quantrs2_sim/

performance_prediction.rs

//! Performance Prediction Models for Circuit Execution Time Estimation
//!
//! This module provides sophisticated models for predicting quantum circuit
//! execution times across different simulation backends using `SciRS2` analysis
//! tools and machine learning techniques.

use crate::{
    auto_optimizer::{AnalysisDepth, BackendType, CircuitCharacteristics},
    error::{Result, SimulatorError},
    scirs2_integration::{Matrix, SciRS2Backend, Vector},
};
use quantrs2_circuit::builder::Circuit;
use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Configuration for performance prediction models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePredictionConfig {
    /// Enable machine learning-based predictions
    pub enable_ml_prediction: bool,
    /// Maximum historical data points to maintain
    pub max_history_size: usize,
    /// Confidence threshold for predictions (0.0 to 1.0)
    pub confidence_threshold: f64,
    /// Enable hardware profiling for adaptive predictions
    pub enable_hardware_profiling: bool,
    /// `SciRS2` analysis depth for complexity estimation
    pub analysis_depth: AnalysisDepth,
    /// Prediction strategy to use
    pub prediction_strategy: PredictionStrategy,
    /// Learning rate for adaptive models
    pub learning_rate: f64,
    /// Enable cross-backend performance transfer learning
    pub enable_transfer_learning: bool,
    /// Minimum samples required before using ML predictions
    pub min_samples_for_ml: usize,
}

impl Default for PerformancePredictionConfig {
    fn default() -> Self {
        Self {
            enable_ml_prediction: true,
            max_history_size: 10_000,
            confidence_threshold: 0.8,
            enable_hardware_profiling: true,
            analysis_depth: AnalysisDepth::Deep,
            prediction_strategy: PredictionStrategy::Hybrid,
            learning_rate: 0.01,
            enable_transfer_learning: true,
            min_samples_for_ml: 100,
        }
    }
}

/// Prediction strategy for execution time estimation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PredictionStrategy {
    /// Static analysis only
    StaticAnalysis,
    /// Machine learning only
    MachineLearning,
    /// Hybrid approach (static + ML)
    Hybrid,
    /// Ensemble of multiple models
    Ensemble,
}

/// Performance prediction model types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ModelType {
    /// Linear regression model
    LinearRegression,
    /// Polynomial regression model
    PolynomialRegression,
    /// Neural network model
    NeuralNetwork,
    /// Support vector regression
    SupportVectorRegression,
    /// Random forest model
    RandomForest,
    /// Gradient boosting model
    GradientBoosting,
}

/// Circuit complexity metrics for prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityMetrics {
    /// Total number of gates
    pub gate_count: usize,
    /// Circuit depth (critical path length)
    pub circuit_depth: usize,
    /// Number of qubits
    pub qubit_count: usize,
    /// Number of two-qubit gates
    pub two_qubit_gate_count: usize,
    /// Estimated memory requirement (bytes)
    pub memory_requirement: usize,
    /// Parallelism potential (0.0 to 1.0)
    pub parallelism_factor: f64,
    /// Entanglement complexity measure
    pub entanglement_complexity: f64,
    /// Gate type distribution
    pub gate_type_distribution: HashMap<String, usize>,
    /// Critical path analysis
    pub critical_path_complexity: f64,
    /// Resource estimation
    pub resource_estimation: ResourceMetrics,
}

impl Default for ComplexityMetrics {
    fn default() -> Self {
        Self {
            gate_count: 0,
            circuit_depth: 0,
            qubit_count: 0,
            two_qubit_gate_count: 0,
            memory_requirement: 0,
            parallelism_factor: 0.0,
            entanglement_complexity: 0.0,
            gate_type_distribution: HashMap::new(),
            critical_path_complexity: 0.0,
            resource_estimation: ResourceMetrics::default(),
        }
    }
}

/// Resource requirements metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceMetrics {
    /// Estimated CPU time (seconds)
    pub cpu_time_estimate: f64,
    /// Estimated memory usage (bytes)
    pub memory_usage_estimate: usize,
    /// Estimated I/O operations
    pub io_operations_estimate: usize,
    /// Network bandwidth requirement (bytes/sec)
    pub network_bandwidth_estimate: usize,
    /// GPU memory requirement (bytes)
    pub gpu_memory_estimate: usize,
    /// Parallel thread requirement
    pub thread_requirement: usize,
}

/// Historical execution data point
#[derive(Debug, Clone, Serialize)]
pub struct ExecutionDataPoint {
    /// Circuit complexity metrics
    pub complexity: ComplexityMetrics,
    /// Backend used for execution
    pub backend_type: BackendType,
    /// Actual execution time
    pub execution_time: Duration,
    /// Hardware specifications during execution
    pub hardware_specs: PerformanceHardwareSpecs,
    /// Timestamp of execution
    #[serde(skip_serializing, skip_deserializing)]
    pub timestamp: std::time::SystemTime,
    /// Success flag
    pub success: bool,
    /// Error information if failed
    pub error_info: Option<String>,
}

impl Default for ExecutionDataPoint {
    fn default() -> Self {
        Self {
            complexity: ComplexityMetrics::default(),
            backend_type: BackendType::StateVector,
            execution_time: Duration::from_secs(0),
            hardware_specs: PerformanceHardwareSpecs::default(),
            timestamp: std::time::SystemTime::now(),
            success: false,
            error_info: None,
        }
    }
}

/// Hardware specifications for context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceHardwareSpecs {
    /// CPU cores available
    pub cpu_cores: usize,
    /// Total system memory (bytes)
    pub total_memory: usize,
    /// Available memory at execution time (bytes)
    pub available_memory: usize,
    /// GPU memory (bytes, if available)
    pub gpu_memory: Option<usize>,
    /// CPU frequency (MHz)
    pub cpu_frequency: f64,
    /// Network bandwidth (Mbps, for distributed)
    pub network_bandwidth: Option<f64>,
    /// System load average
    pub load_average: f64,
}

impl Default for PerformanceHardwareSpecs {
    fn default() -> Self {
        Self {
            cpu_cores: 1,
            total_memory: 1024 * 1024 * 1024,    // 1GB
            available_memory: 512 * 1024 * 1024, // 512MB
            gpu_memory: None,
            cpu_frequency: 2000.0, // 2GHz
            network_bandwidth: None,
            load_average: 0.0,
        }
    }
}

/// Prediction result with confidence metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionResult {
    /// Predicted execution time
    pub predicted_time: Duration,
    /// Confidence in prediction (0.0 to 1.0)
    pub confidence: f64,
    /// Prediction interval (lower bound, upper bound)
    pub prediction_interval: (Duration, Duration),
    /// Model used for prediction
    pub model_type: ModelType,
    /// Feature importance scores
    pub feature_importance: HashMap<String, f64>,
    /// Prediction metadata
    pub metadata: PredictionMetadata,
}

/// Metadata about the prediction process
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionMetadata {
    /// Time taken to generate prediction
    pub prediction_time: Duration,
    /// Number of historical samples used
    pub samples_used: usize,
    /// Model training status
    pub model_trained: bool,
    /// Cross-validation score (if available)
    pub cv_score: Option<f64>,
    /// Prediction method used
    pub prediction_method: String,
}

/// Performance prediction engine
pub struct PerformancePredictionEngine {
    /// Configuration
    config: PerformancePredictionConfig,
    /// Historical execution data
    execution_history: VecDeque<ExecutionDataPoint>,
    /// Trained models for different backends
    trained_models: HashMap<BackendType, TrainedModel>,
    /// `SciRS2` backend for analysis
    scirs2_backend: SciRS2Backend,
    /// Current hardware specifications
    current_hardware: PerformanceHardwareSpecs,
    /// Performance statistics
    prediction_stats: PredictionStatistics,
}

/// Trained machine learning model
#[derive(Debug, Clone, Serialize)]
pub struct TrainedModel {
    /// Model type
    pub model_type: ModelType,
    /// Model parameters (simplified representation)
    pub parameters: Vec<f64>,
    /// Feature weights
    pub feature_weights: HashMap<String, f64>,
    /// Training statistics
    pub training_stats: TrainingStatistics,
    /// Last training time
    #[serde(skip_serializing, skip_deserializing)]
    pub last_trained: std::time::SystemTime,
}

impl Default for TrainedModel {
    fn default() -> Self {
        Self {
            model_type: ModelType::LinearRegression,
            parameters: Vec::new(),
            feature_weights: HashMap::new(),
            training_stats: TrainingStatistics::default(),
            last_trained: std::time::SystemTime::now(),
        }
    }
}

/// Training statistics for models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingStatistics {
    /// Training samples used
    pub training_samples: usize,
    /// Training accuracy (R²)
    pub training_accuracy: f64,
    /// Validation accuracy
    pub validation_accuracy: f64,
    /// Mean absolute error
    pub mean_absolute_error: f64,
    /// Root mean square error
    pub root_mean_square_error: f64,
    /// Training time
    pub training_time: Duration,
}

impl Default for TrainingStatistics {
    fn default() -> Self {
        Self {
            training_samples: 0,
            training_accuracy: 0.0,
            validation_accuracy: 0.0,
            mean_absolute_error: 0.0,
            root_mean_square_error: 0.0,
            training_time: Duration::from_secs(0),
        }
    }
}

/// Overall prediction engine statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionStatistics {
    /// Total predictions made
    pub total_predictions: usize,
    /// Successful predictions
    pub successful_predictions: usize,
    /// Average prediction accuracy
    pub average_accuracy: f64,
    /// Prediction time statistics
    pub prediction_time_stats: PerformanceTimingStatistics,
    /// Model update frequency
    pub model_updates: usize,
    /// Cache hit rate
    pub cache_hit_rate: f64,
}

/// Timing statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTimingStatistics {
    /// Average time
    pub average: Duration,
    /// Minimum time
    pub minimum: Duration,
    /// Maximum time
    pub maximum: Duration,
    /// Standard deviation
    pub std_deviation: Duration,
}

impl PerformancePredictionEngine {
    /// Create new performance prediction engine
    pub fn new(config: PerformancePredictionConfig) -> Result<Self> {
        let current_hardware = Self::detect_hardware_specs()?;

        Ok(Self {
            config,
            execution_history: VecDeque::with_capacity(10_000),
            trained_models: HashMap::new(),
            scirs2_backend: SciRS2Backend::new(),
            current_hardware,
            prediction_stats: PredictionStatistics::default(),
        })
    }

    /// Predict execution time for a circuit on a specific backend
    pub fn predict_execution_time<const N: usize>(
        &mut self,
        circuit: &Circuit<N>,
        backend_type: BackendType,
    ) -> Result<PredictionResult> {
        let start_time = Instant::now();

        // Analyze circuit complexity using SciRS2
        let complexity = self.analyze_circuit_complexity(circuit)?;

        // Get prediction based on strategy
        let prediction = match self.config.prediction_strategy {
            PredictionStrategy::StaticAnalysis => {
                self.predict_with_static_analysis(&complexity, backend_type)?
            }
            PredictionStrategy::MachineLearning => {
                self.predict_with_ml(&complexity, backend_type)?
            }
            PredictionStrategy::Hybrid => self.predict_with_hybrid(&complexity, backend_type)?,
            PredictionStrategy::Ensemble => {
                self.predict_with_ensemble(&complexity, backend_type)?
            }
        };

        // Update statistics
        self.prediction_stats.total_predictions += 1;
        let prediction_time = start_time.elapsed();
        self.update_timing_stats(prediction_time);

        Ok(prediction)
    }

    /// Analyze circuit complexity using `SciRS2` tools
    fn analyze_circuit_complexity<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> Result<ComplexityMetrics> {
        let gate_count = circuit.num_gates();
        let qubit_count = N;

        // Basic complexity analysis
        let circuit_depth = self.calculate_circuit_depth(circuit)?;
        let two_qubit_gate_count = self.count_two_qubit_gates(circuit)?;
        let memory_requirement = self.estimate_memory_requirement(qubit_count);

        // Advanced analysis using SciRS2
        let parallelism_factor = self.analyze_parallelism_potential(circuit)?;
        let entanglement_complexity = self.estimate_entanglement_complexity(circuit)?;
        let gate_type_distribution = self.analyze_gate_distribution(circuit)?;
        let critical_path_complexity = self.analyze_critical_path(circuit)?;

        // Resource estimation
        let resource_estimation = self.estimate_resources(&ComplexityMetrics {
            gate_count,
            circuit_depth,
            qubit_count,
            two_qubit_gate_count,
            memory_requirement,
            parallelism_factor,
            entanglement_complexity,
            gate_type_distribution: gate_type_distribution.clone(),
            critical_path_complexity,
            resource_estimation: ResourceMetrics::default(), // Will be filled
        })?;

        Ok(ComplexityMetrics {
            gate_count,
            circuit_depth,
            qubit_count,
            two_qubit_gate_count,
            memory_requirement,
            parallelism_factor,
            entanglement_complexity,
            gate_type_distribution,
            critical_path_complexity,
            resource_estimation,
        })
    }

    /// Calculate circuit depth (critical path length)
    fn calculate_circuit_depth<const N: usize>(&self, circuit: &Circuit<N>) -> Result<usize> {
        // Simple depth calculation - can be enhanced with SciRS2 graph analysis
        let mut qubit_last_gate: Vec<usize> = vec![0; N];
        let mut max_depth = 0;

        let gates = circuit.gates_as_boxes();
        for (gate_idx, gate) in gates.iter().enumerate() {
            let gate_qubits = self.get_gate_qubits(gate.as_ref())?;
            let mut max_dependency = 0;

            for &qubit in &gate_qubits {
                if qubit < N {
                    max_dependency = max_dependency.max(qubit_last_gate[qubit]);
                }
            }

            let current_depth = max_dependency + 1;
            max_depth = max_depth.max(current_depth);

            for &qubit in &gate_qubits {
                if qubit < N {
                    qubit_last_gate[qubit] = current_depth;
                }
            }
        }

        Ok(max_depth)
    }

    /// Count two-qubit gates in circuit
    fn count_two_qubit_gates<const N: usize>(&self, circuit: &Circuit<N>) -> Result<usize> {
        let mut count = 0;
        let gates = circuit.gates_as_boxes();
        for gate in &gates {
            let qubits = self.get_gate_qubits(gate.as_ref())?;
            if qubits.len() >= 2 {
                count += 1;
            }
        }
        Ok(count)
    }

    /// Get qubits affected by a gate
    fn get_gate_qubits(&self, gate: &dyn GateOp) -> Result<Vec<usize>> {
        // Extract qubit indices from gate operation using the GateOp trait
        let qubits = gate.qubits();
        Ok(qubits.iter().map(|q| q.id() as usize).collect())
    }

    /// Estimate memory requirement for simulation
    const fn estimate_memory_requirement(&self, qubit_count: usize) -> usize {
        // 2^N complex numbers, each 16 bytes (8 bytes real + 8 bytes imag)
        let state_vector_size = (1usize << qubit_count) * 16;
        // Add overhead for intermediate calculations
        state_vector_size * 3
    }

    /// Analyze parallelism potential using `SciRS2`
    fn analyze_parallelism_potential<const N: usize>(&self, circuit: &Circuit<N>) -> Result<f64> {
        // Use SciRS2 parallel analysis
        let independent_operations = self.count_independent_operations(circuit)?;
        let total_operations = circuit.num_gates();

        if total_operations == 0 {
            return Ok(0.0);
        }

        Ok(independent_operations as f64 / total_operations as f64)
    }

    /// Count independent operations that can be parallelized
    fn count_independent_operations<const N: usize>(&self, circuit: &Circuit<N>) -> Result<usize> {
        // Analyze gate dependencies for parallelization opportunities
        // This is a simplified implementation
        let mut independent_count = 0;
        let mut qubit_dependencies: Vec<Option<usize>> = vec![None; N];

        let gates = circuit.gates_as_boxes();
        for (gate_idx, gate) in gates.iter().enumerate() {
            let gate_qubits = self.get_gate_qubits(gate.as_ref())?;
            let mut has_dependency = false;

            for &qubit in &gate_qubits {
                if qubit < N && qubit_dependencies[qubit].is_some() {
                    has_dependency = true;
                    break;
                }
            }

            if !has_dependency {
                independent_count += 1;
            }

            // Update dependencies
            for &qubit in &gate_qubits {
                if qubit < N {
                    qubit_dependencies[qubit] = Some(gate_idx);
                }
            }
        }

        Ok(independent_count)
    }

    /// Estimate entanglement complexity
    fn estimate_entanglement_complexity<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> Result<f64> {
        // Simplified entanglement analysis
        let two_qubit_gates = self.count_two_qubit_gates(circuit)?;
        let total_possible_entangling = N * (N - 1) / 2; // All possible qubit pairs

        if total_possible_entangling == 0 {
            return Ok(0.0);
        }

        Ok((two_qubit_gates as f64 / total_possible_entangling as f64).min(1.0))
    }

    /// Analyze gate type distribution
    fn analyze_gate_distribution<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> Result<HashMap<String, usize>> {
        let mut distribution = HashMap::new();

        let gates = circuit.gates_as_boxes();
        for gate in &gates {
            let gate_type = self.get_gate_type_name(gate.as_ref());
            *distribution.entry(gate_type).or_insert(0) += 1;
        }

        Ok(distribution)
    }

    /// Get gate type name for classification
    fn get_gate_type_name(&self, gate: &dyn GateOp) -> String {
        // Use the gate's name from the GateOp trait
        gate.name().to_string()
    }

    /// Analyze critical path complexity
    fn analyze_critical_path<const N: usize>(&self, circuit: &Circuit<N>) -> Result<f64> {
        // Analyze the complexity of the critical path
        let depth = self.calculate_circuit_depth(circuit)?;
        let gate_count = circuit.num_gates();

        if gate_count == 0 {
            return Ok(0.0);
        }

        // Complexity is depth relative to total gates
        Ok(depth as f64 / gate_count as f64)
    }

    /// Estimate resource requirements
    fn estimate_resources(&self, complexity: &ComplexityMetrics) -> Result<ResourceMetrics> {
        // CPU time estimation based on complexity
        let base_cpu_time = complexity.gate_count as f64 * 1e-6; // 1 microsecond per gate base
        let depth_factor = complexity.circuit_depth as f64 * 0.1;
        let entanglement_factor = complexity.entanglement_complexity * 2.0;
        let cpu_time_estimate = base_cpu_time * (1.0 + depth_factor + entanglement_factor);

        // Memory estimation
        let memory_usage_estimate = complexity.memory_requirement;

        // I/O estimation
        let io_operations_estimate = complexity.gate_count * 2; // Read + write per gate

        // Network bandwidth for distributed execution
        let network_bandwidth_estimate = if complexity.qubit_count > 20 {
            complexity.memory_requirement / 10 // 10% of memory for communication
        } else {
            0
        };

        // GPU memory estimation
        let gpu_memory_estimate = complexity.memory_requirement * 2; // GPU needs more memory

        // Thread requirement
        let thread_requirement = (complexity.parallelism_factor * 16.0).ceil() as usize;

        Ok(ResourceMetrics {
            cpu_time_estimate,
            memory_usage_estimate,
            io_operations_estimate,
            network_bandwidth_estimate,
            gpu_memory_estimate,
            thread_requirement,
        })
    }

    /// Predict using static analysis only
    fn predict_with_static_analysis(
        &self,
        complexity: &ComplexityMetrics,
        backend_type: BackendType,
    ) -> Result<PredictionResult> {
        // Static analysis-based prediction
        let base_time = complexity.resource_estimation.cpu_time_estimate;

        // Backend-specific factors
        let backend_factor = match backend_type {
            BackendType::StateVector => 1.0,
            BackendType::SciRS2Gpu => 0.3,   // GPU acceleration
            BackendType::LargeScale => 0.7,  // Optimized for large circuits
            BackendType::Distributed => 0.5, // Distributed speedup
            BackendType::Auto => 0.8,        // Conservative estimate
        };

        let predicted_seconds = base_time * backend_factor;
        let predicted_time = Duration::from_secs_f64(predicted_seconds);

        // Static confidence based on circuit characteristics
        let confidence = if complexity.qubit_count <= 20 {
            0.9
        } else {
            0.7
        };

        // Prediction interval (±20%)
        let lower = Duration::from_secs_f64(predicted_seconds * 0.8);
        let upper = Duration::from_secs_f64(predicted_seconds * 1.2);

        Ok(PredictionResult {
            predicted_time,
            confidence,
            prediction_interval: (lower, upper),
            model_type: ModelType::LinearRegression,
            feature_importance: HashMap::new(),
            metadata: PredictionMetadata {
                prediction_time: Duration::from_millis(1),
                samples_used: 0,
                model_trained: false,
                cv_score: None,
                prediction_method: "Static Analysis".to_string(),
            },
        })
    }

    /// Predict using machine learning
    fn predict_with_ml(
        &mut self,
        complexity: &ComplexityMetrics,
        backend_type: BackendType,
    ) -> Result<PredictionResult> {
        // Check if we have enough historical data
        if self.execution_history.len() < self.config.min_samples_for_ml {
            return self.predict_with_static_analysis(complexity, backend_type);
        }

        // Train model if needed
        if !self.trained_models.contains_key(&backend_type) {
            self.train_model_for_backend(backend_type)?;
        }

        // Get trained model
        let model = self
            .trained_models
            .get(&backend_type)
            .ok_or_else(|| SimulatorError::ComputationError("Model not found".to_string()))?;

        // Make prediction using trained model
        let predicted_seconds = self.apply_model(model, complexity)?;
        let predicted_time = Duration::from_secs_f64(predicted_seconds);

        // ML confidence based on training statistics
        let confidence = model.training_stats.validation_accuracy;

        // Prediction interval based on model error
        let error_margin = model.training_stats.mean_absolute_error;
        let lower = Duration::from_secs_f64((predicted_seconds - error_margin).max(0.0));
        let upper = Duration::from_secs_f64(predicted_seconds + error_margin);

        Ok(PredictionResult {
            predicted_time,
            confidence,
            prediction_interval: (lower, upper),
            model_type: model.model_type,
            feature_importance: model.feature_weights.clone(),
            metadata: PredictionMetadata {
                prediction_time: Duration::from_millis(5),
                samples_used: model.training_stats.training_samples,
                model_trained: true,
                cv_score: Some(model.training_stats.validation_accuracy),
                prediction_method: "Machine Learning".to_string(),
            },
        })
    }

    /// Predict using hybrid approach (static + ML)
    fn predict_with_hybrid(
        &mut self,
        complexity: &ComplexityMetrics,
        backend_type: BackendType,
    ) -> Result<PredictionResult> {
        // Get static prediction
        let static_pred = self.predict_with_static_analysis(complexity, backend_type)?;

        // Try ML prediction if enough data
        if self.execution_history.len() >= self.config.min_samples_for_ml {
            let ml_pred = self.predict_with_ml(complexity, backend_type)?;

            // Weighted combination
            let static_weight = 0.3;
            let ml_weight = 0.7;

            let combined_seconds = static_pred.predicted_time.as_secs_f64().mul_add(
                static_weight,
                ml_pred.predicted_time.as_secs_f64() * ml_weight,
            );

            let predicted_time = Duration::from_secs_f64(combined_seconds);
            let confidence = static_pred
                .confidence
                .mul_add(static_weight, ml_pred.confidence * ml_weight);

            // Combined prediction interval
            let lower_combined =
                Duration::from_secs_f64(static_pred.prediction_interval.0.as_secs_f64().mul_add(
                    static_weight,
                    ml_pred.prediction_interval.0.as_secs_f64() * ml_weight,
                ));
            let upper_combined =
                Duration::from_secs_f64(static_pred.prediction_interval.1.as_secs_f64().mul_add(
                    static_weight,
                    ml_pred.prediction_interval.1.as_secs_f64() * ml_weight,
                ));

            Ok(PredictionResult {
                predicted_time,
                confidence,
                prediction_interval: (lower_combined, upper_combined),
                model_type: ModelType::LinearRegression, // Hybrid
                feature_importance: ml_pred.feature_importance,
                metadata: PredictionMetadata {
                    prediction_time: Duration::from_millis(6),
                    samples_used: ml_pred.metadata.samples_used,
                    model_trained: ml_pred.metadata.model_trained,
                    cv_score: ml_pred.metadata.cv_score,
                    prediction_method: "Hybrid (Static + ML)".to_string(),
                },
            })
        } else {
            // Fall back to static analysis
            Ok(static_pred)
        }
    }

    /// Predict using ensemble of models
    fn predict_with_ensemble(
        &mut self,
        complexity: &ComplexityMetrics,
        backend_type: BackendType,
    ) -> Result<PredictionResult> {
        // For now, ensemble is the same as hybrid
        // In a full implementation, this would use multiple ML models
        self.predict_with_hybrid(complexity, backend_type)
    }

    /// Train machine learning model for a specific backend
    fn train_model_for_backend(&mut self, backend_type: BackendType) -> Result<()> {
        // Simplified model training
        // In a real implementation, this would use proper ML libraries

        let training_data: Vec<_> = self
            .execution_history
            .iter()
            .filter(|data| data.backend_type == backend_type && data.success)
            .collect();

        if training_data.is_empty() {
            return Err(SimulatorError::ComputationError(
                "No training data available".to_string(),
            ));
        }

        // Simple linear regression model
        let model = TrainedModel {
            model_type: ModelType::LinearRegression,
            parameters: vec![1.0, 0.5, 0.3], // Simplified coefficients
            feature_weights: self.calculate_feature_weights(&training_data)?,
            training_stats: TrainingStatistics {
                training_samples: training_data.len(),
                training_accuracy: 0.85, // Simplified
                validation_accuracy: 0.80,
                mean_absolute_error: 0.1,
                root_mean_square_error: 0.15,
                training_time: Duration::from_millis(100),
            },
            last_trained: std::time::SystemTime::now(),
        };

        self.trained_models.insert(backend_type, model);
        self.prediction_stats.model_updates += 1;

        Ok(())
    }

    /// Calculate feature weights for training
    fn calculate_feature_weights(
        &self,
        training_data: &[&ExecutionDataPoint],
    ) -> Result<HashMap<String, f64>> {
        let mut weights = HashMap::new();

        // Simplified feature importance calculation
        weights.insert("gate_count".to_string(), 0.3);
        weights.insert("circuit_depth".to_string(), 0.25);
        weights.insert("qubit_count".to_string(), 0.2);
        weights.insert("entanglement_complexity".to_string(), 0.15);
        weights.insert("parallelism_factor".to_string(), 0.1);

        Ok(weights)
    }

    /// Apply trained model to make prediction
    fn apply_model(&self, model: &TrainedModel, complexity: &ComplexityMetrics) -> Result<f64> {
        // Simplified model application
        let base_prediction = complexity.resource_estimation.cpu_time_estimate;

        // Apply model coefficients
        let gate_factor =
            model.parameters.first().unwrap_or(&1.0) * (complexity.gate_count as f64).ln();
        let depth_factor =
            model.parameters.get(1).unwrap_or(&1.0) * complexity.circuit_depth as f64;
        let qubit_factor =
            model.parameters.get(2).unwrap_or(&1.0) * (complexity.qubit_count as f64).powi(2);

        let prediction = base_prediction
            * (1.0 + gate_factor * 1e-6 + depth_factor * 1e-4 + qubit_factor * 1e-3);

        Ok(prediction)
    }

    /// Record actual execution time for model improvement
    pub fn record_execution(&mut self, data_point: ExecutionDataPoint) -> Result<()> {
        // Add to history
        self.execution_history.push_back(data_point.clone());

        // Maintain size limit
        if self.execution_history.len() > self.config.max_history_size {
            self.execution_history.pop_front();
        }

        // Update prediction accuracy if we have a prediction for this data
        self.update_prediction_accuracy(&data_point);

        // Retrain models periodically
        if self.execution_history.len() % 100 == 0 {
            self.retrain_models()?;
        }

        Ok(())
    }

    /// Update prediction accuracy statistics
    const fn update_prediction_accuracy(&mut self, data_point: &ExecutionDataPoint) {
        // This would compare actual vs predicted times
        // Simplified implementation
        if data_point.success {
            self.prediction_stats.successful_predictions += 1;
        }
    }

    /// Retrain all models with latest data
    fn retrain_models(&mut self) -> Result<()> {
        let backends = vec![
            BackendType::StateVector,
            BackendType::SciRS2Gpu,
            BackendType::LargeScale,
            BackendType::Distributed,
        ];

        for backend in backends {
            if self
                .execution_history
                .iter()
                .any(|d| d.backend_type == backend)
            {
                self.train_model_for_backend(backend)?;
            }
        }

        Ok(())
    }

    /// Detect current hardware specifications
    fn detect_hardware_specs() -> Result<PerformanceHardwareSpecs> {
        // Simplified hardware detection
        Ok(PerformanceHardwareSpecs {
            cpu_cores: num_cpus::get(),
            total_memory: 16 * 1024 * 1024 * 1024, // 16GB default
            available_memory: 12 * 1024 * 1024 * 1024, // 12GB available
            gpu_memory: Some(8 * 1024 * 1024 * 1024), // 8GB GPU
            cpu_frequency: 3000.0,                 // 3GHz
            network_bandwidth: Some(1000.0),       // 1Gbps
            load_average: 0.5,
        })
    }

    /// Update timing statistics
    const fn update_timing_stats(&self, elapsed: Duration) {
        // Update timing statistics
        // Simplified implementation
    }

    /// Get prediction engine statistics
    #[must_use]
    pub const fn get_statistics(&self) -> &PredictionStatistics {
        &self.prediction_stats
    }

    /// Export prediction models for persistence
    pub fn export_models(&self) -> Result<Vec<u8>> {
        // Serialize models for storage
        let serialized = serde_json::to_vec(&self.trained_models)
            .map_err(|e| SimulatorError::ComputationError(format!("Serialization error: {e}")))?;
        Ok(serialized)
    }

    /// Import prediction models from storage
    pub fn import_models(&mut self, _data: &[u8]) -> Result<()> {
        // Note: Import functionality disabled due to SystemTime serialization limitations
        // In a full implementation, would use a custom serialization format or different time representation
        Err(SimulatorError::ComputationError(
            "Import not supported in current implementation".to_string(),
        ))
    }
}

impl Default for ResourceMetrics {
    fn default() -> Self {
        Self {
            cpu_time_estimate: 0.0,
            memory_usage_estimate: 0,
            io_operations_estimate: 0,
            network_bandwidth_estimate: 0,
            gpu_memory_estimate: 0,
            thread_requirement: 1,
        }
    }
}

impl Default for PredictionStatistics {
    fn default() -> Self {
        Self {
            total_predictions: 0,
            successful_predictions: 0,
            average_accuracy: 0.0,
            prediction_time_stats: PerformanceTimingStatistics {
                average: Duration::from_millis(0),
                minimum: Duration::from_millis(0),
                maximum: Duration::from_millis(0),
                std_deviation: Duration::from_millis(0),
            },
            model_updates: 0,
            cache_hit_rate: 0.0,
        }
    }
}

/// Convenience function to create a performance prediction engine with default config
pub fn create_performance_predictor() -> Result<PerformancePredictionEngine> {
    PerformancePredictionEngine::new(PerformancePredictionConfig::default())
}

/// Convenience function to predict execution time for a circuit
pub fn predict_circuit_execution_time<const N: usize>(
    predictor: &mut PerformancePredictionEngine,
    circuit: &Circuit<N>,
    backend_type: BackendType,
) -> Result<PredictionResult> {
    predictor.predict_execution_time(circuit, backend_type)
}