quantrs2-device 0.1.3

//! Main executor for dynamical decoupling operations

use std::time::Instant;

use quantrs2_core::qubit::QubitId;

use crate::{calibration::CalibrationManager, topology::HardwareTopology, DeviceResult};

use super::{
    analysis::DDStatisticalAnalyzer,
    config::DynamicalDecouplingConfig,
    hardware::DDHardwareAnalyzer,
    noise::DDNoiseAnalyzer,
    optimization::DDSequenceOptimizer,
    performance::DDPerformanceAnalyzer,
    sequences::{DDSequenceGenerator, SequenceCache},
    validation::DDValidator,
    DDCircuitExecutor, DynamicalDecouplingResult,
};

// SciRS2 fallback
#[cfg(not(feature = "scirs2"))]
mod fallback_scirs2 {
    use scirs2_core::ndarray::{Array1, Array2};

    pub fn mean(_data: &scirs2_core::ndarray::ArrayView1<f64>) -> Result<f64, String> {
        Ok(0.0)
    }
    pub fn std(_data: &scirs2_core::ndarray::ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
        Ok(1.0)
    }
    pub fn pearsonr(
        _x: &scirs2_core::ndarray::ArrayView1<f64>,
        _y: &scirs2_core::ndarray::ArrayView1<f64>,
        _alt: &str,
    ) -> Result<(f64, f64), String> {
        Ok((0.0, 0.5))
    }
    pub fn trace(_matrix: &scirs2_core::ndarray::ArrayView2<f64>) -> Result<f64, String> {
        Ok(1.0)
    }
    pub fn inv(_matrix: &scirs2_core::ndarray::ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Ok(Array2::eye(2))
    }

    pub struct OptimizeResult {
        pub x: Array1<f64>,
        pub fun: f64,
        pub success: bool,
        pub nit: usize,
        pub nfev: usize,
        pub message: String,
    }

    pub fn minimize(
        _func: fn(&Array1<f64>) -> f64,
        _x0: &Array1<f64>,
        _method: &str,
    ) -> Result<OptimizeResult, String> {
        Ok(OptimizeResult {
            x: Array1::zeros(2),
            fun: 0.0,
            success: true,
            nit: 0,
            nfev: 0,
            message: "Fallback optimization".to_string(),
        })
    }
}

/// Main dynamical decoupling executor
#[derive(Debug, Clone)]
pub struct DynamicalDecouplingExecutor {
    /// Configuration
    pub config: DynamicalDecouplingConfig,
    /// Calibration manager
    pub calibration_manager: CalibrationManager,
    /// Hardware topology
    pub device_topology: Option<HardwareTopology>,
    /// Sequence cache
    pub sequence_cache: SequenceCache,
}

impl DynamicalDecouplingExecutor {
    /// Create a new DD executor
    pub fn new(
        config: DynamicalDecouplingConfig,
        calibration_manager: CalibrationManager,
        device_topology: Option<HardwareTopology>,
    ) -> Self {
        Self {
            config,
            calibration_manager,
            device_topology,
            sequence_cache: SequenceCache::new(),
        }
    }

    /// Generate and optimize DD sequence with comprehensive analysis
    pub async fn generate_optimized_sequence(
        &mut self,
        device_id: &str,
        target_qubits: &[QubitId],
        sequence_duration: f64,
        executor: &dyn DDCircuitExecutor,
    ) -> DeviceResult<DynamicalDecouplingResult> {
        let start_time = Instant::now();

        // Step 1: Generate base DD sequence
        let base_sequence = self.generate_base_sequence(target_qubits, sequence_duration)?;

        // Step 2: Optimize sequence if enabled
        let optimized_sequence = if self.config.optimization_config.enable_optimization {
            self.optimize_sequence(&base_sequence, executor).await?
        } else {
            base_sequence
        };

        // Step 3: Analyze performance
        let performance_analysis = self
            .analyze_performance(&optimized_sequence, executor)
            .await?;

        // Step 4: Statistical analysis
        let statistical_analysis =
            self.perform_statistical_analysis(&optimized_sequence, &performance_analysis)?;

        // Step 5: Hardware analysis
        let hardware_analysis =
            self.analyze_hardware_implementation(device_id, &optimized_sequence)?;

        // Step 6: Noise analysis
        let noise_analysis =
            self.analyze_noise_characteristics(&optimized_sequence, &performance_analysis)?;

        // Step 7: Validation
        let validation_results = if self.config.validation_config.enable_validation {
            self.perform_validation(&optimized_sequence, executor)
                .await?
        } else {
            super::validation::DDValidationResults {
                cross_validation: None,
                out_of_sample: None,
                robustness_tests: super::validation::RobustnessTestResults {
                    parameter_sensitivity_results: std::collections::HashMap::new(),
                    noise_sensitivity_results: std::collections::HashMap::new(),
                    hardware_variation_results: super::validation::HardwareVariationResults {
                        variation_tolerance: 0.8,
                        performance_degradation: std::collections::HashMap::new(),
                        adaptation_effectiveness: 0.9,
                    },
                    systematic_error_results: super::validation::SystematicErrorResults {
                        error_types_tested: Vec::new(),
                        error_tolerance: std::collections::HashMap::new(),
                        mitigation_strategies: Vec::new(),
                    },
                },
                generalization_analysis: super::validation::GeneralizationAnalysis {
                    generalization_score: 0.8,
                    transfer_performance: super::validation::TransferPerformance {
                        source_performance: 0.9,
                        target_performance: 0.8,
                        transfer_efficiency: 0.85,
                        knowledge_retention: 0.75,
                    },
                    domain_adaptation: super::validation::DomainAdaptationResults {
                        adaptation_success_rate: 0.8,
                        adaptation_effort: 0.3,
                        adapted_performance: 0.85,
                        adaptation_strategies: vec!["Parameter tuning".to_string()],
                    },
                    scalability_analysis: super::validation::ScalabilityAnalysis {
                        scalability_score: 0.7,
                        scaling_law: super::validation::ScalingLaw {
                            scaling_exponent: 1.2,
                            scaling_coefficient: 1.0,
                            goodness_of_fit: 0.95,
                            scaling_regime: super::validation::ScalingRegime::PowerLaw,
                        },
                        resource_scaling: super::validation::ResourceScaling {
                            time_complexity: 1.5,
                            space_complexity: 1.2,
                            communication_complexity: 1.0,
                            energy_scaling: 1.3,
                        },
                        complexity_analysis: super::validation::ComplexityAnalysis {
                            computational_complexity: "O(n^1.5)".to_string(),
                            sample_complexity: 1000,
                            communication_complexity: "O(n log n)".to_string(),
                            bottlenecks: Vec::new(),
                        },
                    },
                },
            }
        };

        let execution_time = start_time.elapsed();

        // Calculate quality score based on all analyses
        let quality_score = self.calculate_quality_score(
            &performance_analysis,
            &hardware_analysis,
            &noise_analysis,
            &validation_results,
        )?;

        println!("DD sequence generation completed in {execution_time:?}");

        Ok(DynamicalDecouplingResult {
            optimized_sequence,
            execution_time,
            success: true,
            quality_score,
            performance_analysis: None,
            noise_analysis: None,
            hardware_analysis: None,
            adaptation_stats: None,
        })
    }

    /// Generate base DD sequence
    fn generate_base_sequence(
        &mut self,
        target_qubits: &[QubitId],
        sequence_duration: f64,
    ) -> DeviceResult<super::sequences::DDSequence> {
        // Check cache first
        let cache_key = format!(
            "{:?}_{}_{}",
            self.config.sequence_type,
            target_qubits.len(),
            sequence_duration
        );

        if let Some(cached_sequence) = self.sequence_cache.get_sequence(&cache_key) {
            return Ok(cached_sequence);
        }

        // Generate new sequence
        let sequence = DDSequenceGenerator::generate_base_sequence(
            &self.config.sequence_type,
            target_qubits,
            sequence_duration,
        )?;

        // Store in cache
        self.sequence_cache
            .store_sequence(cache_key, sequence.clone());

        Ok(sequence)
    }

    /// Optimize sequence
    async fn optimize_sequence(
        &self,
        base_sequence: &super::sequences::DDSequence,
        executor: &dyn DDCircuitExecutor,
    ) -> DeviceResult<super::sequences::DDSequence> {
        let mut optimizer = DDSequenceOptimizer::new(self.config.optimization_config.clone());

        let optimization_result = optimizer.optimize_sequence(base_sequence, executor).await?;
        Ok(optimization_result.optimized_sequence)
    }

    /// Analyze performance
    async fn analyze_performance(
        &self,
        sequence: &super::sequences::DDSequence,
        executor: &dyn DDCircuitExecutor,
    ) -> DeviceResult<super::performance::DDPerformanceAnalysis> {
        let mut analyzer = DDPerformanceAnalyzer::new(self.config.performance_config.clone());
        analyzer.analyze_performance(sequence, executor).await
    }

    /// Perform statistical analysis
    fn perform_statistical_analysis(
        &self,
        sequence: &super::sequences::DDSequence,
        performance_analysis: &super::performance::DDPerformanceAnalysis,
    ) -> DeviceResult<super::analysis::DDStatisticalAnalysis> {
        DDStatisticalAnalyzer::perform_statistical_analysis(sequence, performance_analysis)
    }

    /// Analyze hardware implementation
    fn analyze_hardware_implementation(
        &self,
        device_id: &str,
        sequence: &super::sequences::DDSequence,
    ) -> DeviceResult<super::hardware::DDHardwareAnalysis> {
        let analyzer = DDHardwareAnalyzer::new(
            self.config.hardware_adaptation.clone(),
            Some(self.calibration_manager.clone()),
            self.device_topology.clone(),
        );

        analyzer.analyze_hardware_implementation(device_id, sequence)
    }

    /// Analyze noise characteristics
    fn analyze_noise_characteristics(
        &self,
        sequence: &super::sequences::DDSequence,
        performance_analysis: &super::performance::DDPerformanceAnalysis,
    ) -> DeviceResult<super::noise::DDNoiseAnalysis> {
        let analyzer = DDNoiseAnalyzer::new(self.config.noise_characterization.clone());
        analyzer.analyze_noise_characteristics(sequence, performance_analysis)
    }

    /// Perform validation
    async fn perform_validation(
        &self,
        sequence: &super::sequences::DDSequence,
        executor: &dyn DDCircuitExecutor,
    ) -> DeviceResult<super::validation::DDValidationResults> {
        let validator = DDValidator::new(self.config.validation_config.clone());
        validator.perform_validation(sequence, executor).await
    }

    /// Calculate overall quality score
    fn calculate_quality_score(
        &self,
        performance_analysis: &super::performance::DDPerformanceAnalysis,
        hardware_analysis: &super::hardware::DDHardwareAnalysis,
        noise_analysis: &super::noise::DDNoiseAnalysis,
        validation_results: &super::validation::DDValidationResults,
    ) -> DeviceResult<f64> {
        // Weighted combination of various quality metrics
        let mut total_score = 0.0;
        let mut total_weight = 0.0;

        // Performance score (weight: 0.3)
        let performance_score = performance_analysis.metrics.values().sum::<f64>()
            / performance_analysis.metrics.len() as f64;
        total_score += 0.3 * performance_score;
        total_weight += 0.3;

        // Hardware compatibility score (weight: 0.2)
        let hardware_score = hardware_analysis.hardware_compatibility.compatibility_score;
        total_score += 0.2 * hardware_score;
        total_weight += 0.2;

        // Noise suppression score (weight: 0.2)
        let noise_score = noise_analysis.suppression_effectiveness.overall_suppression;
        total_score += 0.2 * noise_score;
        total_weight += 0.2;

        // Validation score (weight: 0.3)
        let validation_score = if let Some(cv_results) = &validation_results.cross_validation {
            cv_results.mean_score
        } else {
            validation_results
                .generalization_analysis
                .generalization_score
        };
        total_score += 0.3 * validation_score;
        total_weight += 0.3;

        // Normalize by total weight
        let final_score = if total_weight > 0.0 {
            total_score / total_weight
        } else {
            0.5 // Default score if no valid metrics
        };

        Ok(final_score.clamp(0.0, 1.0)) // Clamp to [0, 1]
    }
}

/// Create default DD executor
pub fn create_dd_executor(
    calibration_manager: CalibrationManager,
    device_topology: Option<HardwareTopology>,
) -> DynamicalDecouplingExecutor {
    DynamicalDecouplingExecutor::new(
        DynamicalDecouplingConfig::default(),
        calibration_manager,
        device_topology,
    )
}

/// Create custom DD executor
pub fn create_custom_dd_executor(
    config: DynamicalDecouplingConfig,
    calibration_manager: CalibrationManager,
    device_topology: Option<HardwareTopology>,
) -> DynamicalDecouplingExecutor {
    DynamicalDecouplingExecutor::new(config, calibration_manager, device_topology)
}