quantrs2_device/mid_circuit_measurements/

executor.rs

//! Main executor for mid-circuit measurements

use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

use quantrs2_circuit::{
    classical::{ClassicalCondition, ClassicalValue, ComparisonOp},
    measurement::{CircuitOp, FeedForward, Measurement, MeasurementCircuit},
};
use quantrs2_core::{error::QuantRS2Result, gate::GateOp, qubit::QubitId};
use tokio::sync::Mutex as AsyncMutex;

use crate::{
    calibration::CalibrationManager,
    translation::{GateTranslator, HardwareBackend},
    DeviceError, DeviceResult,
};

use super::analytics::AdvancedAnalyticsEngine;
use super::config::MidCircuitConfig;
use super::ml::{AdaptiveMeasurementManager, MLOptimizer, MeasurementPredictor};
use super::monitoring::{OptimizationCache, PerformanceMonitor};
use super::results::*;

/// Trait for device-specific mid-circuit measurement execution
#[async_trait::async_trait]
pub trait MidCircuitDeviceExecutor {
    /// Get device identifier
    fn device_id(&self) -> &str;

    /// Execute a quantum gate
    async fn execute_gate(&self, gate: &dyn GateOp) -> DeviceResult<()>;

    /// Measure a specific qubit
    async fn measure_qubit(&self, qubit: QubitId) -> DeviceResult<u8>;

    /// Measure all qubits
    async fn measure_all(&self) -> DeviceResult<HashMap<String, usize>>;

    /// Synchronize execution (barrier)
    async fn synchronize(&self) -> DeviceResult<()>;

    /// Reset a qubit to |0⟩ state
    async fn reset_qubit(&self, qubit: QubitId) -> DeviceResult<()>;
}

/// Validation result for circuits
#[derive(Debug, Clone)]
pub struct ValidationResult {
    pub is_valid: bool,
    pub warnings: Vec<String>,
    pub errors: Vec<String>,
    pub recommendations: Vec<String>,
}

/// Advanced SciRS2-powered mid-circuit measurement executor
pub struct MidCircuitExecutor {
    config: MidCircuitConfig,
    calibration_manager: CalibrationManager,
    capabilities: Option<MidCircuitCapabilities>,
    gate_translator: GateTranslator,

    // Advanced analytics components
    analytics_engine: Arc<RwLock<AdvancedAnalyticsEngine>>,
    ml_optimizer: Arc<AsyncMutex<MLOptimizer>>,
    predictor: Arc<AsyncMutex<MeasurementPredictor>>,
    adaptive_manager: Arc<AsyncMutex<AdaptiveMeasurementManager>>,

    // Performance monitoring
    performance_monitor: Arc<RwLock<PerformanceMonitor>>,
    measurement_history: Arc<RwLock<VecDeque<MeasurementEvent>>>,
    optimization_cache: Arc<RwLock<OptimizationCache>>,
}

impl MidCircuitExecutor {
    /// Create a new advanced mid-circuit measurement executor
    pub fn new(config: MidCircuitConfig, calibration_manager: CalibrationManager) -> Self {
        Self {
            config: config.clone(),
            calibration_manager,
            capabilities: None,
            gate_translator: GateTranslator::new(),
            analytics_engine: Arc::new(RwLock::new(AdvancedAnalyticsEngine::new(
                &config.analytics_config,
            ))),
            ml_optimizer: Arc::new(AsyncMutex::new(MLOptimizer::new(
                &config.ml_optimization_config,
            ))),
            predictor: Arc::new(AsyncMutex::new(MeasurementPredictor::new(
                &config.prediction_config,
            ))),
            adaptive_manager: Arc::new(AsyncMutex::new(AdaptiveMeasurementManager::new(
                &config.adaptive_config,
            ))),
            performance_monitor: Arc::new(RwLock::new(PerformanceMonitor::new())),
            measurement_history: Arc::new(RwLock::new(VecDeque::with_capacity(10000))),
            optimization_cache: Arc::new(RwLock::new(OptimizationCache::new())),
        }
    }

    /// Query and cache device capabilities for mid-circuit measurements
    pub fn query_capabilities(
        &mut self,
        backend: HardwareBackend,
        device_id: &str,
    ) -> DeviceResult<&MidCircuitCapabilities> {
        let backend_caps = self.query_backend_capabilities(backend);

        let capabilities = MidCircuitCapabilities {
            max_measurements: backend_caps.max_mid_circuit_measurements,
            supported_measurement_types: self.get_supported_measurement_types(backend)?,
            classical_register_capacity: backend_caps.classical_register_size.unwrap_or(64),
            max_classical_processing_time: 1000.0, // 1ms default
            realtime_feedback: backend_caps.supports_real_time_feedback.unwrap_or(false),
            parallel_measurements: backend_caps.supports_parallel_execution.unwrap_or(false),
            native_protocols: self.get_native_protocols(backend),
            timing_constraints: self.get_timing_constraints(backend, device_id)?,
        };

        self.capabilities = Some(capabilities);
        // Safe to expect: we just set capabilities to Some above
        Ok(self.capabilities.as_ref().expect("capabilities just set"))
    }

    /// Validate a measurement circuit against device capabilities
    pub fn validate_circuit<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        device_id: &str,
    ) -> DeviceResult<ValidationResult> {
        let mut validation_result = ValidationResult {
            is_valid: true,
            warnings: Vec::new(),
            errors: Vec::new(),
            recommendations: Vec::new(),
        };

        if !self.config.validation_config.validate_capabilities {
            return Ok(validation_result);
        }

        let capabilities = self
            .capabilities
            .as_ref()
            .ok_or_else(|| DeviceError::APIError("Capabilities not queried".into()))?;

        // Check measurement count limits
        let measurement_count = circuit
            .operations()
            .iter()
            .filter(|op| matches!(op, CircuitOp::Measure(_)))
            .count();

        if let Some(max_measurements) = capabilities.max_measurements {
            if measurement_count > max_measurements {
                validation_result.errors.push(format!(
                    "Circuit requires {measurement_count} measurements but device supports maximum {max_measurements}"
                ));
                validation_result.is_valid = false;
            }
        }

        // Validate classical register usage
        if self.config.validation_config.validate_register_sizes {
            self.validate_classical_registers(circuit, capabilities, &mut validation_result)?;
        }

        // Check timing constraints
        if self.config.validation_config.check_timing_constraints {
            self.validate_timing_constraints(circuit, capabilities, &mut validation_result)?;
        }

        // Validate feed-forward operations
        if self.config.validation_config.validate_feedforward {
            self.validate_feedforward_operations(circuit, capabilities, &mut validation_result)?;
        }

        // Check for measurement conflicts
        if self.config.validation_config.check_measurement_conflicts {
            self.check_measurement_conflicts(circuit, &mut validation_result)?;
        }

        Ok(validation_result)
    }

    /// Execute a circuit with mid-circuit measurements
    pub async fn execute_circuit<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        device_executor: &dyn MidCircuitDeviceExecutor,
        shots: usize,
    ) -> DeviceResult<MidCircuitExecutionResult> {
        let start_time = Instant::now();

        // Validate circuit before execution
        let validation = self.validate_circuit(circuit, device_executor.device_id())?;
        if !validation.is_valid {
            return Err(DeviceError::APIError(format!(
                "Circuit validation failed: {:?}",
                validation.errors
            )));
        }

        // Optimize circuit for hardware
        let optimized_circuit = self.optimize_for_hardware(circuit, device_executor).await?;

        // Execute with measurement tracking
        let mut measurement_history = Vec::new();
        let mut classical_registers = HashMap::new();
        let mut execution_stats = ExecutionStats {
            total_execution_time: Duration::from_millis(0),
            quantum_time: Duration::from_millis(0),
            measurement_time: Duration::from_millis(0),
            classical_time: Duration::from_millis(0),
            num_measurements: 0,
            num_conditional_ops: 0,
            avg_measurement_latency: 0.0,
            max_measurement_latency: 0.0,
        };

        // Execute the optimized circuit
        let final_measurements = self
            .execute_with_tracking(
                optimized_circuit,
                device_executor,
                shots,
                &mut measurement_history,
                &mut classical_registers,
                &mut execution_stats,
            )
            .await?;

        // Calculate performance metrics
        let performance_metrics =
            self.calculate_performance_metrics(&measurement_history, &execution_stats)?;

        // Perform error analysis
        let error_analysis = if self.config.enable_measurement_mitigation {
            Some(self.analyze_measurement_errors(&measurement_history, circuit)?)
        } else {
            None
        };

        // Perform advanced analytics
        let analytics_results = self
            .perform_advanced_analytics(&measurement_history, &execution_stats)
            .await?;

        // Generate predictions if enabled
        let prediction_results = if self.config.prediction_config.enable_prediction {
            Some(
                self.predict_measurements(
                    &measurement_history,
                    self.config.prediction_config.prediction_horizon,
                )
                .await?,
            )
        } else {
            None
        };

        // Generate optimization recommendations
        let optimization_recommendations = self
            .generate_optimization_recommendations(
                &performance_metrics,
                &analytics_results,
                &measurement_history,
            )
            .await?;

        // Generate adaptive learning insights
        let adaptive_insights = self
            .generate_adaptive_insights(&performance_metrics, &measurement_history)
            .await?;

        execution_stats.total_execution_time = start_time.elapsed();

        let execution_result = MidCircuitExecutionResult {
            final_measurements,
            classical_registers,
            measurement_history: measurement_history.clone(),
            execution_stats,
            performance_metrics,
            error_analysis,
            analytics_results,
            prediction_results,
            optimization_recommendations,
            adaptive_insights,
        };

        // Update performance monitoring
        self.update_performance_monitoring(&execution_result)
            .await?;

        Ok(execution_result)
    }

    /// Optimize circuit for specific hardware backend
    async fn optimize_for_hardware<'a, const N: usize>(
        &self,
        circuit: &'a MeasurementCircuit<N>,
        device_executor: &dyn MidCircuitDeviceExecutor,
    ) -> DeviceResult<&'a MeasurementCircuit<N>> {
        // Since optimization methods are currently placeholders that don't modify the circuit,
        // we can just return the input reference for now
        // TODO: Implement actual optimization that creates new circuits

        if self.config.hardware_optimizations.batch_measurements {
            // self.batch_measurements(circuit)?;
        }

        if self.config.hardware_optimizations.optimize_scheduling {
            // self.optimize_measurement_scheduling(circuit)?;
        }

        if self.config.hardware_optimizations.precompile_conditions {
            // self.precompile_classical_conditions(circuit)?;
        }

        Ok(circuit)
    }

    /// Execute circuit with detailed tracking
    async fn execute_with_tracking<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        device_executor: &dyn MidCircuitDeviceExecutor,
        shots: usize,
        measurement_history: &mut Vec<MeasurementEvent>,
        classical_registers: &mut HashMap<String, Vec<u8>>,
        execution_stats: &mut ExecutionStats,
    ) -> DeviceResult<HashMap<String, usize>> {
        let mut final_measurements = HashMap::new();
        let execution_start = Instant::now();

        // Process each shot
        for shot in 0..shots {
            let shot_start = Instant::now();

            // Reset classical registers for this shot
            classical_registers.clear();

            // Execute operations sequentially
            for (op_index, operation) in circuit.operations().iter().enumerate() {
                match operation {
                    CircuitOp::Gate(gate) => {
                        let gate_start = Instant::now();
                        device_executor.execute_gate(gate.as_ref()).await?;
                        execution_stats.quantum_time += gate_start.elapsed();
                    }
                    CircuitOp::Measure(measurement) => {
                        let measurement_start = Instant::now();
                        let result = self
                            .execute_measurement(
                                measurement,
                                device_executor,
                                measurement_history,
                                execution_start.elapsed().as_micros() as f64,
                            )
                            .await?;

                        // Store result in classical register
                        self.store_measurement_result(measurement, result, classical_registers)?;

                        execution_stats.num_measurements += 1;
                        let latency = measurement_start.elapsed().as_micros() as f64;
                        execution_stats.measurement_time += measurement_start.elapsed();

                        if latency > execution_stats.max_measurement_latency {
                            execution_stats.max_measurement_latency = latency;
                        }
                    }
                    CircuitOp::FeedForward(feedforward) => {
                        let classical_start = Instant::now();

                        // Evaluate condition
                        let condition_met = self.evaluate_classical_condition(
                            &feedforward.condition,
                            classical_registers,
                        )?;

                        if condition_met {
                            device_executor.execute_gate(&*feedforward.gate).await?;
                            execution_stats.num_conditional_ops += 1;
                        }

                        execution_stats.classical_time += classical_start.elapsed();
                    }
                    CircuitOp::Barrier(_) => {
                        // Synchronization point - ensure all previous operations complete
                        device_executor.synchronize().await?;
                    }
                    CircuitOp::Reset(qubit) => {
                        device_executor.reset_qubit(*qubit).await?;
                    }
                }
            }

            // Final measurements for this shot
            let final_result = device_executor.measure_all().await?;
            for (qubit_str, result) in final_result {
                *final_measurements.entry(qubit_str).or_insert(0) += result;
            }
        }

        // Calculate average measurement latency
        if execution_stats.num_measurements > 0 {
            execution_stats.avg_measurement_latency = execution_stats.measurement_time.as_micros()
                as f64
                / execution_stats.num_measurements as f64;
        }

        Ok(final_measurements)
    }

    /// Execute a single measurement with tracking
    async fn execute_measurement(
        &self,
        measurement: &Measurement,
        device_executor: &dyn MidCircuitDeviceExecutor,
        measurement_history: &mut Vec<MeasurementEvent>,
        timestamp: f64,
    ) -> DeviceResult<u8> {
        let measurement_start = Instant::now();

        let result = device_executor.measure_qubit(measurement.qubit).await?;

        let latency = measurement_start.elapsed().as_micros() as f64;

        // Calculate measurement confidence based on calibration data
        let confidence = self.calculate_measurement_confidence(measurement.qubit)?;

        measurement_history.push(MeasurementEvent {
            timestamp,
            qubit: measurement.qubit,
            result,
            storage_location: StorageLocation::ClassicalBit(measurement.target_bit),
            latency,
            confidence,
        });

        Ok(result)
    }

    /// Store measurement result in classical registers
    fn store_measurement_result(
        &self,
        measurement: &Measurement,
        result: u8,
        classical_registers: &mut HashMap<String, Vec<u8>>,
    ) -> DeviceResult<()> {
        // For now, store in a default register
        let register = classical_registers
            .entry("measurements".to_string())
            .or_insert_with(|| vec![0; 64]); // 64-bit default register

        if measurement.target_bit < register.len() {
            register[measurement.target_bit] = result;
        }

        Ok(())
    }

    /// Evaluate classical condition
    fn evaluate_classical_condition(
        &self,
        condition: &ClassicalCondition,
        classical_registers: &HashMap<String, Vec<u8>>,
    ) -> DeviceResult<bool> {
        // Evaluate the classical condition using the struct fields
        match (&condition.lhs, &condition.rhs) {
            (ClassicalValue::Bit(lhs_bit), ClassicalValue::Bit(rhs_bit)) => {
                Ok(match condition.op {
                    ComparisonOp::Equal => lhs_bit == rhs_bit,
                    ComparisonOp::NotEqual => lhs_bit != rhs_bit,
                    _ => false, // Other comparisons not meaningful for bits
                })
            }
            (ClassicalValue::Register(reg_name), ClassicalValue::Integer(expected)) => {
                Ok(classical_registers.get(reg_name).map_or(false, |register| {
                    // Compare first few bits with expected value
                    let actual_value = register
                        .iter()
                        .take(8) // Take first 8 bits
                        .enumerate()
                        .fold(0u8, |acc, (i, &bit)| acc | (bit << i));
                    actual_value == *expected as u8
                }))
            }
            // Add other condition types as needed
            _ => Ok(false),
        }
    }

    /// Calculate measurement confidence based on calibration
    const fn calculate_measurement_confidence(&self, qubit: QubitId) -> DeviceResult<f64> {
        // Use calibration data to estimate measurement fidelity
        // This is a simplified implementation
        Ok(0.99) // 99% confidence default
    }

    /// Calculate performance metrics
    fn calculate_performance_metrics(
        &self,
        measurement_history: &[MeasurementEvent],
        execution_stats: &ExecutionStats,
    ) -> DeviceResult<PerformanceMetrics> {
        let total_measurements = measurement_history.len() as f64;

        // Calculate measurement success rate (simplified)
        let high_confidence_measurements = measurement_history
            .iter()
            .filter(|event| event.confidence > 0.95)
            .count() as f64;

        let measurement_success_rate = if total_measurements > 0.0 {
            high_confidence_measurements / total_measurements
        } else {
            1.0
        };

        // Calculate timing efficiency
        let total_time = execution_stats.total_execution_time.as_micros() as f64;
        let useful_time = execution_stats.quantum_time.as_micros() as f64;
        let timing_overhead = if useful_time > 0.0 {
            (total_time - useful_time) / useful_time
        } else {
            0.0
        };

        // Resource utilization
        let resource_utilization = ResourceUtilization {
            quantum_utilization: if total_time > 0.0 {
                useful_time / total_time
            } else {
                0.0
            },
            classical_utilization: if total_time > 0.0 {
                execution_stats.classical_time.as_micros() as f64 / total_time
            } else {
                0.0
            },
            memory_usage: total_measurements as usize * 32, // Estimate 32 bytes per measurement
            communication_overhead: execution_stats.measurement_time.as_micros() as f64
                / total_time,
        };

        Ok(PerformanceMetrics {
            measurement_success_rate,
            classical_efficiency: 0.95, // Placeholder
            circuit_fidelity: measurement_success_rate * 0.98, // Estimate
            measurement_error_rate: 1.0 - measurement_success_rate,
            timing_overhead,
            resource_utilization,
        })
    }

    /// Analyze measurement errors
    fn analyze_measurement_errors<const N: usize>(
        &self,
        measurement_history: &[MeasurementEvent],
        circuit: &MeasurementCircuit<N>,
    ) -> DeviceResult<ErrorAnalysis> {
        let mut measurement_errors = HashMap::new();

        // Calculate error statistics for each qubit
        for event in measurement_history {
            let error_stats =
                measurement_errors
                    .entry(event.qubit)
                    .or_insert(MeasurementErrorStats {
                        readout_error_rate: 0.01,
                        spam_error: 0.02,
                        thermal_relaxation: 0.005,
                        dephasing: 0.008,
                    });

            // Update error statistics based on measurement confidence
            if event.confidence < 0.95 {
                error_stats.readout_error_rate += 0.001;
            }
        }

        Ok(ErrorAnalysis {
            measurement_errors,
            classical_errors: Vec::new(),
            timing_violations: Vec::new(),
            error_correlations: scirs2_core::ndarray::Array2::zeros((0, 0)),
        })
    }

    // Placeholder methods for analytics and other components
    async fn perform_advanced_analytics(
        &self,
        measurement_history: &[MeasurementEvent],
        execution_stats: &ExecutionStats,
    ) -> DeviceResult<AdvancedAnalyticsResults> {
        // This would call the analytics engine
        // For now, return a default result
        Ok(AdvancedAnalyticsResults::default())
    }

    async fn predict_measurements(
        &self,
        measurement_history: &[MeasurementEvent],
        horizon: usize,
    ) -> DeviceResult<MeasurementPredictionResults> {
        // This would call the predictor
        Ok(MeasurementPredictionResults::default())
    }

    async fn generate_optimization_recommendations(
        &self,
        performance_metrics: &PerformanceMetrics,
        analytics_results: &AdvancedAnalyticsResults,
        measurement_history: &[MeasurementEvent],
    ) -> DeviceResult<OptimizationRecommendations> {
        Ok(OptimizationRecommendations::default())
    }

    async fn generate_adaptive_insights(
        &self,
        performance_metrics: &PerformanceMetrics,
        measurement_history: &[MeasurementEvent],
    ) -> DeviceResult<AdaptiveLearningInsights> {
        Ok(AdaptiveLearningInsights::default())
    }

    async fn update_performance_monitoring(
        &self,
        execution_result: &MidCircuitExecutionResult,
    ) -> DeviceResult<()> {
        // Update performance monitoring
        Ok(())
    }

    // Helper methods for validation
    const fn validate_classical_registers<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        capabilities: &MidCircuitCapabilities,
        validation_result: &mut ValidationResult,
    ) -> DeviceResult<()> {
        // Implementation for classical register validation
        Ok(())
    }

    const fn validate_timing_constraints<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        capabilities: &MidCircuitCapabilities,
        validation_result: &mut ValidationResult,
    ) -> DeviceResult<()> {
        // Implementation for timing constraint validation
        Ok(())
    }

    const fn validate_feedforward_operations<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        capabilities: &MidCircuitCapabilities,
        validation_result: &mut ValidationResult,
    ) -> DeviceResult<()> {
        // Implementation for feedforward validation
        Ok(())
    }

    const fn check_measurement_conflicts<const N: usize>(
        &self,
        circuit: &MeasurementCircuit<N>,
        validation_result: &mut ValidationResult,
    ) -> DeviceResult<()> {
        // Implementation for measurement conflict checking
        Ok(())
    }

    // Helper methods for capabilities
    fn query_backend_capabilities(&self, backend: HardwareBackend) -> BackendCapabilities {
        // Mock implementation
        BackendCapabilities::default()
    }

    fn get_supported_measurement_types(
        &self,
        backend: HardwareBackend,
    ) -> DeviceResult<Vec<MeasurementType>> {
        Ok(vec![MeasurementType::ZBasis])
    }

    fn get_native_protocols(&self, backend: HardwareBackend) -> Vec<String> {
        vec!["standard".to_string()]
    }

    fn get_timing_constraints(
        &self,
        backend: HardwareBackend,
        device_id: &str,
    ) -> DeviceResult<TimingConstraints> {
        Ok(TimingConstraints {
            min_measurement_spacing: 100.0,
            max_measurement_duration: 1000.0,
            classical_deadline: 500.0,
            coherence_limits: HashMap::new(),
        })
    }
}

// Placeholder backend capabilities
#[derive(Debug, Clone, Default)]
pub struct BackendCapabilities {
    pub max_mid_circuit_measurements: Option<usize>,
    pub classical_register_size: Option<usize>,
    pub supports_real_time_feedback: Option<bool>,
    pub supports_parallel_execution: Option<bool>,
}

// Default implementations for result types
impl Default for DescriptiveStatistics {
    fn default() -> Self {
        Self {
            mean_latency: 0.0,
            std_latency: 0.0,
            median_latency: 0.0,
            latency_percentiles: vec![],
            success_rate_stats: MeasurementSuccessStats::default(),
            error_rate_distribution: ErrorRateDistribution::default(),
        }
    }
}

impl Default for MeasurementSuccessStats {
    fn default() -> Self {
        Self {
            overall_success_rate: 1.0,
            per_qubit_success_rate: HashMap::new(),
            temporal_success_rate: vec![],
            success_rate_ci: (0.95, 1.0),
        }
    }
}

impl Default for ErrorRateDistribution {
    fn default() -> Self {
        Self {
            histogram: vec![],
            best_fit_distribution: "normal".to_string(),
            distribution_parameters: vec![],
            goodness_of_fit: 0.95,
        }
    }
}

impl Default for ConfidenceIntervals {
    fn default() -> Self {
        Self {
            confidence_level: 0.95,
            mean_intervals: HashMap::new(),
            bootstrap_intervals: HashMap::new(),
            prediction_intervals: HashMap::new(),
        }
    }
}

impl Default for CorrelationAnalysisResults {
    fn default() -> Self {
        Self {
            pearson_correlations: scirs2_core::ndarray::Array2::zeros((0, 0)),
            spearman_correlations: scirs2_core::ndarray::Array2::zeros((0, 0)),
            kendall_correlations: scirs2_core::ndarray::Array2::zeros((0, 0)),
            significant_correlations: vec![],
            partial_correlations: scirs2_core::ndarray::Array2::zeros((0, 0)),
            network_analysis: CorrelationNetworkAnalysis::default(),
        }
    }
}

impl Default for CorrelationNetworkAnalysis {
    fn default() -> Self {
        Self {
            adjacency_matrix: scirs2_core::ndarray::Array2::zeros((0, 0)),
            centrality_measures: NodeCentralityMeasures::default(),
            communities: vec![],
            network_density: 0.0,
            clustering_coefficient: 0.0,
        }
    }
}

impl Default for NormalityAssessment {
    fn default() -> Self {
        Self {
            shapiro_wilk: StatisticalTest::default(),
            anderson_darling: StatisticalTest::default(),
            jarque_bera: StatisticalTest::default(),
            is_normal: true,
            normality_confidence: 0.95,
        }
    }
}

impl Default for MeasurementPredictionResults {
    fn default() -> Self {
        Self {
            predictions: scirs2_core::ndarray::Array1::zeros(0),
            confidence_intervals: scirs2_core::ndarray::Array2::zeros((0, 0)),
            timestamps: vec![],
            model_performance: PredictionModelPerformance::default(),
            uncertainty: PredictionUncertainty::default(),
        }
    }
}

impl Default for PredictionModelPerformance {
    fn default() -> Self {
        Self {
            mae: 0.0,
            mse: 0.0,
            rmse: 0.0,
            mape: 0.0,
            r2_score: 1.0,
            accuracy: 1.0,
        }
    }
}

impl Default for PredictionUncertainty {
    fn default() -> Self {
        Self {
            aleatoric_uncertainty: scirs2_core::ndarray::Array1::zeros(0),
            epistemic_uncertainty: scirs2_core::ndarray::Array1::zeros(0),
            total_uncertainty: scirs2_core::ndarray::Array1::zeros(0),
            uncertainty_bounds: scirs2_core::ndarray::Array2::zeros((0, 0)),
        }
    }
}

impl Default for OptimizationRecommendations {
    fn default() -> Self {
        Self {
            scheduling_optimizations: vec![],
            protocol_optimizations: vec![],
            resource_optimizations: vec![],
            performance_improvements: vec![],
        }
    }
}

impl Default for AdaptiveLearningInsights {
    fn default() -> Self {
        Self {
            learning_progress: LearningProgress::default(),
            adaptation_history: vec![],
            performance_trends: PerformanceTrends::default(),
            drift_detection: DriftDetectionResults::default(),
            transfer_learning: TransferLearningInsights::default(),
        }
    }
}

impl Default for LearningProgress {
    fn default() -> Self {
        Self {
            iterations_completed: 0,
            current_learning_rate: 0.001,
            loss_history: scirs2_core::ndarray::Array1::zeros(0),
            accuracy_history: scirs2_core::ndarray::Array1::zeros(0),
            convergence_status: ConvergenceStatus::NotStarted,
        }
    }
}

impl Default for PerformanceTrends {
    fn default() -> Self {
        Self {
            short_term_trend: TrendDirection::Stable,
            long_term_trend: TrendDirection::Stable,
            trend_strength: 0.0,
            seasonal_patterns: None,
            volatility: 0.0,
        }
    }
}

impl Default for DriftDetectionResults {
    fn default() -> Self {
        Self {
            drift_detected: false,
            drift_type: None,
            drift_magnitude: 0.0,
            detection_confidence: 0.95,
            recommended_actions: vec![],
        }
    }
}

impl Default for TransferLearningInsights {
    fn default() -> Self {
        Self {
            transfer_effectiveness: 0.8,
            domain_similarity: 0.9,
            feature_transferability: scirs2_core::ndarray::Array1::zeros(0),
            adaptation_requirements: vec![],
            recommendations: vec![],
        }
    }
}