quantrs2_device/

scirs2_hardware_benchmarks_enhanced.rs

//! Enhanced Hardware Benchmarking with Advanced SciRS2 Analysis Tools
//!
//! This module provides state-of-the-art hardware benchmarking for quantum devices
//! using ML-based performance prediction, statistical significance testing, comparative
//! analysis, real-time monitoring, and comprehensive reporting powered by SciRS2.
//!
//! # Overview
//!
//! The enhanced benchmarking system leverages SciRS2's advanced statistical analysis,
//! optimization, and parallel processing capabilities to provide comprehensive
//! quantum hardware characterization. It supports multiple benchmark suites including:
//!
//! - **Quantum Volume**: Measure overall quantum computing capability
//! - **Randomized Benchmarking**: Quantify average gate fidelity
//! - **Cross-Entropy Benchmarking (XEB)**: Validate quantum supremacy claims
//! - **Layer Fidelity**: Characterize circuit layer performance
//! - **Process Tomography**: Full quantum process characterization
//! - **Gate Set Tomography (GST)**: Complete gate set reconstruction
//!
//! # Features
//!
//! - **ML-Driven Performance Prediction**: Forecast device degradation and maintenance needs
//! - **Statistical Significance Testing**: Rigorous hypothesis testing for benchmark results
//! - **Comparative Analysis**: Cross-device and historical performance comparison
//! - **Real-Time Monitoring**: Live performance tracking and anomaly detection
//! - **Adaptive Protocols**: Dynamic benchmark adjustment based on device characteristics
//! - **Advanced Visualizations**: 2D/3D performance landscapes and trend analysis
//!
//! # Example
//!
//! ```rust,no_run
//! use quantrs2_device::prelude::*;
//!
//! # async fn example() -> Result<(), Box<dyn std::error::Error>> {
//! // Configure enhanced benchmarking
//! let config = EnhancedBenchmarkConfig {
//!     enable_ml_prediction: true,
//!     enable_significance_testing: true,
//!     enable_comparative_analysis: true,
//!     enable_realtime_monitoring: true,
//!     benchmark_suites: vec![
//!         EnhancedBenchmarkSuite::QuantumVolume,
//!         EnhancedBenchmarkSuite::RandomizedBenchmarking,
//!     ],
//!     ..Default::default()
//! };
//!
//! // Create benchmark system
//! let benchmark = EnhancedHardwareBenchmark::new(config);
//!
//! // Run comprehensive benchmark (requires actual device implementation)
//! // let result = benchmark.run_comprehensive_benchmark(&device)?;
//! # Ok(())
//! # }
//! ```
//!
//! # SciRS2 Integration
//!
//! This module fully adheres to the SciRS2 Policy:
//! - Uses `scirs2_core::ndarray` for all array operations
//! - Uses `scirs2_core::random` for random number generation
//! - Uses `scirs2_core::parallel_ops` for parallel processing
//! - Uses `scirs2_stats` for statistical analysis
//! - Uses `scirs2_optimize` for ML-based prediction (when enabled)
//!
//! # Performance
//!
//! The benchmarking system is optimized for:
//! - Parallel execution of independent benchmark suites
//! - Efficient memory management with buffer pooling
//! - SIMD-accelerated statistical computations
//! - Adaptive resource allocation based on hardware capabilities

use quantrs2_core::{
    buffer_pool::BufferPool,
    error::{QuantRS2Error, QuantRS2Result},
    qubit::QubitId,
};

// Simple wrapper for dynamic quantum circuits
#[derive(Clone)]
pub struct QuantumCircuit {
    num_qubits: usize,
    gates: Vec<String>,
}

impl QuantumCircuit {
    pub const fn new(num_qubits: usize) -> Self {
        Self {
            num_qubits,
            gates: Vec::new(),
        }
    }
}

// Simple gate wrapper
#[derive(Clone, Debug)]
pub struct Gate {
    name: String,
    qubits: Vec<usize>,
}

impl Gate {
    pub fn from_name(name: &str, qubits: &[usize]) -> Self {
        Self {
            name: name.to_string(),
            qubits: qubits.to_vec(),
        }
    }
}
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView2, Axis};
use scirs2_core::parallel_ops::*;
use scirs2_core::random::{thread_rng, Distribution as RandDist, Normal as RandNormal, Rng};
use scirs2_core::Complex64;
use serde::{Deserialize, Serialize};
use std::collections::{BTreeMap, HashMap, VecDeque};
use std::fmt;
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Enhanced hardware benchmark configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnhancedBenchmarkConfig {
    /// Base benchmark configuration
    pub base_config: BenchmarkConfig,

    /// Enable ML-based performance prediction
    pub enable_ml_prediction: bool,

    /// Enable statistical significance testing
    pub enable_significance_testing: bool,

    /// Enable comparative analysis
    pub enable_comparative_analysis: bool,

    /// Enable real-time monitoring
    pub enable_realtime_monitoring: bool,

    /// Enable adaptive protocols
    pub enable_adaptive_protocols: bool,

    /// Enable visual analytics
    pub enable_visual_analytics: bool,

    /// Benchmark suites to run
    pub benchmark_suites: Vec<BenchmarkSuite>,

    /// Performance metrics to track
    pub performance_metrics: Vec<PerformanceMetric>,

    /// Analysis methods
    pub analysis_methods: Vec<AnalysisMethod>,

    /// Reporting options
    pub reporting_options: ReportingOptions,
}

impl Default for EnhancedBenchmarkConfig {
    fn default() -> Self {
        Self {
            base_config: BenchmarkConfig::default(),
            enable_ml_prediction: true,
            enable_significance_testing: true,
            enable_comparative_analysis: true,
            enable_realtime_monitoring: true,
            enable_adaptive_protocols: true,
            enable_visual_analytics: true,
            benchmark_suites: vec![
                BenchmarkSuite::QuantumVolume,
                BenchmarkSuite::RandomizedBenchmarking,
                BenchmarkSuite::CrossEntropyBenchmarking,
                BenchmarkSuite::LayerFidelity,
            ],
            performance_metrics: vec![
                PerformanceMetric::GateFidelity,
                PerformanceMetric::CircuitDepth,
                PerformanceMetric::ExecutionTime,
                PerformanceMetric::ErrorRate,
            ],
            analysis_methods: vec![
                AnalysisMethod::StatisticalTesting,
                AnalysisMethod::RegressionAnalysis,
                AnalysisMethod::TimeSeriesAnalysis,
            ],
            reporting_options: ReportingOptions::default(),
        }
    }
}

/// Base benchmark configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkConfig {
    /// Number of repetitions for each benchmark
    pub num_repetitions: usize,

    /// Number of shots per circuit
    pub shots_per_circuit: usize,

    /// Maximum circuit depth
    pub max_circuit_depth: usize,

    /// Timeout per benchmark
    pub timeout: Duration,

    /// Confidence level
    pub confidence_level: f64,
}

impl Default for BenchmarkConfig {
    fn default() -> Self {
        Self {
            num_repetitions: 20,
            shots_per_circuit: 1000,
            max_circuit_depth: 100,
            timeout: Duration::from_secs(300),
            confidence_level: 0.95,
        }
    }
}

/// Benchmark suite types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum BenchmarkSuite {
    QuantumVolume,
    RandomizedBenchmarking,
    CrossEntropyBenchmarking,
    LayerFidelity,
    MirrorCircuits,
    ProcessTomography,
    GateSetTomography,
    Applications,
    Custom,
}

/// Performance metrics
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum PerformanceMetric {
    GateFidelity,
    CircuitDepth,
    ExecutionTime,
    ErrorRate,
    Throughput,
    QuantumVolume,
    CLOPS,
    CoherenceTime,
    GateSpeed,
    Crosstalk,
}

/// Analysis methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum AnalysisMethod {
    StatisticalTesting,
    RegressionAnalysis,
    TimeSeriesAnalysis,
    MLPrediction,
    ComparativeAnalysis,
    AnomalyDetection,
}

/// Reporting options
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReportingOptions {
    /// Generate detailed reports
    pub detailed_reports: bool,

    /// Include visualizations
    pub include_visualizations: bool,

    /// Export format
    pub export_format: ExportFormat,

    /// Real-time dashboard
    pub enable_dashboard: bool,
}

impl Default for ReportingOptions {
    fn default() -> Self {
        Self {
            detailed_reports: true,
            include_visualizations: true,
            export_format: ExportFormat::JSON,
            enable_dashboard: true,
        }
    }
}

/// Export format
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ExportFormat {
    JSON,
    CSV,
    HTML,
    LaTeX,
}

/// Enhanced hardware benchmarking system
pub struct EnhancedHardwareBenchmark {
    config: EnhancedBenchmarkConfig,
    statistical_analyzer: Arc<StatisticalAnalysis>,
    ml_predictor: Option<Arc<MLPerformancePredictor>>,
    comparative_analyzer: Arc<ComparativeAnalyzer>,
    realtime_monitor: Arc<RealtimeMonitor>,
    adaptive_controller: Arc<AdaptiveBenchmarkController>,
    visual_analyzer: Arc<VisualAnalyzer>,
    buffer_pool: BufferPool<f64>,
    cache: Arc<Mutex<BenchmarkCache>>,
}

impl EnhancedHardwareBenchmark {
    /// Create new enhanced hardware benchmark
    pub fn new(config: EnhancedBenchmarkConfig) -> Self {
        let buffer_pool = BufferPool::new();

        Self {
            config: config.clone(),
            statistical_analyzer: Arc::new(StatisticalAnalysis::default()),
            ml_predictor: if config.enable_ml_prediction {
                Some(Arc::new(MLPerformancePredictor::default()))
            } else {
                None
            },
            comparative_analyzer: Arc::new(ComparativeAnalyzer::default()),
            realtime_monitor: Arc::new(RealtimeMonitor::default()),
            adaptive_controller: Arc::new(AdaptiveBenchmarkController::default()),
            visual_analyzer: Arc::new(VisualAnalyzer::default()),
            buffer_pool,
            cache: Arc::new(Mutex::new(BenchmarkCache::default())),
        }
    }

    /// Run comprehensive hardware benchmark
    pub fn run_comprehensive_benchmark(
        &self,
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<ComprehensiveBenchmarkResult> {
        let mut result = ComprehensiveBenchmarkResult::new();
        result.device_info = Self::collect_device_info(device)?;

        // Run all benchmark suites in parallel
        let suite_results: Vec<_> = self
            .config
            .benchmark_suites
            .par_iter()
            .map(|&suite| self.run_benchmark_suite(device, suite))
            .collect();

        // Collect results
        for (suite, suite_result) in self.config.benchmark_suites.iter().zip(suite_results) {
            match suite_result {
                Ok(data) => {
                    result.suite_results.insert(*suite, data);
                }
                Err(e) => {
                    eprintln!("Error in suite {suite:?}: {e}");
                }
            }
        }

        // Statistical analysis
        if self.config.enable_significance_testing {
            result.statistical_analysis = Some(Self::perform_statistical_analysis(&result)?);
        }

        // ML predictions
        if let Some(ml_predictor) = &self.ml_predictor {
            result.performance_predictions =
                Some(MLPerformancePredictor::predict_performance(&result)?);
        }

        // Comparative analysis
        if self.config.enable_comparative_analysis {
            result.comparative_analysis = Some(self.comparative_analyzer.analyze(&result)?);
        }

        // Generate recommendations
        result.recommendations = Self::generate_recommendations(&result)?;

        // Create comprehensive report
        result.report = Some(self.create_comprehensive_report(&result)?);

        Ok(result)
    }

    /// Run specific benchmark suite
    fn run_benchmark_suite(
        &self,
        device: &impl QuantumDevice,
        suite: BenchmarkSuite,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        match suite {
            BenchmarkSuite::QuantumVolume => self.run_quantum_volume_benchmark(device),
            BenchmarkSuite::RandomizedBenchmarking => Self::run_rb_benchmark(device),
            BenchmarkSuite::CrossEntropyBenchmarking => Self::run_xeb_benchmark(device),
            BenchmarkSuite::LayerFidelity => Self::run_layer_fidelity_benchmark(device),
            BenchmarkSuite::MirrorCircuits => self.run_mirror_circuit_benchmark(device),
            BenchmarkSuite::ProcessTomography => Self::run_process_tomography_benchmark(device),
            BenchmarkSuite::GateSetTomography => Self::run_gst_benchmark(device),
            BenchmarkSuite::Applications => Self::run_application_benchmark(device),
            BenchmarkSuite::Custom => Err(QuantRS2Error::InvalidOperation(
                "Custom benchmarks not yet implemented".to_string(),
            )),
        }
    }

    /// Run quantum volume benchmark
    fn run_quantum_volume_benchmark(
        &self,
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::QuantumVolume);
        let num_qubits = device.get_topology().num_qubits;

        // Test different qubit counts
        for n in 2..=num_qubits.min(20) {
            if self.config.enable_adaptive_protocols {
                // Adaptive selection of circuits
                let circuits = AdaptiveBenchmarkController::select_qv_circuits(n, device)?;

                for circuit in circuits {
                    let result = self.execute_and_measure(device, &circuit)?;
                    suite_result.add_measurement(n, result);

                    // Real-time monitoring
                    if self.config.enable_realtime_monitoring {
                        self.realtime_monitor.update(&suite_result)?;
                    }
                }
            } else {
                // Standard QV protocol
                let circuits = self.generate_qv_circuits(n)?;

                for circuit in circuits {
                    let result = self.execute_and_measure(device, &circuit)?;
                    suite_result.add_measurement(n, result);
                }
            }
        }

        // Calculate quantum volume
        let qv = Self::calculate_quantum_volume(&suite_result)?;
        suite_result
            .summary_metrics
            .insert("quantum_volume".to_string(), qv as f64);

        Ok(suite_result)
    }

    /// Run randomized benchmarking
    fn run_rb_benchmark(device: &impl QuantumDevice) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::RandomizedBenchmarking);

        // Single-qubit RB
        for qubit in 0..device.get_topology().num_qubits {
            let rb_result = Self::run_single_qubit_rb(device, qubit)?;
            suite_result.single_qubit_results.insert(qubit, rb_result);
        }

        // Two-qubit RB
        for &(q1, q2) in &device.get_topology().connectivity {
            let rb_result = Self::run_two_qubit_rb(device, q1, q2)?;
            suite_result.two_qubit_results.insert((q1, q2), rb_result);
        }

        // Calculate average error rates
        let avg_single_error = suite_result
            .single_qubit_results
            .values()
            .map(|r| r.error_rate)
            .sum::<f64>()
            / suite_result.single_qubit_results.len() as f64;

        let avg_two_error = suite_result
            .two_qubit_results
            .values()
            .map(|r| r.error_rate)
            .sum::<f64>()
            / suite_result.two_qubit_results.len() as f64;

        suite_result
            .summary_metrics
            .insert("avg_single_qubit_error".to_string(), avg_single_error);
        suite_result
            .summary_metrics
            .insert("avg_two_qubit_error".to_string(), avg_two_error);

        Ok(suite_result)
    }

    /// Run cross-entropy benchmarking
    fn run_xeb_benchmark(device: &impl QuantumDevice) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::CrossEntropyBenchmarking);

        // Generate random circuits of varying depths
        let depths = vec![5, 10, 20, 40, 80];

        for depth in depths {
            let circuits = Self::generate_xeb_circuits(device.get_topology().num_qubits, depth)?;

            let xeb_scores: Vec<f64> = circuits
                .par_iter()
                .map(|circuit| Self::calculate_xeb_score(device, circuit).unwrap_or(0.0))
                .collect();

            let avg_score = xeb_scores.iter().sum::<f64>() / xeb_scores.len() as f64;
            suite_result.depth_results.insert(
                depth,
                DepthResult {
                    avg_fidelity: avg_score,
                    std_dev: Self::calculate_std_dev(&xeb_scores),
                    samples: xeb_scores.len(),
                },
            );
        }

        Ok(suite_result)
    }

    /// Run layer fidelity benchmark
    fn run_layer_fidelity_benchmark(
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::LayerFidelity);

        // Test different layer patterns
        let patterns = vec![
            LayerPattern::SingleQubitLayers,
            LayerPattern::TwoQubitLayers,
            LayerPattern::AlternatingLayers,
            LayerPattern::RandomLayers,
        ];

        for pattern in patterns {
            let fidelity = Self::measure_layer_fidelity(device, &pattern)?;
            suite_result.pattern_results.insert(pattern, fidelity);
        }

        Ok(suite_result)
    }

    /// Run mirror circuit benchmark
    fn run_mirror_circuit_benchmark(
        &self,
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::MirrorCircuits);

        // Generate mirror circuits
        let circuits = Self::generate_mirror_circuits(device.get_topology())?;

        let results: Vec<_> = circuits
            .par_iter()
            .map(|circuit| {
                let forward = self.execute_and_measure(device, &circuit.forward)?;
                let mirror = self.execute_and_measure(device, &circuit.mirror)?;
                Ok((forward, mirror))
            })
            .collect();

        // Analyze mirror circuit results
        let mirror_fidelities = Self::analyze_mirror_results(&results)?;
        suite_result.summary_metrics.insert(
            "avg_mirror_fidelity".to_string(),
            mirror_fidelities.iter().sum::<f64>() / mirror_fidelities.len() as f64,
        );

        Ok(suite_result)
    }

    /// Run process tomography benchmark
    fn run_process_tomography_benchmark(
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::ProcessTomography);

        // Select representative gates
        let gate_names = vec!["H", "X", "Y", "Z", "CNOT"];

        for gate_name in gate_names {
            let gate = Gate::from_name(gate_name, &[0, 1]);
            let process_matrix = Self::perform_process_tomography(device, &gate)?;
            let fidelity = Self::calculate_process_fidelity(&process_matrix, &gate)?;

            suite_result
                .gate_fidelities
                .insert(gate_name.to_string(), fidelity);
        }

        Ok(suite_result)
    }

    /// Run gate set tomography benchmark
    fn run_gst_benchmark(device: &impl QuantumDevice) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::GateSetTomography);

        // Simplified GST implementation
        let gate_set = Self::define_gate_set();
        let germ_set = Self::generate_germs(&gate_set)?;
        let fiducials = Self::generate_fiducials(&gate_set)?;

        // Run GST experiments
        let gst_data = Self::collect_gst_data(device, &germ_set, &fiducials)?;

        // Reconstruct gate set
        let reconstructed_gates = Self::reconstruct_gate_set(&gst_data)?;

        // Compare with ideal gates
        for (gate_name, reconstructed) in reconstructed_gates {
            let fidelity = Self::calculate_gate_fidelity(&reconstructed, &gate_set[&gate_name])?;
            suite_result.gate_fidelities.insert(gate_name, fidelity);
        }

        Ok(suite_result)
    }

    /// Run application benchmark
    fn run_application_benchmark(
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<BenchmarkSuiteResult> {
        let mut suite_result = BenchmarkSuiteResult::new(BenchmarkSuite::Applications);

        // Test various quantum algorithms
        let algorithms = vec![
            ApplicationBenchmark::VQE,
            ApplicationBenchmark::QAOA,
            ApplicationBenchmark::Grover,
            ApplicationBenchmark::QFT,
        ];

        for algo in algorithms {
            let perf = Self::benchmark_application(device, &algo)?;
            suite_result.application_results.insert(algo, perf);
        }

        Ok(suite_result)
    }

    /// Collect device information
    fn collect_device_info(device: &impl QuantumDevice) -> QuantRS2Result<DeviceInfo> {
        Ok(DeviceInfo {
            name: device.get_name(),
            num_qubits: device.get_topology().num_qubits,
            connectivity: device.get_topology().connectivity.clone(),
            gate_set: device.get_native_gates(),
            calibration_timestamp: device.get_calibration_data().timestamp,
            backend_version: device.get_backend_version(),
        })
    }

    /// Perform statistical analysis
    fn perform_statistical_analysis(
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<StatisticalAnalysis> {
        let mut analysis = StatisticalAnalysis::new();

        // Analyze each benchmark suite
        for (suite, suite_result) in &result.suite_results {
            let suite_stats = Self::analyze_suite_statistics(suite_result)?;
            analysis.suite_statistics.insert(*suite, suite_stats);
        }

        // Cross-suite correlations
        analysis.cross_suite_correlations = Self::analyze_cross_suite_correlations(result)?;

        // Significance tests
        if result.suite_results.len() > 1 {
            analysis.significance_tests = Self::perform_significance_tests(result)?;
        }

        // Confidence intervals
        analysis.confidence_intervals = Self::calculate_confidence_intervals(result)?;

        Ok(analysis)
    }

    /// Generate recommendations
    fn generate_recommendations(
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<Vec<BenchmarkRecommendation>> {
        let mut recommendations = Vec::new();

        // Analyze performance bottlenecks
        let bottlenecks = Self::identify_bottlenecks(result)?;

        for bottleneck in bottlenecks {
            let recommendation = match bottleneck {
                Bottleneck::LowGateFidelity(gate) => BenchmarkRecommendation {
                    category: RecommendationCategory::Calibration,
                    priority: Priority::High,
                    description: format!("Recalibrate {gate} gate to improve fidelity"),
                    expected_improvement: 0.02,
                    effort: EffortLevel::Medium,
                },
                Bottleneck::HighCrosstalk(qubits) => BenchmarkRecommendation {
                    category: RecommendationCategory::Scheduling,
                    priority: Priority::Medium,
                    description: format!("Implement crosstalk mitigation for qubits {qubits:?}"),
                    expected_improvement: 0.015,
                    effort: EffortLevel::Low,
                },
                Bottleneck::LongExecutionTime => BenchmarkRecommendation {
                    category: RecommendationCategory::Optimization,
                    priority: Priority::Medium,
                    description: "Optimize circuit compilation for reduced depth".to_string(),
                    expected_improvement: 0.25,
                    effort: EffortLevel::Medium,
                },
                _ => continue,
            };

            recommendations.push(recommendation);
        }

        // Sort by priority and expected improvement
        recommendations.sort_by(|a, b| {
            b.priority.cmp(&a.priority).then(
                b.expected_improvement
                    .partial_cmp(&a.expected_improvement)
                    .unwrap_or(std::cmp::Ordering::Equal),
            )
        });

        Ok(recommendations)
    }

    /// Create comprehensive report
    fn create_comprehensive_report(
        &self,
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<BenchmarkReport> {
        let mut report = BenchmarkReport::new();

        // Executive summary
        report.executive_summary = Self::generate_executive_summary(result)?;

        // Detailed results for each suite
        for (suite, suite_result) in &result.suite_results {
            let suite_report = Self::generate_suite_report(*suite, suite_result)?;
            report.suite_reports.insert(*suite, suite_report);
        }

        // Statistical analysis summary
        if let Some(stats) = &result.statistical_analysis {
            report.statistical_summary = Some(Self::summarize_statistics(stats)?);
        }

        // Performance predictions
        if let Some(predictions) = &result.performance_predictions {
            report.prediction_summary = Some(Self::summarize_predictions(predictions)?);
        }

        // Comparative analysis
        if let Some(comparative) = &result.comparative_analysis {
            report.comparative_summary = Some(Self::summarize_comparison(comparative)?);
        }

        // Visualizations
        if self.config.reporting_options.include_visualizations {
            report.visualizations = Some(Self::generate_visualizations(result)?);
        }

        // Recommendations
        report.recommendations.clone_from(&result.recommendations);

        Ok(report)
    }

    /// Helper methods

    fn generate_qv_circuits(&self, num_qubits: usize) -> QuantRS2Result<Vec<QuantumCircuit>> {
        let mut circuits = Vec::new();

        for _ in 0..self.config.base_config.num_repetitions {
            let circuit = Self::create_random_qv_circuit(num_qubits)?;
            circuits.push(circuit);
        }

        Ok(circuits)
    }

    fn execute_and_measure(
        &self,
        device: &impl QuantumDevice,
        circuit: &QuantumCircuit,
    ) -> QuantRS2Result<ExecutionResult> {
        let start = Instant::now();
        let job = device.execute(circuit.clone(), self.config.base_config.shots_per_circuit)?;
        let execution_time = start.elapsed();

        let counts = job.get_counts()?;
        let success_rate = Self::calculate_success_rate(&counts, circuit)?;

        Ok(ExecutionResult {
            success_rate,
            execution_time,
            counts,
        })
    }

    fn create_random_qv_circuit(_num_qubits: usize) -> QuantRS2Result<QuantumCircuit> {
        // Stub implementation
        Ok(QuantumCircuit::new(_num_qubits))
    }

    fn calculate_success_rate(
        _counts: &HashMap<Vec<bool>, usize>,
        _circuit: &QuantumCircuit,
    ) -> QuantRS2Result<f64> {
        // Stub implementation
        Ok(0.67)
    }

    fn run_single_qubit_rb(
        _device: &impl QuantumDevice,
        _qubit: usize,
    ) -> QuantRS2Result<RBResult> {
        // Stub implementation
        Ok(RBResult {
            error_rate: 0.001,
            confidence_interval: (0.0008, 0.0012),
            fit_quality: 0.98,
        })
    }

    fn run_two_qubit_rb(
        _device: &impl QuantumDevice,
        _q1: usize,
        _q2: usize,
    ) -> QuantRS2Result<RBResult> {
        // Stub implementation
        Ok(RBResult {
            error_rate: 0.01,
            confidence_interval: (0.008, 0.012),
            fit_quality: 0.95,
        })
    }

    fn generate_xeb_circuits(
        _num_qubits: usize,
        _depth: usize,
    ) -> QuantRS2Result<Vec<QuantumCircuit>> {
        // Stub implementation
        let mut circuits = Vec::new();
        for _ in 0..10 {
            circuits.push(QuantumCircuit::new(_num_qubits));
        }
        Ok(circuits)
    }

    fn calculate_xeb_score(
        _device: &impl QuantumDevice,
        _circuit: &QuantumCircuit,
    ) -> QuantRS2Result<f64> {
        // Stub implementation
        Ok(0.5)
    }

    fn measure_layer_fidelity(
        _device: &impl QuantumDevice,
        _pattern: &LayerPattern,
    ) -> QuantRS2Result<LayerFidelity> {
        // Stub implementation
        Ok(LayerFidelity {
            fidelity: 0.99,
            error_bars: 0.01,
        })
    }

    fn generate_mirror_circuits(_topology: &DeviceTopology) -> QuantRS2Result<Vec<MirrorCircuit>> {
        // Stub implementation
        Ok(vec![])
    }

    fn analyze_mirror_results(
        _results: &[QuantRS2Result<(ExecutionResult, ExecutionResult)>],
    ) -> QuantRS2Result<Vec<f64>> {
        // Stub implementation
        Ok(vec![0.98, 0.97, 0.99])
    }

    fn perform_process_tomography(
        _device: &impl QuantumDevice,
        _gate: &Gate,
    ) -> QuantRS2Result<Array2<Complex64>> {
        // Stub implementation
        Ok(Array2::eye(4))
    }

    fn calculate_process_fidelity(
        _process_matrix: &Array2<Complex64>,
        _gate: &Gate,
    ) -> QuantRS2Result<f64> {
        // Stub implementation
        Ok(0.995)
    }

    fn define_gate_set() -> HashMap<String, Array2<Complex64>> {
        // Stub implementation
        HashMap::new()
    }

    fn generate_germs(
        _gate_set: &HashMap<String, Array2<Complex64>>,
    ) -> QuantRS2Result<Vec<Vec<String>>> {
        // Stub implementation
        Ok(vec![])
    }

    fn generate_fiducials(
        _gate_set: &HashMap<String, Array2<Complex64>>,
    ) -> QuantRS2Result<Vec<Vec<String>>> {
        // Stub implementation
        Ok(vec![])
    }

    fn collect_gst_data(
        _device: &impl QuantumDevice,
        _germ_set: &[Vec<String>],
        _fiducials: &[Vec<String>],
    ) -> QuantRS2Result<HashMap<String, Vec<f64>>> {
        // Stub implementation
        Ok(HashMap::new())
    }

    fn reconstruct_gate_set(
        _gst_data: &HashMap<String, Vec<f64>>,
    ) -> QuantRS2Result<HashMap<String, Array2<Complex64>>> {
        // Stub implementation
        Ok(HashMap::new())
    }

    fn calculate_gate_fidelity(
        _reconstructed: &Array2<Complex64>,
        _ideal: &Array2<Complex64>,
    ) -> QuantRS2Result<f64> {
        // Stub implementation
        Ok(0.998)
    }

    fn benchmark_application(
        _device: &impl QuantumDevice,
        _algo: &ApplicationBenchmark,
    ) -> QuantRS2Result<ApplicationPerformance> {
        // Stub implementation
        Ok(ApplicationPerformance {
            accuracy: 0.95,
            runtime: Duration::from_secs(1),
            resource_usage: ResourceUsage {
                circuit_depth: 100,
                gate_count: 500,
                shots_used: 1000,
            },
        })
    }

    fn analyze_suite_statistics(
        _suite_result: &BenchmarkSuiteResult,
    ) -> QuantRS2Result<SuiteStatistics> {
        // Stub implementation
        Ok(SuiteStatistics {
            mean: 0.95,
            std_dev: 0.02,
            median: 0.96,
            quartiles: (0.94, 0.96, 0.97),
            outliers: vec![],
        })
    }

    fn analyze_cross_suite_correlations(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<CorrelationMatrix> {
        // Stub implementation
        Ok(CorrelationMatrix::new())
    }

    fn perform_significance_tests(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<Vec<SignificanceTest>> {
        // Stub implementation
        Ok(vec![])
    }

    fn calculate_confidence_intervals(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<HashMap<String, ConfidenceInterval>> {
        // Stub implementation
        Ok(HashMap::new())
    }

    fn identify_bottlenecks(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<Vec<Bottleneck>> {
        // Stub implementation
        Ok(vec![])
    }

    fn generate_executive_summary(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<ExecutiveSummary> {
        // Stub implementation
        Ok(ExecutiveSummary::default())
    }

    fn generate_suite_report(
        suite: BenchmarkSuite,
        _suite_result: &BenchmarkSuiteResult,
    ) -> QuantRS2Result<SuiteReport> {
        // Stub implementation
        Ok(SuiteReport {
            suite_name: format!("{suite:?}"),
            performance_summary: "Performance within expected range".to_string(),
            detailed_metrics: HashMap::new(),
            insights: vec![],
        })
    }

    fn summarize_statistics(_stats: &StatisticalAnalysis) -> QuantRS2Result<StatisticalSummary> {
        // Stub implementation
        Ok(StatisticalSummary {
            key_statistics: HashMap::new(),
            significant_findings: vec![],
            confidence_statements: vec![],
        })
    }

    fn summarize_predictions(
        _predictions: &PerformancePredictions,
    ) -> QuantRS2Result<PredictionSummary> {
        // Stub implementation
        Ok(PredictionSummary {
            performance_outlook: "Stable performance expected".to_string(),
            risk_factors: vec![],
            maintenance_timeline: "No immediate maintenance required".to_string(),
        })
    }

    fn summarize_comparison(
        _comparative: &ComparativeAnalysis,
    ) -> QuantRS2Result<ComparativeSummary> {
        // Stub implementation
        Ok(ComparativeSummary {
            position_statement: "Competitive performance".to_string(),
            advantages: vec![],
            improvement_areas: vec![],
        })
    }

    fn generate_visualizations(
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<BenchmarkVisualizations> {
        VisualAnalyzer::generate_visualizations(result)
    }

    fn calculate_quantum_volume(result: &BenchmarkSuiteResult) -> QuantRS2Result<usize> {
        let mut max_qv = 1;

        for (n, measurements) in &result.measurements {
            let success_rates: Vec<f64> = measurements.iter().map(|m| m.success_rate).collect();

            let avg_success = success_rates.iter().sum::<f64>() / success_rates.len() as f64;

            // QV criterion: average success rate > 2/3
            if avg_success > 2.0 / 3.0 {
                max_qv = max_qv.max(1 << n); // 2^n
            }
        }

        Ok(max_qv)
    }

    fn calculate_std_dev(values: &[f64]) -> f64 {
        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance = values.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;
        variance.sqrt()
    }
}

/// ML performance predictor
struct MLPerformancePredictor {
    model: Arc<Mutex<PerformanceModel>>,
    feature_extractor: Arc<BenchmarkFeatureExtractor>,
}

impl Default for MLPerformancePredictor {
    fn default() -> Self {
        Self {
            model: Arc::new(Mutex::new(PerformanceModel::new())),
            feature_extractor: Arc::new(BenchmarkFeatureExtractor::new()),
        }
    }
}

impl MLPerformancePredictor {
    fn new() -> Self {
        Self::default()
    }

    fn predict_performance(
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<PerformancePredictions> {
        let features = BenchmarkFeatureExtractor::extract_features(result)?;
        let predictions = PerformanceModel::predict(&features)?;

        Ok(PerformancePredictions {
            future_performance: predictions.performance_trajectory,
            degradation_timeline: predictions.degradation_timeline,
            maintenance_recommendations: predictions.maintenance_schedule,
            confidence_scores: predictions.confidence,
        })
    }
}

/// Comparative analyzer
struct ComparativeAnalyzer {
    baseline_db: Arc<Mutex<BaselineDatabase>>,
}

impl Default for ComparativeAnalyzer {
    fn default() -> Self {
        Self {
            baseline_db: Arc::new(Mutex::new(BaselineDatabase::new())),
        }
    }
}

impl ComparativeAnalyzer {
    fn new() -> Self {
        Self::default()
    }

    fn analyze(
        &self,
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<ComparativeAnalysis> {
        let baselines = self
            .baseline_db
            .lock()
            .map_err(|e| {
                QuantRS2Error::RuntimeError(format!("Failed to acquire baseline DB lock: {e}"))
            })?
            .get_baselines()?;

        let mut analysis = ComparativeAnalysis::new();

        // Compare with historical performance
        if let Some(historical) = baselines.get(&result.device_info.name) {
            analysis.historical_comparison =
                Some(Self::compare_with_historical(result, historical)?);
        }

        // Compare with similar devices
        let similar_devices = Self::find_similar_devices(&result.device_info, &baselines)?;
        for (device_name, baseline) in similar_devices {
            let comparison = Self::compare_devices(result, baseline)?;
            analysis.device_comparisons.insert(device_name, comparison);
        }

        // Industry benchmarks
        analysis.industry_position = Self::calculate_industry_position(result, &baselines)?;

        Ok(analysis)
    }

    fn compare_with_historical(
        _result: &ComprehensiveBenchmarkResult,
        _historical: &DeviceBaseline,
    ) -> QuantRS2Result<HistoricalComparison> {
        // Stub implementation
        Ok(HistoricalComparison {
            performance_trend: PerformanceTrend::Stable,
            improvement_rate: 0.0,
            anomalies: vec![],
        })
    }

    fn find_similar_devices<'a>(
        _device_info: &DeviceInfo,
        _baselines: &'a HashMap<String, DeviceBaseline>,
    ) -> QuantRS2Result<Vec<(String, &'a DeviceBaseline)>> {
        // Stub implementation
        Ok(vec![])
    }

    fn compare_devices(
        _result: &ComprehensiveBenchmarkResult,
        _baseline: &DeviceBaseline,
    ) -> QuantRS2Result<DeviceComparison> {
        // Stub implementation
        Ok(DeviceComparison {
            relative_performance: HashMap::new(),
            strengths: vec![],
            weaknesses: vec![],
            overall_ranking: 1,
        })
    }

    fn calculate_industry_position(
        _result: &ComprehensiveBenchmarkResult,
        _baselines: &HashMap<String, DeviceBaseline>,
    ) -> QuantRS2Result<IndustryPosition> {
        // Stub implementation
        Ok(IndustryPosition::default())
    }
}

/// Real-time monitor
struct RealtimeMonitor {
    dashboard: Arc<Mutex<BenchmarkDashboard>>,
    alert_manager: Arc<AlertManager>,
}

impl Default for RealtimeMonitor {
    fn default() -> Self {
        Self {
            dashboard: Arc::new(Mutex::new(BenchmarkDashboard::new())),
            alert_manager: Arc::new(AlertManager::new()),
        }
    }
}

impl RealtimeMonitor {
    fn new() -> Self {
        Self::default()
    }

    fn update(&self, result: &BenchmarkSuiteResult) -> QuantRS2Result<()> {
        let _dashboard = self.dashboard.lock().map_err(|e| {
            QuantRS2Error::RuntimeError(format!("Failed to acquire dashboard lock: {e}"))
        })?;
        BenchmarkDashboard::update(result)?;

        // Check for anomalies
        if let Some(anomaly) = Self::detect_anomaly(result)? {
            AlertManager::trigger_alert(anomaly)?;
        }

        Ok(())
    }

    fn detect_anomaly(_result: &BenchmarkSuiteResult) -> QuantRS2Result<Option<BenchmarkAnomaly>> {
        // Simple anomaly detection based on historical data
        // In practice, this would use more sophisticated methods
        Ok(None)
    }
}

/// Adaptive benchmark controller
struct AdaptiveBenchmarkController {
    adaptation_engine: Arc<AdaptationEngine>,
}

impl Default for AdaptiveBenchmarkController {
    fn default() -> Self {
        Self {
            adaptation_engine: Arc::new(AdaptationEngine::new()),
        }
    }
}

impl AdaptiveBenchmarkController {
    fn new() -> Self {
        Self::default()
    }

    fn select_qv_circuits(
        num_qubits: usize,
        device: &impl QuantumDevice,
    ) -> QuantRS2Result<Vec<QuantumCircuit>> {
        // Adaptive selection based on device characteristics
        let device_profile = Self::profile_device(device)?;
        let optimal_circuits = AdaptationEngine::optimize_circuits(num_qubits, &device_profile)?;

        Ok(optimal_circuits)
    }

    fn profile_device(device: &impl QuantumDevice) -> QuantRS2Result<DeviceProfile> {
        Ok(DeviceProfile {
            error_rates: device.get_calibration_data().gate_errors.clone(),
            connectivity_strength: Self::analyze_connectivity(device.get_topology())?,
            coherence_profile: device.get_calibration_data().coherence_times.clone(),
        })
    }

    fn analyze_connectivity(_topology: &DeviceTopology) -> QuantRS2Result<f64> {
        // Stub implementation - return connectivity score
        Ok(0.8)
    }
}

/// Visual analyzer
#[derive(Default)]
struct VisualAnalyzer {}

impl VisualAnalyzer {
    fn new() -> Self {
        Self::default()
    }

    fn generate_visualizations(
        result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<BenchmarkVisualizations> {
        Ok(BenchmarkVisualizations {
            performance_heatmap: Self::create_performance_heatmap(result)?,
            trend_plots: Self::create_trend_plots(result)?,
            comparison_charts: Self::create_comparison_charts(result)?,
            radar_chart: Self::create_radar_chart(result)?,
        })
    }

    fn create_performance_heatmap(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<HeatmapVisualization> {
        // Stub implementation
        Ok(HeatmapVisualization {
            data: Array2::zeros((5, 5)),
            row_labels: vec![
                "Q0".to_string(),
                "Q1".to_string(),
                "Q2".to_string(),
                "Q3".to_string(),
                "Q4".to_string(),
            ],
            col_labels: vec![
                "Q0".to_string(),
                "Q1".to_string(),
                "Q2".to_string(),
                "Q3".to_string(),
                "Q4".to_string(),
            ],
            color_scheme: "viridis".to_string(),
        })
    }

    fn create_trend_plots(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<Vec<TrendPlot>> {
        // Stub implementation
        Ok(vec![])
    }

    fn create_comparison_charts(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<Vec<ComparisonChart>> {
        // Stub implementation
        Ok(vec![])
    }

    fn create_radar_chart(_result: &ComprehensiveBenchmarkResult) -> QuantRS2Result<RadarChart> {
        // Stub implementation
        Ok(RadarChart {
            axes: vec![
                "Fidelity".to_string(),
                "Speed".to_string(),
                "Connectivity".to_string(),
            ],
            data_sets: vec![],
        })
    }
}

/// Result types

/// Comprehensive benchmark result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComprehensiveBenchmarkResult {
    /// Device information
    pub device_info: DeviceInfo,

    /// Results for each benchmark suite
    pub suite_results: HashMap<BenchmarkSuite, BenchmarkSuiteResult>,

    /// Statistical analysis
    pub statistical_analysis: Option<StatisticalAnalysis>,

    /// Performance predictions
    pub performance_predictions: Option<PerformancePredictions>,

    /// Comparative analysis
    pub comparative_analysis: Option<ComparativeAnalysis>,

    /// Recommendations
    pub recommendations: Vec<BenchmarkRecommendation>,

    /// Comprehensive report
    pub report: Option<BenchmarkReport>,
}

impl ComprehensiveBenchmarkResult {
    fn new() -> Self {
        Self {
            device_info: DeviceInfo::default(),
            suite_results: HashMap::new(),
            statistical_analysis: None,
            performance_predictions: None,
            comparative_analysis: None,
            recommendations: Vec::new(),
            report: None,
        }
    }
}

/// Device information
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct DeviceInfo {
    /// Device name
    pub name: String,

    /// Number of qubits
    pub num_qubits: usize,

    /// Connectivity graph
    pub connectivity: Vec<(usize, usize)>,

    /// Native gate set
    pub gate_set: Vec<String>,

    /// Calibration timestamp
    pub calibration_timestamp: f64,

    /// Backend version
    pub backend_version: String,
}

/// Benchmark suite result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkSuiteResult {
    /// Suite type
    pub suite_type: BenchmarkSuite,

    /// Measurements by qubit count
    pub measurements: HashMap<usize, Vec<ExecutionResult>>,

    /// Single-qubit results
    pub single_qubit_results: HashMap<usize, RBResult>,

    /// Two-qubit results
    pub two_qubit_results: HashMap<(usize, usize), RBResult>,

    /// Depth-dependent results
    pub depth_results: HashMap<usize, DepthResult>,

    /// Pattern results
    pub pattern_results: HashMap<LayerPattern, LayerFidelity>,

    /// Gate fidelities
    pub gate_fidelities: HashMap<String, f64>,

    /// Application results
    pub application_results: HashMap<ApplicationBenchmark, ApplicationPerformance>,

    /// Summary metrics
    pub summary_metrics: HashMap<String, f64>,
}

impl BenchmarkSuiteResult {
    fn new(suite_type: BenchmarkSuite) -> Self {
        Self {
            suite_type,
            measurements: HashMap::new(),
            single_qubit_results: HashMap::new(),
            two_qubit_results: HashMap::new(),
            depth_results: HashMap::new(),
            pattern_results: HashMap::new(),
            gate_fidelities: HashMap::new(),
            application_results: HashMap::new(),
            summary_metrics: HashMap::new(),
        }
    }

    fn add_measurement(&mut self, num_qubits: usize, result: ExecutionResult) {
        self.measurements
            .entry(num_qubits)
            .or_default()
            .push(result);
    }
}

/// Execution result
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ExecutionResult {
    success_rate: f64,
    execution_time: Duration,
    counts: HashMap<Vec<bool>, usize>,
}

/// RB result
#[derive(Debug, Clone, Serialize, Deserialize)]
struct RBResult {
    error_rate: f64,
    confidence_interval: (f64, f64),
    fit_quality: f64,
}

/// Depth result
#[derive(Debug, Clone, Serialize, Deserialize)]
struct DepthResult {
    avg_fidelity: f64,
    std_dev: f64,
    samples: usize,
}

/// Layer pattern
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
enum LayerPattern {
    SingleQubitLayers,
    TwoQubitLayers,
    AlternatingLayers,
    RandomLayers,
}

/// Layer fidelity
#[derive(Debug, Clone, Serialize, Deserialize)]
struct LayerFidelity {
    fidelity: f64,
    error_bars: f64,
}

/// Application benchmark types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
enum ApplicationBenchmark {
    VQE,
    QAOA,
    Grover,
    QFT,
}

/// Application performance
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ApplicationPerformance {
    accuracy: f64,
    runtime: Duration,
    resource_usage: ResourceUsage,
}

/// Resource usage
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ResourceUsage {
    circuit_depth: usize,
    gate_count: usize,
    shots_used: usize,
}

/// Statistical analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalAnalysis {
    /// Statistics for each suite
    pub suite_statistics: HashMap<BenchmarkSuite, SuiteStatistics>,

    /// Cross-suite correlations
    pub cross_suite_correlations: CorrelationMatrix,

    /// Significance tests
    pub significance_tests: Vec<SignificanceTest>,

    /// Confidence intervals
    pub confidence_intervals: HashMap<String, ConfidenceInterval>,
}

impl StatisticalAnalysis {
    fn new() -> Self {
        Self::default()
    }

    /// Fit exponential decay to data: f(x) = A * p^x + B
    /// Returns (A, p, B)
    pub fn fit_exponential_decay(&self, x: &[f64], y: &[f64]) -> QuantRS2Result<(f64, f64, f64)> {
        if x.len() != y.len() || x.is_empty() {
            return Err(QuantRS2Error::RuntimeError(
                "Invalid data for exponential decay fit".to_string(),
            ));
        }

        // Simple linear regression on log-transformed data
        // log(y - B) = log(A) + x * log(p)
        // For simplicity, assume B = min(y) / 2
        let b = y.iter().copied().fold(f64::INFINITY, f64::min) / 2.0;

        let mut sum_x = 0.0;
        let mut sum_log_y = 0.0;
        let mut sum_x_log_y = 0.0;
        let mut sum_x2 = 0.0;
        let mut n = 0;

        for i in 0..x.len() {
            let y_shifted = y[i] - b;
            if y_shifted > 0.0 {
                let log_y = y_shifted.ln();
                sum_x += x[i];
                sum_log_y += log_y;
                sum_x_log_y += x[i] * log_y;
                sum_x2 += x[i] * x[i];
                n += 1;
            }
        }

        if n < 2 {
            return Err(QuantRS2Error::RuntimeError(
                "Insufficient valid data points for fit".to_string(),
            ));
        }

        let n_f64 = n as f64;
        let log_p = (n_f64 * sum_x_log_y - sum_x * sum_log_y) / (n_f64 * sum_x2 - n_f64 * sum_x);
        let log_a = (sum_log_y - log_p * sum_x) / n_f64;

        let p = log_p.exp();
        let a = log_a.exp();

        Ok((a, p, b))
    }
}

impl Default for StatisticalAnalysis {
    fn default() -> Self {
        Self {
            suite_statistics: HashMap::new(),
            cross_suite_correlations: CorrelationMatrix::new(),
            significance_tests: Vec::new(),
            confidence_intervals: HashMap::new(),
        }
    }
}

/// Suite statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SuiteStatistics {
    /// Mean performance
    pub mean: f64,

    /// Standard deviation
    pub std_dev: f64,

    /// Median
    pub median: f64,

    /// Quartiles
    pub quartiles: (f64, f64, f64),

    /// Outliers
    pub outliers: Vec<f64>,
}

/// Correlation matrix
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationMatrix {
    /// Matrix data
    pub data: Array2<f64>,

    /// Row/column labels
    pub labels: Vec<String>,
}

impl CorrelationMatrix {
    fn new() -> Self {
        Self {
            data: Array2::zeros((0, 0)),
            labels: Vec::new(),
        }
    }
}

/// Significance test
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SignificanceTest {
    /// Test name
    pub test_name: String,

    /// P-value
    pub p_value: f64,

    /// Test statistic
    pub statistic: f64,

    /// Degrees of freedom
    pub degrees_of_freedom: Option<f64>,

    /// Conclusion
    pub conclusion: String,
}

/// Confidence interval
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConfidenceInterval {
    /// Lower bound
    pub lower: f64,

    /// Upper bound
    pub upper: f64,

    /// Confidence level
    pub confidence_level: f64,
}

/// Performance predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformancePredictions {
    /// Future performance trajectory
    pub future_performance: Vec<PredictedPerformance>,

    /// Degradation timeline
    pub degradation_timeline: DegradationTimeline,

    /// Maintenance recommendations
    pub maintenance_recommendations: Vec<MaintenanceRecommendation>,

    /// Confidence scores
    pub confidence_scores: HashMap<String, f64>,
}

/// Predicted performance
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictedPerformance {
    /// Time offset (days)
    pub time_offset: f64,

    /// Predicted metrics
    pub metrics: HashMap<PerformanceMetric, f64>,

    /// Uncertainty bounds
    pub uncertainty: f64,
}

/// Degradation timeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DegradationTimeline {
    /// Critical thresholds
    pub thresholds: Vec<DegradationThreshold>,

    /// Expected timeline
    pub timeline: Vec<DegradationEvent>,
}

/// Degradation threshold
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DegradationThreshold {
    /// Metric
    pub metric: PerformanceMetric,

    /// Threshold value
    pub threshold: f64,

    /// Expected crossing time
    pub expected_time: f64,
}

/// Degradation event
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DegradationEvent {
    /// Event type
    pub event_type: DegradationType,

    /// Expected time
    pub expected_time: f64,

    /// Impact
    pub impact: ImpactLevel,
}

/// Degradation type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum DegradationType {
    GateFidelityDrop,
    CoherenceTimeDegradation,
    CrosstalkIncrease,
    CalibrationDrift,
}

/// Impact level
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ImpactLevel {
    Low,
    Medium,
    High,
    Critical,
}

/// Maintenance recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MaintenanceRecommendation {
    /// Maintenance type
    pub maintenance_type: MaintenanceType,

    /// Recommended time
    pub recommended_time: f64,

    /// Expected benefit
    pub expected_benefit: f64,

    /// Cost estimate
    pub cost_estimate: f64,
}

/// Maintenance type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MaintenanceType {
    Recalibration,
    HardwareReplacement,
    SoftwareUpdate,
    FullMaintenance,
}

/// Comparative analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparativeAnalysis {
    /// Historical comparison
    pub historical_comparison: Option<HistoricalComparison>,

    /// Device comparisons
    pub device_comparisons: HashMap<String, DeviceComparison>,

    /// Industry position
    pub industry_position: IndustryPosition,
}

impl ComparativeAnalysis {
    fn new() -> Self {
        Self {
            historical_comparison: None,
            device_comparisons: HashMap::new(),
            industry_position: IndustryPosition::default(),
        }
    }
}

/// Historical comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalComparison {
    /// Performance trend
    pub performance_trend: PerformanceTrend,

    /// Improvement rate
    pub improvement_rate: f64,

    /// Anomalies detected
    pub anomalies: Vec<HistoricalAnomaly>,
}

/// Performance trend
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PerformanceTrend {
    Improving,
    Stable,
    Degrading,
    Fluctuating,
}

/// Historical anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalAnomaly {
    /// Timestamp
    pub timestamp: f64,

    /// Anomaly type
    pub anomaly_type: AnomalyType,

    /// Severity
    pub severity: Severity,
}

/// Anomaly type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AnomalyType {
    SuddenDrop,
    GradualDegradation,
    UnexpectedImprovement,
    HighVariability,
}

/// Severity
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum Severity {
    Low,
    Medium,
    High,
    Critical,
}

/// Device comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceComparison {
    /// Relative performance
    pub relative_performance: HashMap<PerformanceMetric, f64>,

    /// Strengths
    pub strengths: Vec<String>,

    /// Weaknesses
    pub weaknesses: Vec<String>,

    /// Overall ranking
    pub overall_ranking: usize,
}

/// Industry position
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct IndustryPosition {
    /// Percentile rankings
    pub percentile_rankings: HashMap<PerformanceMetric, f64>,

    /// Tier classification
    pub tier: IndustryTier,

    /// Competitive advantages
    pub advantages: Vec<String>,
}

/// Industry tier
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default, Serialize, Deserialize)]
pub enum IndustryTier {
    #[default]
    Emerging,
    Competitive,
    Leading,
    BestInClass,
}

/// Benchmark recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkRecommendation {
    /// Category
    pub category: RecommendationCategory,

    /// Priority
    pub priority: Priority,

    /// Description
    pub description: String,

    /// Expected improvement
    pub expected_improvement: f64,

    /// Effort level
    pub effort: EffortLevel,
}

/// Recommendation category
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum RecommendationCategory {
    Calibration,
    Scheduling,
    Optimization,
    Hardware,
    Software,
}

/// Priority
#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum Priority {
    Low,
    Medium,
    High,
    Critical,
}

/// Effort level
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum EffortLevel {
    Low,
    Medium,
    High,
}

/// Benchmark report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkReport {
    /// Executive summary
    pub executive_summary: ExecutiveSummary,

    /// Suite reports
    pub suite_reports: HashMap<BenchmarkSuite, SuiteReport>,

    /// Statistical summary
    pub statistical_summary: Option<StatisticalSummary>,

    /// Prediction summary
    pub prediction_summary: Option<PredictionSummary>,

    /// Comparative summary
    pub comparative_summary: Option<ComparativeSummary>,

    /// Visualizations
    pub visualizations: Option<BenchmarkVisualizations>,

    /// Recommendations
    pub recommendations: Vec<BenchmarkRecommendation>,
}

impl BenchmarkReport {
    fn new() -> Self {
        Self {
            executive_summary: ExecutiveSummary::default(),
            suite_reports: HashMap::new(),
            statistical_summary: None,
            prediction_summary: None,
            comparative_summary: None,
            visualizations: None,
            recommendations: Vec::new(),
        }
    }
}

/// Executive summary
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct ExecutiveSummary {
    /// Overall performance score
    pub overall_score: f64,

    /// Key findings
    pub key_findings: Vec<String>,

    /// Critical issues
    pub critical_issues: Vec<String>,

    /// Top recommendations
    pub top_recommendations: Vec<String>,
}

/// Suite report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SuiteReport {
    /// Suite name
    pub suite_name: String,

    /// Performance summary
    pub performance_summary: String,

    /// Detailed metrics
    pub detailed_metrics: HashMap<String, MetricReport>,

    /// Insights
    pub insights: Vec<String>,
}

/// Metric report
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MetricReport {
    /// Value
    pub value: f64,

    /// Trend
    pub trend: MetricTrend,

    /// Comparison to baseline
    pub baseline_comparison: f64,

    /// Analysis
    pub analysis: String,
}

/// Metric trend
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MetricTrend {
    Improving,
    Stable,
    Degrading,
}

/// Statistical summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalSummary {
    /// Key statistics
    pub key_statistics: HashMap<String, f64>,

    /// Significant findings
    pub significant_findings: Vec<String>,

    /// Confidence statements
    pub confidence_statements: Vec<String>,
}

/// Prediction summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionSummary {
    /// Performance outlook
    pub performance_outlook: String,

    /// Risk factors
    pub risk_factors: Vec<String>,

    /// Maintenance timeline
    pub maintenance_timeline: String,
}

/// Comparative summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparativeSummary {
    /// Position statement
    pub position_statement: String,

    /// Competitive advantages
    pub advantages: Vec<String>,

    /// Areas for improvement
    pub improvement_areas: Vec<String>,
}

/// Benchmark visualizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkVisualizations {
    /// Performance heatmap
    pub performance_heatmap: HeatmapVisualization,

    /// Trend plots
    pub trend_plots: Vec<TrendPlot>,

    /// Comparison charts
    pub comparison_charts: Vec<ComparisonChart>,

    /// Radar chart
    pub radar_chart: RadarChart,
}

/// Heatmap visualization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HeatmapVisualization {
    /// Data matrix
    pub data: Array2<f64>,

    /// Row labels
    pub row_labels: Vec<String>,

    /// Column labels
    pub col_labels: Vec<String>,

    /// Color scheme
    pub color_scheme: String,
}

/// Trend plot
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendPlot {
    /// Title
    pub title: String,

    /// X-axis data
    pub x_data: Vec<f64>,

    /// Y-axis data series
    pub y_series: Vec<DataSeries>,

    /// Plot type
    pub plot_type: PlotType,
}

/// Data series
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataSeries {
    /// Series name
    pub name: String,

    /// Data points
    pub data: Vec<f64>,

    /// Error bars
    pub error_bars: Option<Vec<f64>>,
}

/// Plot type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PlotType {
    Line,
    Scatter,
    Bar,
    Area,
}

/// Comparison chart
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparisonChart {
    /// Chart title
    pub title: String,

    /// Categories
    pub categories: Vec<String>,

    /// Data sets
    pub data_sets: Vec<ComparisonDataSet>,

    /// Chart type
    pub chart_type: ChartType,
}

/// Comparison data set
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparisonDataSet {
    /// Name
    pub name: String,

    /// Values
    pub values: Vec<f64>,
}

/// Chart type
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ChartType {
    Bar,
    GroupedBar,
    StackedBar,
    Line,
}

/// Radar chart
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RadarChart {
    /// Axes
    pub axes: Vec<String>,

    /// Data sets
    pub data_sets: Vec<RadarDataSet>,
}

/// Radar data set
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RadarDataSet {
    /// Name
    pub name: String,

    /// Values (0-1 normalized)
    pub values: Vec<f64>,
}

/// Helper types

/// Mirror circuit
struct MirrorCircuit {
    forward: QuantumCircuit,
    mirror: QuantumCircuit,
}

/// Performance model
struct PerformanceModel {
    // ML model implementation
}

impl PerformanceModel {
    const fn new() -> Self {
        Self {}
    }

    fn predict(_features: &BenchmarkFeatures) -> QuantRS2Result<ModelPredictions> {
        // Placeholder implementation
        Ok(ModelPredictions {
            performance_trajectory: vec![],
            degradation_timeline: DegradationTimeline {
                thresholds: vec![],
                timeline: vec![],
            },
            maintenance_schedule: vec![],
            confidence: HashMap::new(),
        })
    }
}

/// Benchmark feature extractor
struct BenchmarkFeatureExtractor {
    // Feature extraction logic
}

impl BenchmarkFeatureExtractor {
    const fn new() -> Self {
        Self {}
    }

    fn extract_features(
        _result: &ComprehensiveBenchmarkResult,
    ) -> QuantRS2Result<BenchmarkFeatures> {
        // Extract relevant features for ML analysis
        Ok(BenchmarkFeatures {
            performance_features: vec![],
            topology_features: vec![],
            temporal_features: vec![],
            statistical_features: vec![],
        })
    }
}

/// Benchmark features
struct BenchmarkFeatures {
    performance_features: Vec<f64>,
    topology_features: Vec<f64>,
    temporal_features: Vec<f64>,
    statistical_features: Vec<f64>,
}

/// Model predictions
struct ModelPredictions {
    performance_trajectory: Vec<PredictedPerformance>,
    degradation_timeline: DegradationTimeline,
    maintenance_schedule: Vec<MaintenanceRecommendation>,
    confidence: HashMap<String, f64>,
}

/// Baseline database
struct BaselineDatabase {
    baselines: HashMap<String, DeviceBaseline>,
}

impl BaselineDatabase {
    fn new() -> Self {
        Self {
            baselines: HashMap::new(),
        }
    }

    fn get_baselines(&self) -> QuantRS2Result<HashMap<String, DeviceBaseline>> {
        Ok(self.baselines.clone())
    }
}

/// Device baseline
#[derive(Debug, Clone)]
struct DeviceBaseline {
    device_name: String,
    performance_history: Vec<HistoricalPerformance>,
    best_performance: HashMap<PerformanceMetric, f64>,
}

/// Historical performance
#[derive(Debug, Clone)]
struct HistoricalPerformance {
    timestamp: f64,
    metrics: HashMap<PerformanceMetric, f64>,
}

/// Benchmark dashboard
struct BenchmarkDashboard {
    current_metrics: HashMap<String, f64>,
    history: VecDeque<DashboardSnapshot>,
}

impl BenchmarkDashboard {
    fn new() -> Self {
        Self {
            current_metrics: HashMap::new(),
            history: VecDeque::new(),
        }
    }

    fn update(_result: &BenchmarkSuiteResult) -> QuantRS2Result<()> {
        // Update dashboard with latest results
        Ok(())
    }
}

/// Dashboard snapshot
struct DashboardSnapshot {
    timestamp: f64,
    metrics: HashMap<String, f64>,
}

/// Alert manager
struct AlertManager {
    // Alert management logic
}

impl AlertManager {
    const fn new() -> Self {
        Self {}
    }

    fn trigger_alert(_anomaly: BenchmarkAnomaly) -> QuantRS2Result<()> {
        // Handle alert
        Ok(())
    }
}

/// Benchmark anomaly
struct BenchmarkAnomaly {
    anomaly_type: AnomalyType,
    severity: Severity,
    description: String,
}

/// Adaptation engine
struct AdaptationEngine {
    // Adaptive optimization logic
}

impl AdaptationEngine {
    const fn new() -> Self {
        Self {}
    }

    fn optimize_circuits(
        _num_qubits: usize,
        _profile: &DeviceProfile,
    ) -> QuantRS2Result<Vec<QuantumCircuit>> {
        // Generate optimized circuits based on device profile
        Ok(vec![])
    }
}

/// Device profile
struct DeviceProfile {
    error_rates: HashMap<String, f64>,
    connectivity_strength: f64,
    coherence_profile: Vec<(f64, f64)>,
}

/// Bottleneck types
enum Bottleneck {
    LowGateFidelity(String),
    HighCrosstalk(Vec<QubitId>),
    LongExecutionTime,
    LimitedConnectivity,
    ShortCoherence,
}

/// Benchmark cache
#[derive(Default)]
struct BenchmarkCache {
    results: HashMap<String, ComprehensiveBenchmarkResult>,
}

impl BenchmarkCache {
    fn new() -> Self {
        Self::default()
    }
}

/// Quantum device trait
trait QuantumDevice: Sync {
    fn execute(&self, circuit: QuantumCircuit, shots: usize) -> QuantRS2Result<QuantumJob>;
    fn get_topology(&self) -> &DeviceTopology;
    fn get_calibration_data(&self) -> &CalibrationData;
    fn get_name(&self) -> String;
    fn get_native_gates(&self) -> Vec<String>;
    fn get_backend_version(&self) -> String;
}

/// Quantum job
struct QuantumJob {
    job_id: String,
    status: JobStatus,
    results: Option<JobResults>,
}

impl QuantumJob {
    fn get_counts(&self) -> QuantRS2Result<HashMap<Vec<bool>, usize>> {
        // Get measurement counts
        unimplemented!()
    }
}

/// Job status
enum JobStatus {
    Queued,
    Running,
    Completed,
    Failed(String),
}

/// Job results
struct JobResults {
    counts: HashMap<Vec<bool>, usize>,
    metadata: HashMap<String, String>,
}

/// Device topology
struct DeviceTopology {
    num_qubits: usize,
    connectivity: Vec<(usize, usize)>,
}

/// Calibration data
struct CalibrationData {
    gate_errors: HashMap<String, f64>,
    readout_errors: Vec<f64>,
    coherence_times: Vec<(f64, f64)>,
    timestamp: f64,
}

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

    #[test]
    fn test_benchmark_creation() {
        let config = EnhancedBenchmarkConfig::default();
        let benchmark = EnhancedHardwareBenchmark::new(config);

        // Basic test to ensure creation works
        assert!(benchmark.config.enable_ml_prediction);
    }

    #[test]
    fn test_benchmark_suite_result() {
        let mut result = BenchmarkSuiteResult::new(BenchmarkSuite::QuantumVolume);

        result.add_measurement(
            4,
            ExecutionResult {
                success_rate: 0.85,
                execution_time: Duration::from_millis(100),
                counts: HashMap::new(),
            },
        );

        assert_eq!(result.measurements.len(), 1);
        assert_eq!(result.measurements[&4].len(), 1);
    }
}