessor,
        quantum_correction: &QuantumErrorCorrector,
        post_processing: &ClassicalPostprocessor,
    ) -> Result<Array2<f64>> {
        // Classical preprocessing
        let preprocessed_data = classical_preprocessing.process(measurements)?;

        // Quantum error correction
        let quantum_corrected = quantum_correction.correct(circuit, &preprocessed_data)?;

        // Classical post-processing
        post_processing.process(&quantum_corrected)
    }

    /// Apply adaptive multi-strategy mitigation
    fn apply_adaptive_multi_strategy(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        strategies: &[MitigationStrategy],
        selection_policy: &StrategySelectionPolicy,
    ) -> Result<Array2<f64>> {
        // Select best strategy based on current circuit and performance history
        let selected_strategy =
            selection_policy.select_strategy(circuit, &self.performance_metrics, strategies)?;

        // Apply selected strategy
        let mitigator = QuantumMLErrorMitigator {
            mitigation_strategy: selected_strategy,
            noise_model: self.noise_model.clone(),
            calibration_data: self.calibration_data.clone(),
            adaptive_config: self.adaptive_config.clone(),
            performance_metrics: self.performance_metrics.clone(),
        };

        mitigator.apply_measurement_mitigation(circuit, measurements)
    }

    /// Apply gradient error mitigation
    fn apply_gradient_mitigation(
        &self,
        circuit: &QuantumCircuit,
        parameters: &Array1<f64>,
        gradients: &Array1<f64>,
    ) -> Result<Array1<f64>> {
        // Parameter shift rule with error mitigation
        let mut mitigated_gradients = Array1::zeros(gradients.len());

        for (i, &param) in parameters.iter().enumerate() {
            // Create shifted circuits
            let mut params_plus = parameters.clone();
            let mut params_minus = parameters.clone();
            params_plus[i] = param + std::f64::consts::PI / 2.0;
            params_minus[i] = param - std::f64::consts::PI / 2.0;

            // Apply error mitigation to shifted measurements
            let circuit_plus = circuit.with_parameters(&params_plus)?;
            let circuit_minus = circuit.with_parameters(&params_minus)?;

            let measurements_plus = self.measure_circuit(&circuit_plus)?;
            let measurements_minus = self.measure_circuit(&circuit_minus)?;

            let mitigated_plus =
                self.apply_measurement_mitigation(&circuit_plus, &measurements_plus)?;
            let mitigated_minus =
                self.apply_measurement_mitigation(&circuit_minus, &measurements_minus)?;

            // Compute mitigated gradient
            mitigated_gradients[i] = (mitigated_plus.mean().unwrap_or(0.0)
                - mitigated_minus.mean().unwrap_or(0.0))
                / 2.0;
        }

        Ok(mitigated_gradients)
    }

    /// Update noise model based on current measurements
    fn update_noise_model(&mut self, measurements: &Array2<f64>) -> Result<()> {
        // Analyze measurement statistics to infer noise characteristics
        let noise_statistics = self.analyze_noise_statistics(measurements)?;

        // Update gate error models
        for (gate_name, error_model) in &mut self.noise_model.gate_errors {
            error_model.update_from_statistics(&noise_statistics)?;
        }

        // Update measurement error model
        self.noise_model
            .measurement_errors
            .update_from_measurements(measurements)?;

        Ok(())
    }

    /// Check if mitigation strategy should be adapted
    fn should_adapt_strategy(&self) -> Result<bool> {
        let current_performance = self.performance_metrics.current_performance();
        let adaptation_threshold = self.adaptive_config.performance_threshold;

        Ok(current_performance < adaptation_threshold)
    }

    /// Adapt mitigation strategy based on performance
    fn adapt_mitigation_strategy(&mut self) -> Result<()> {
        match &self.adaptive_config.strategy_switching_policy {
            SwitchingPolicy::PerformanceBased => {
                self.switch_to_best_performing_strategy()?;
            }
            SwitchingPolicy::ResourceOptimized => {
                self.switch_to_resource_optimal_strategy()?;
            }
            SwitchingPolicy::HybridAdaptive => {
                self.switch_to_hybrid_adaptive_strategy()?;
            }
        }

        Ok(())
    }

    /// Compute confidence scores for mitigation results
    fn compute_confidence_scores(&self, circuit: &QuantumCircuit) -> Result<Array1<f64>> {
        let circuit_complexity = self.assess_circuit_complexity(circuit)?;
        let noise_level = self.estimate_noise_level(circuit)?;
        let mitigation_effectiveness = self.estimate_mitigation_effectiveness()?;

        let base_confidence = 1.0 - (circuit_complexity * noise_level);
        let adjusted_confidence = base_confidence * mitigation_effectiveness;

        Ok(Array1::from_elem(circuit.num_qubits(), adjusted_confidence))
    }

    /// Compute uncertainty estimates
    fn compute_uncertainty_estimates(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
    ) -> Result<Array1<f64>> {
        // Bootstrap sampling for uncertainty estimation
        let num_bootstrap_samples = 1000;
        let mut bootstrap_results = Vec::new();

        for _ in 0..num_bootstrap_samples {
            let bootstrap_measurements = self.bootstrap_sample(measurements)?;
            let mitigated_bootstrap =
                self.apply_measurement_mitigation(circuit, &bootstrap_measurements)?;
            bootstrap_results.push(mitigated_bootstrap.mean().unwrap_or(0.0));
        }

        // Compute standard deviation as uncertainty
        let mean_result = bootstrap_results.iter().sum::<f64>() / bootstrap_results.len() as f64;
        let variance = bootstrap_results
            .iter()
            .map(|&x| (x - mean_result).powi(2))
            .sum::<f64>()
            / bootstrap_results.len() as f64;
        let uncertainty = variance.sqrt();

        Ok(Array1::from_elem(1, uncertainty))
    }

    /// Compute reliability score
    fn compute_reliability_score(&self, circuit: &QuantumCircuit) -> Result<f64> {
        let mitigation_fidelity = self.estimate_mitigation_fidelity(circuit)?;
        let noise_resilience = self.assess_noise_resilience(circuit)?;
        let calibration_quality = self.assess_calibration_quality()?;

        Ok(mitigation_fidelity * noise_resilience * calibration_quality)
    }

    // Helper methods for implementation details...

    fn scale_circuit_noise(
        &self,
        circuit: &QuantumCircuit,
        scale_factor: f64,
    ) -> Result<QuantumCircuit> {
        // Implement noise scaling by circuit folding or gate repetition
        // This is a placeholder implementation
        Ok(circuit.clone())
    }

    fn execute_scaled_circuit(&self, circuit: &QuantumCircuit) -> Result<Array2<f64>> {
        // Execute circuit with scaled noise and return measurements
        // This is a placeholder implementation
        Ok(Array2::zeros((100, circuit.num_qubits())))
    }

    fn extrapolate_to_zero_noise(
        &self,
        scaled_results: &[(f64, Array2<f64>)],
        extrapolation_method: &ExtrapolationMethod,
    ) -> Result<Array2<f64>> {
        // Implement polynomial or exponential extrapolation
        // This is a placeholder implementation
        Ok(scaled_results[0].1.clone())
    }

    fn measure_circuit(&self, circuit: &QuantumCircuit) -> Result<Array2<f64>> {
        // Execute circuit and return measurements
        // This is a placeholder implementation
        Ok(Array2::zeros((100, circuit.num_qubits())))
    }

    fn analyze_noise_statistics(&self, measurements: &Array2<f64>) -> Result<NoiseStatistics> {
        // Analyze measurement data to extract noise characteristics
        Ok(NoiseStatistics::default())
    }

    fn assess_circuit_complexity(&self, circuit: &QuantumCircuit) -> Result<f64> {
        // Assess circuit complexity for confidence scoring
        Ok(0.5) // Placeholder
    }

    fn estimate_noise_level(&self, circuit: &QuantumCircuit) -> Result<f64> {
        // Estimate current noise level
        Ok(0.1) // Placeholder
    }

    fn estimate_mitigation_effectiveness(&self) -> Result<f64> {
        // Estimate how effective the current mitigation strategy is
        Ok(0.8) // Placeholder
    }

    fn bootstrap_sample(&self, measurements: &Array2<f64>) -> Result<Array2<f64>> {
        // Create bootstrap sample from measurements
        Ok(measurements.clone()) // Placeholder
    }

    fn estimate_mitigation_fidelity(&self, circuit: &QuantumCircuit) -> Result<f64> {
        // Estimate fidelity of mitigation process
        Ok(0.9) // Placeholder
    }

    fn assess_noise_resilience(&self, circuit: &QuantumCircuit) -> Result<f64> {
        // Assess how resilient the circuit is to noise
        Ok(0.7) // Placeholder
    }

    fn assess_calibration_quality(&self) -> Result<f64> {
        // Assess quality of calibration data
        Ok(0.85) // Placeholder
    }

    // Additional helper methods would be implemented here...
}

// Supporting structures and implementations...

#[derive(Debug, Clone)]
pub struct MitigatedTrainingData {
    pub measurements: Array2<f64>,
    pub gradients: Array1<f64>,
    pub confidence_scores: Array1<f64>,
    pub mitigation_overhead: f64,
}

#[derive(Debug, Clone)]
pub struct MitigatedInferenceData {
    pub measurements: Array2<f64>,
    pub uncertainty: Array1<f64>,
    pub reliability_score: f64,
}

#[derive(Debug, Clone)]
pub enum ExtrapolationMethod {
    Polynomial { degree: usize },
    Exponential { exponential_form: ExponentialForm },
    Richardson { orders: Vec<usize> },
    Adaptive { method_selection: MethodSelection },
}

#[derive(Debug, Clone)]
pub enum ReadoutCorrectionMethod {
    MatrixInversion,
    ConstrainedLeastSquares,
    IterativeMaximumLikelihood,
}

#[derive(Debug, Clone)]
pub enum CircuitFoldingMethod {
    GlobalFolding,
    LocalFolding { gate_priorities: Vec<String> },
    ParametricFolding { scaling_function: ScalingFunction },
}

// Additional supporting types and implementations...

#[derive(Debug, Clone)]
pub struct QuantumCircuit {
    pub gates: Vec<QuantumGate>,
    pub qubits: usize,
}

impl QuantumCircuit {
    pub fn num_qubits(&self) -> usize {
        self.qubits
    }

    pub fn with_parameters(&self, params: &Array1<f64>) -> Result<Self> {
        // Create circuit with new parameters
        Ok(self.clone())
    }

    pub fn clone(&self) -> Self {
        Self {
            gates: self.gates.clone(),
            qubits: self.qubits,
        }
    }
}

#[derive(Debug, Clone)]
pub struct QuantumGate {
    pub name: String,
    pub qubits: Vec<usize>,
    pub parameters: Array1<f64>,
}

// Default implementations
impl Default for CalibrationData {
    fn default() -> Self {
        Self {
            process_tomography: HashMap::new(),
            state_tomography: HashMap::new(),
            randomized_benchmarking: RBData::default(),
            gate_set_tomography: GSTData::default(),
            noise_spectroscopy: SpectroscopyData::default(),
        }
    }
}

impl Default for AdaptiveConfig {
    fn default() -> Self {
        Self {
            adaptation_frequency: 100,
            performance_threshold: 0.8,
            strategy_switching_policy: SwitchingPolicy::PerformanceBased,
            online_calibration: true,
            feedback_mechanism: FeedbackMechanism::default(),
        }
    }
}

impl Default for PerformanceTracker {
    fn default() -> Self {
        Self {
            metrics_history: Vec::new(),
            current_performance: PerformanceMetrics::new(),
        }
    }
}

// Additional placeholder structures for compilation
#[derive(Debug, Clone, Default)]
pub struct ProcessMatrix;

#[derive(Debug, Clone, Default)]
pub struct StateMatrix;

#[derive(Debug, Clone, Default)]
pub struct RBData;

#[derive(Debug, Clone, Default)]
pub struct GSTData;

#[derive(Debug, Clone, Default)]
pub struct SpectroscopyData;

#[derive(Debug, Clone)]
pub enum SwitchingPolicy {
    PerformanceBased,
    ResourceOptimized,
    HybridAdaptive,
}

#[derive(Debug, Clone, Default)]
pub struct FeedbackMechanism;

#[derive(Debug, Clone, Default)]
pub struct PerformanceMetrics {
    pub mitigation_overhead: f64,
}

impl PerformanceMetrics {
    pub fn new() -> Self {
        Self {
            mitigation_overhead: 0.1,
        }
    }

    pub fn update(&mut self, _measurements: &Array2<f64>, _gradients: &Array1<f64>) -> Result<()> {
        // Update performance metrics
        Ok(())
    }

    pub fn current_performance(&self) -> f64 {
        0.85 // Placeholder
    }
}

// Additional placeholder types for full compilation
#[derive(Debug, Clone)]
pub struct CliffordCircuit;

#[derive(Debug, Clone)]
pub struct CDRModel;

#[derive(Debug, Clone)]
pub enum FeatureExtractionMethod {
    CircuitDepth,
    GateCount,
    EntanglementStructure,
}

#[derive(Debug, Clone)]
pub struct SymmetryGroup;

#[derive(Debug, Clone)]
pub struct VerificationCircuit;

#[derive(Debug, Clone)]
pub enum EntanglementProtocol {
    Bell,
    GHZ,
    Cluster,
}

#[derive(Debug, Clone)]
pub struct NoisePredictorModel;

#[derive(Debug, Clone)]
pub struct CorrectionNetwork;

#[derive(Debug, Clone)]
pub struct TrainingDataSet;

#[derive(Debug, Clone)]
pub struct ClassicalPreprocessor;

#[derive(Debug, Clone)]
pub struct QuantumErrorCorrector;

#[derive(Debug, Clone)]
pub struct ClassicalPostprocessor;

#[derive(Debug, Clone)]
pub struct StrategySelectionPolicy;

#[derive(Debug, Clone)]
pub enum ExponentialForm {
    SingleExponential,
    DoubleExponential,
    Stretched,
}

#[derive(Debug, Clone)]
pub enum MethodSelection {
    CrossValidation,
    BayesianOptimization,
    AdaptiveGrid,
}

#[derive(Debug, Clone)]
pub enum ScalingFunction {
    Linear,
    Polynomial,
    Exponential,
}

#[derive(Debug, Clone)]
pub struct FidelityModel;

#[derive(Debug, Clone)]
pub struct TemporalFluctuation;

#[derive(Debug, Clone)]
pub enum CorrelationFunction {
    Exponential,
    Gaussian,
    PowerLaw,
}

#[derive(Debug, Clone)]
pub struct NoiseSpectrum;

#[derive(Debug, Clone, Default)]
pub struct NoiseStatistics;

// Additional implementation methods for supporting types
impl GateErrorModel {
    pub fn update_from_statistics(&mut self, _stats: &NoiseStatistics) -> Result<()> {
        // Update gate error model from statistics
        Ok(())
    }
}

impl MeasurementErrorModel {
    pub fn update_from_measurements(&mut self, _measurements: &Array2<f64>) -> Result<()> {
        // Update measurement error model
        Ok(())
    }
}

impl QuantumMLErrorMitigator {
    // Additional helper method implementations
    fn apply_matrix_inversion_correction(
        &self,
        measurements: &Array2<f64>,
        calibration_matrix: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // Implement matrix inversion correction
        Ok(measurements.clone()) // Placeholder
    }

    fn apply_constrained_least_squares_correction(
        &self,
        measurements: &Array2<f64>,
        calibration_matrix: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // Implement constrained least squares correction
        Ok(measurements.clone()) // Placeholder
    }

    fn apply_ml_correction(
        &self,
        measurements: &Array2<f64>,
        calibration_matrix: &Array2<f64>,
    ) -> Result<Array2<f64>> {
        // Implement maximum likelihood correction
        Ok(measurements.clone()) // Placeholder
    }

    fn extract_circuit_features(&self, circuit: &QuantumCircuit) -> Result<Array1<f64>> {
        // Extract features from quantum circuit
        Ok(Array1::zeros(10)) // Placeholder
    }

    fn generate_training_features(&self, circuits: &[CliffordCircuit]) -> Result<Array2<f64>> {
        // Generate training features from Clifford circuits
        Ok(Array2::zeros((circuits.len(), 10))) // Placeholder
    }

    fn execute_clifford_circuits(&self, circuits: &[CliffordCircuit]) -> Result<Array1<f64>> {
        // Execute Clifford circuits and return results
        Ok(Array1::zeros(circuits.len())) // Placeholder
    }

    fn apply_cdr_correction(
        &self,
        measurements: &Array2<f64>,
        predicted_values: &Array1<f64>,
    ) -> Result<Array2<f64>> {
        // Apply CDR correction
        Ok(measurements.clone()) // Placeholder
    }

    fn detect_symmetry_violations(
        &self,
        circuit: &QuantumCircuit,
        measurements: &Array2<f64>,
        symmetry_group: &SymmetryGroup,
    ) -> Result<Array1<f64>> {
        // Detect symmetry violations
        Ok(Array1::zeros(measurements.nrows())) // Placeholder
    }

    fn apply_symmetry_constraints(
        &self,
        measurements: &Array2<f64>,
        violations: &Array1<f64>,
        symmetry_group: &SymmetryGroup,
    ) -> Result<Array2<f64>> {
        // Apply symmetry constraints
        Ok(measurements.clone()) // Placeholder
    }

    fn create_virtual_copies(
        &self,
        circuit: &QuantumCircuit,
        num_copies: usize,
    ) -> Result<Vec<QuantumCircuit>> {
        // Create virtual copies of circuit
        Ok(vec![circuit.clone(); num_copies]) // Placeholder
    }

    fn measure_virtual_entanglement(&self, circuits: &[QuantumCircuit]) -> Result<Array1<f64>> {
        // Measure entanglement between virtual copies
        Ok(Array1::zeros(circuits.len())) // Placeholder
    }

    fn apply_distillation_protocol(
        &self,
        measurements: &Array2<f64>,
        entanglement_measures: &Array1<f64>,
    ) -> Result<Array2<f64>> {
        // Apply virtual distillation protocol
        Ok(measurements.clone()) // Placeholder
    }

    fn prepare_correction_input(
        &self,
        measurements: &Array2<f64>,
        predicted_noise: &Array1<f64>,
    ) -> Result<Array2<f64>> {
        // Prepare input for correction network
        Ok(measurements.clone()) // Placeholder
    }

    fn switch_to_best_performing_strategy(&mut self) -> Result<()> {
        // Switch to best performing mitigation strategy
        Ok(())
    }

    fn switch_to_resource_optimal_strategy(&mut self) -> Result<()> {
        // Switch to most resource efficient strategy
        Ok(())
    }

    fn switch_to_hybrid_adaptive_strategy(&mut self) -> Result<()> {
        // Switch to hybrid adaptive strategy
        Ok(())
    }
}

// Trait implementations for ML models
impl CDRModel {
    pub fn train(&self, features: &Array2<f64>, labels: &Array1<f64>) -> Result<TrainedCDRModel> {
        // Train CDR regression model
        Ok(TrainedCDRModel::default())
    }
}

#[derive(Debug, Clone, Default)]
pub struct TrainedCDRModel;

impl TrainedCDRModel {
    pub fn predict(&self, features: &Array1<f64>) -> Result<Array1<f64>> {
        // Predict using trained CDR model
        Ok(Array1::zeros(features.len())) // Placeholder
    }
}

impl NoisePredictorModel {
    pub fn predict(&self, features: &Array1<f64>) -> Result<Array1<f64>> {
        // Predict noise characteristics
        Ok(Array1::zeros(features.len())) // Placeholder
    }
}

impl CorrectionNetwork {
    pub fn forward(&self, input: &Array2<f64>) -> Result<Array2<f64>> {
        // Forward pass through correction network
        Ok(Array2::zeros(input.dim())) // Placeholder
    }
}

impl ClassicalPreprocessor {
    pub fn process(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        // Classical preprocessing
        Ok(data.clone()) // Placeholder
    }
}

impl QuantumErrorCorrector {
    pub fn correct(&self, circuit: &QuantumCircuit, data: &Array2<f64>) -> Result<Array2<f64>> {
        // Quantum error correction
        Ok(data.clone()) // Placeholder
    }
}

impl ClassicalPostprocessor {
    pub fn process(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        // Classical post-processing
        Ok(data.clone()) // Placeholder
    }
}

impl StrategySelectionPolicy {
    pub fn select_strategy(
        &self,
        circuit: &QuantumCircuit,
        metrics: &PerformanceMetrics,
        strategies: &[MitigationStrategy],
    ) -> Result<MitigationStrategy> {
        // Select best strategy based on policy
        Ok(strategies[0].clone()) // Placeholder
    }
}