quantrs2_device/

cross_platform_benchmarking.rs

//! Cross-Platform Performance Benchmarking System with SciRS2 Analysis
//!
//! This module provides comprehensive benchmarking capabilities across different quantum
//! cloud providers (IBM Quantum, AWS Braket, Azure Quantum, etc.) with advanced
//! statistical analysis using SciRS2 for performance comparison and optimization.

use std::collections::{BTreeMap, HashMap, HashSet};
use std::time::{Duration, Instant, SystemTime};

use scirs2_core::random::{thread_rng, Rng};
use serde::{Deserialize, Serialize};
use tokio::time::timeout;

use quantrs2_circuit::prelude::*;
use quantrs2_core::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    qubit::QubitId,
};

// SciRS2 imports for advanced analysis
#[cfg(feature = "scirs2")]
use scirs2_linalg::{correlationmatrix, eig, matrix_norm, svd, LinalgResult};
#[cfg(feature = "scirs2")]
use scirs2_optimize::{minimize, OptimizeResult};
#[cfg(feature = "scirs2")]
use scirs2_stats::{
    distributions::{chi2, norm, t},
    ks_2samp, mean, median, pearsonr, spearmanr, std, ttest_ind, var, Alternative, TTestResult,
};

#[cfg(not(feature = "scirs2"))]
use crate::ml_optimization::fallback_scirs2::{mean, minimize, pearsonr, std, var, OptimizeResult};

use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};

use crate::{
    aws::AWSBraketClient,
    azure::AzureQuantumClient,
    backend_traits::{query_backend_capabilities, BackendCapabilities},
    benchmarking::{BenchmarkConfig, DeviceExecutor, HardwareBenchmarkSuite},
    calibration::{CalibrationManager, DeviceCalibration},
    ibm::IBMQuantumClient,
    CircuitResult, DeviceError, DeviceResult,
};

/// Cross-platform benchmarking configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossPlatformBenchmarkConfig {
    /// Target platforms to benchmark
    pub target_platforms: Vec<QuantumPlatform>,
    /// Circuit complexity levels to test
    pub complexity_levels: Vec<ComplexityLevel>,
    /// Statistical analysis configuration
    pub statistical_config: StatisticalAnalysisConfig,
    /// Parallel execution configuration
    pub parallel_config: ParallelBenchmarkConfig,
    /// Timeout for individual benchmarks
    pub benchmark_timeout: Duration,
    /// Number of repetitions per test
    pub repetitions: usize,
    /// Enable cost analysis
    pub enable_cost_analysis: bool,
    /// Enable latency analysis
    pub enable_latency_analysis: bool,
    /// Enable reliability analysis
    pub enable_reliability_analysis: bool,
    /// Custom benchmark circuits
    pub custom_circuits: Vec<CustomBenchmarkCircuit>,
}

/// Quantum computing platforms
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum QuantumPlatform {
    IBMQuantum(String),      // Device name
    AWSBraket(String),       // Device ARN
    AzureQuantum(String),    // Target ID
    IonQ(String),            // Device name
    Rigetti(String),         // Device name
    GoogleQuantumAI(String), // Device name
    Custom(String),          // Custom platform identifier
}

/// Circuit complexity levels for benchmarking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComplexityLevel {
    pub name: String,
    pub qubit_count: usize,
    pub circuit_depth: usize,
    pub gate_count_range: (usize, usize),
    pub two_qubit_gate_ratio: f64,
    pub description: String,
}

/// Statistical analysis configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalAnalysisConfig {
    /// Confidence level for statistical tests
    pub confidence_level: f64,
    /// Enable ANOVA analysis
    pub enable_anova: bool,
    /// Enable pairwise comparisons
    pub enable_pairwise_comparisons: bool,
    /// Enable outlier detection
    pub enable_outlier_detection: bool,
    /// Enable distribution fitting
    pub enable_distribution_fitting: bool,
    /// Enable correlation analysis
    pub enable_correlation_analysis: bool,
    /// Minimum sample size for statistical tests
    pub min_sample_size: usize,
}

/// Parallel benchmarking configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelBenchmarkConfig {
    /// Enable parallel execution across platforms
    pub enable_parallel: bool,
    /// Maximum concurrent benchmarks
    pub max_concurrent: usize,
    /// Load balancing strategy
    pub load_balancing: LoadBalancingStrategy,
    /// Resource allocation per platform
    pub resource_allocation: HashMap<QuantumPlatform, ResourceAllocation>,
}

/// Load balancing strategies
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum LoadBalancingStrategy {
    RoundRobin,
    WeightedRoundRobin,
    LeastConnections,
    ResourceBased,
    CostOptimized,
    LatencyOptimized,
}

/// Resource allocation per platform
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceAllocation {
    pub max_shots_per_circuit: usize,
    pub max_concurrent_circuits: usize,
    pub priority: BenchmarkPriority,
    pub timeout: Duration,
}

/// Benchmark priority levels
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum BenchmarkPriority {
    Low,
    Medium,
    High,
    Critical,
}

/// Custom benchmark circuit definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomBenchmarkCircuit {
    pub name: String,
    pub description: String,
    pub qubit_count: usize,
    pub circuit_definition: CircuitDefinition,
    pub expected_outcomes: HashMap<String, f64>,
    pub performance_targets: PerformanceTargets,
}

/// Circuit definition formats
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CircuitDefinition {
    QASM(String),
    QuantumCircuit(String), // Serialized circuit
    PythonCode(String),
    Custom(String),
}

/// Performance targets for benchmarking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTargets {
    pub target_fidelity: f64,
    pub max_execution_time: Duration,
    pub max_cost_per_shot: f64,
    pub min_success_rate: f64,
}

/// Comprehensive cross-platform benchmark results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossPlatformBenchmarkResult {
    pub benchmark_id: String,
    pub timestamp: SystemTime,
    pub config: CrossPlatformBenchmarkConfig,
    pub platform_results: HashMap<QuantumPlatform, PlatformBenchmarkResult>,
    pub comparative_analysis: ComparativeAnalysisResult,
    pub statistical_analysis: CrossPlatformStatisticalAnalysis,
    pub cost_analysis: Option<CrossPlatformCostAnalysis>,
    pub recommendations: Vec<PlatformRecommendation>,
    pub execution_summary: ExecutionSummary,
}

/// Results for a specific platform
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformBenchmarkResult {
    pub platform: QuantumPlatform,
    pub device_info: DeviceInfo,
    pub benchmark_metrics: BenchmarkMetrics,
    pub circuit_results: HashMap<String, Vec<CircuitBenchmarkResult>>,
    pub performance_analysis: PlatformPerformanceAnalysis,
    pub reliability_metrics: ReliabilityMetrics,
    pub latency_analysis: LatencyAnalysis,
    pub error_analysis: ErrorAnalysis,
}

/// Device information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DeviceInfo {
    pub device_name: String,
    pub provider: String,
    pub technology: QuantumTechnology,
    pub qubit_count: usize,
    pub connectivity: ConnectivityInfo,
    pub capabilities: BackendCapabilities,
    pub calibration_date: Option<SystemTime>,
}

/// Quantum technology types
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum QuantumTechnology {
    Superconducting,
    TrappedIon,
    Photonic,
    NeutralAtom,
    Topological,
    SpinQubit,
    Other(String),
}

/// Connectivity information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConnectivityInfo {
    pub topology_type: TopologyType,
    pub connectivity_graph: Array2<f64>,
    pub average_connectivity: f64,
    pub max_path_length: usize,
}

/// Topology types
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum TopologyType {
    Linear,
    Grid,
    Heavy,
    AllToAll,
    Custom,
}

/// Benchmark metrics for a platform
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BenchmarkMetrics {
    pub overall_score: f64,
    pub fidelity_score: f64,
    pub speed_score: f64,
    pub reliability_score: f64,
    pub cost_efficiency_score: f64,
    pub scalability_score: f64,
    pub detailed_metrics: HashMap<String, f64>,
}

/// Individual circuit benchmark result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CircuitBenchmarkResult {
    pub circuit_name: String,
    pub complexity_level: String,
    pub execution_time: Duration,
    pub queue_time: Duration,
    pub total_time: Duration,
    pub fidelity: f64,
    pub success_rate: f64,
    pub cost: f64,
    pub shots: usize,
    pub measurement_counts: HashMap<String, usize>,
    pub error_messages: Vec<String>,
}

/// Platform performance analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformPerformanceAnalysis {
    pub throughput: f64,
    pub latency_distribution: LatencyDistribution,
    pub scalability_analysis: ScalabilityAnalysis,
    pub resource_utilization: ResourceUtilization,
    pub performance_trends: PerformanceTrends,
}

/// Latency distribution analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LatencyDistribution {
    pub mean: f64,
    pub median: f64,
    pub std_dev: f64,
    pub percentiles: HashMap<u8, f64>, // P50, P95, P99, etc.
    pub distribution_type: String,
}

/// Scalability analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalabilityAnalysis {
    pub qubit_scaling_factor: f64,
    pub depth_scaling_factor: f64,
    pub theoretical_limits: TheoreticalLimits,
    pub bottleneck_analysis: BottleneckAnalysis,
}

/// Theoretical performance limits
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TheoreticalLimits {
    pub max_qubit_count: usize,
    pub max_circuit_depth: usize,
    pub max_gate_count: usize,
    pub coherence_limited_depth: usize,
}

/// Performance bottleneck analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BottleneckAnalysis {
    pub primary_bottleneck: BottleneckType,
    pub bottleneck_severity: f64,
    pub mitigation_strategies: Vec<String>,
}

/// Types of performance bottlenecks
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum BottleneckType {
    QueueTime,
    ExecutionTime,
    GateErrors,
    Connectivity,
    Coherence,
    Readout,
    Classical,
    Network,
    Other(String),
}

/// Resource utilization metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceUtilization {
    pub qubit_utilization: f64,
    pub gate_efficiency: f64,
    pub shot_efficiency: f64,
    pub time_efficiency: f64,
}

/// Performance trends over time
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceTrends {
    pub fidelity_trend: TrendAnalysis,
    pub latency_trend: TrendAnalysis,
    pub reliability_trend: TrendAnalysis,
    pub cost_trend: TrendAnalysis,
}

/// Trend analysis data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrendAnalysis {
    pub slope: f64,
    pub r_squared: f64,
    pub trend_direction: TrendDirection,
    pub confidence_interval: (f64, f64),
}

/// Trend direction
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum TrendDirection {
    Improving,
    Stable,
    Degrading,
    Volatile,
}

/// Reliability metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ReliabilityMetrics {
    pub uptime_percentage: f64,
    pub mtbf: Duration, // Mean Time Between Failures
    pub mttr: Duration, // Mean Time To Recovery
    pub error_rates: HashMap<String, f64>,
    pub consistency_score: f64,
}

/// Latency analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LatencyAnalysis {
    pub submission_latency: Duration,
    pub queue_latency: Duration,
    pub execution_latency: Duration,
    pub retrieval_latency: Duration,
    pub total_latency: Duration,
    pub latency_variability: f64,
}

/// Error analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorAnalysis {
    pub systematic_errors: HashMap<String, f64>,
    pub random_errors: HashMap<String, f64>,
    pub coherent_errors: HashMap<String, f64>,
    pub readout_errors: HashMap<String, f64>,
    pub gate_errors: HashMap<String, f64>,
    pub error_correlations: Array2<f64>,
}

/// Comparative analysis across platforms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComparativeAnalysisResult {
    pub platform_rankings: HashMap<String, PlatformRanking>,
    pub performance_matrix: Array2<f64>,
    pub statistical_comparisons: HashMap<String, StatisticalComparison>,
    pub cost_effectiveness_analysis: CostEffectivenessAnalysis,
    pub use_case_recommendations: HashMap<String, Vec<QuantumPlatform>>,
}

/// Platform ranking in different categories
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformRanking {
    pub overall_rank: usize,
    pub category_ranks: HashMap<String, usize>,
    pub normalized_scores: HashMap<String, f64>,
    pub relative_performance: f64,
}

/// Statistical comparison between platforms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticalComparison {
    pub comparison_type: ComparisonType,
    pub platforms_compared: Vec<QuantumPlatform>,
    pub test_statistic: f64,
    pub p_value: f64,
    pub effect_size: f64,
    pub confidence_interval: (f64, f64),
    pub interpretation: String,
}

/// Types of statistical comparisons
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ComparisonType {
    TTest,
    ANOVA,
    KruskalWallis,
    MannWhitney,
    ChiSquare,
    KolmogorovSmirnov,
}

/// Cost effectiveness analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostEffectivenessAnalysis {
    pub cost_per_shot: HashMap<QuantumPlatform, f64>,
    pub cost_per_gate: HashMap<QuantumPlatform, f64>,
    pub cost_per_successful_result: HashMap<QuantumPlatform, f64>,
    pub value_for_money_score: HashMap<QuantumPlatform, f64>,
    pub cost_optimization_recommendations: Vec<CostOptimizationRecommendation>,
}

/// Cross-platform statistical analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossPlatformStatisticalAnalysis {
    pub anova_results: HashMap<String, ANOVAResult>,
    pub correlation_analysis: CorrelationAnalysisResult,
    pub cluster_analysis: ClusterAnalysisResult,
    pub principal_component_analysis: PCAResult,
    pub outlier_detection: OutlierDetectionResult,
    pub distribution_analysis: DistributionAnalysisResult,
}

/// ANOVA analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ANOVAResult {
    pub f_statistic: f64,
    pub p_value: f64,
    pub degrees_of_freedom: (usize, usize),
    pub mean_squares: f64,
    pub effect_size: f64,
    pub post_hoc_tests: HashMap<String, PostHocTest>,
}

/// Post-hoc test results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PostHocTest {
    pub test_type: String,
    pub comparisons: HashMap<String, PairwiseComparison>,
}

/// Pairwise comparison results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PairwiseComparison {
    pub mean_difference: f64,
    pub p_value: f64,
    pub confidence_interval: (f64, f64),
    pub significant: bool,
}

/// Correlation analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationAnalysisResult {
    pub correlationmatrix: Array2<f64>,
    pub significant_correlations: Vec<CorrelationPair>,
    pub correlation_network: HashMap<String, Vec<String>>,
}

/// Significant correlation pair
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CorrelationPair {
    pub metric1: String,
    pub metric2: String,
    pub correlation: f64,
    pub p_value: f64,
    pub correlation_type: CorrelationType,
}

/// Types of correlations
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum CorrelationType {
    Pearson,
    Spearman,
    Kendall,
}

/// Cluster analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClusterAnalysisResult {
    pub cluster_assignments: HashMap<QuantumPlatform, usize>,
    pub cluster_centers: Array2<f64>,
    pub silhouette_score: f64,
    pub cluster_interpretations: HashMap<usize, String>,
}

/// Principal Component Analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PCAResult {
    pub principal_components: Array2<f64>,
    pub explained_variance_ratio: Array1<f64>,
    pub cumulative_variance: Array1<f64>,
    pub loadings: Array2<f64>,
    pub platform_scores: HashMap<QuantumPlatform, Array1<f64>>,
}

/// Outlier detection results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OutlierDetectionResult {
    pub outliers: HashMap<QuantumPlatform, Vec<String>>,
    pub outlier_scores: HashMap<QuantumPlatform, f64>,
    pub detection_method: String,
    pub threshold: f64,
}

/// Distribution analysis results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributionAnalysisResult {
    pub platform_distributions: HashMap<QuantumPlatform, DistributionFit>,
    pub distribution_comparisons: HashMap<String, DistributionComparison>,
    pub normality_tests: HashMap<QuantumPlatform, NormalityTest>,
}

/// Distribution fit results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributionFit {
    pub distribution_type: String,
    pub parameters: Vec<f64>,
    pub goodness_of_fit: f64,
    pub aic: f64,
    pub bic: f64,
}

/// Distribution comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DistributionComparison {
    pub platforms: Vec<QuantumPlatform>,
    pub ks_statistic: f64,
    pub p_value: f64,
    pub distributions_equal: bool,
}

/// Normality test results
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NormalityTest {
    pub test_type: String,
    pub statistic: f64,
    pub p_value: f64,
    pub is_normal: bool,
}

/// Cross-platform cost analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossPlatformCostAnalysis {
    pub total_costs: HashMap<QuantumPlatform, f64>,
    pub cost_breakdown: HashMap<QuantumPlatform, CostBreakdown>,
    pub cost_trends: HashMap<QuantumPlatform, CostTrend>,
    pub cost_optimization: CostOptimizationAnalysis,
    pub roi_analysis: ROIAnalysis,
}

/// Cost breakdown per platform
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostBreakdown {
    pub compute_cost: f64,
    pub queue_cost: f64,
    pub storage_cost: f64,
    pub data_transfer_cost: f64,
    pub other_costs: HashMap<String, f64>,
}

/// Cost trend analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostTrend {
    pub trend_direction: TrendDirection,
    pub cost_per_month: Vec<f64>,
    pub projected_costs: Vec<f64>,
    pub cost_volatility: f64,
}

/// Cost optimization analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostOptimizationAnalysis {
    pub optimization_opportunities: Vec<CostOptimizationRecommendation>,
    pub potential_savings: f64,
    pub optimization_strategies: Vec<String>,
}

/// Cost optimization recommendation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostOptimizationRecommendation {
    pub recommendation_type: CostOptimizationType,
    pub description: String,
    pub estimated_savings: f64,
    pub implementation_effort: ImplementationEffort,
    pub time_to_implement: Duration,
}

/// Types of cost optimizations
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum CostOptimizationType {
    PlatformSelection,
    ShotOptimization,
    TimingOptimization,
    ResourcePooling,
    BulkPricing,
    Other(String),
}

/// Implementation effort levels
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum ImplementationEffort {
    Low,
    Medium,
    High,
    VeryHigh,
}

/// ROI analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ROIAnalysis {
    pub platform_roi: HashMap<QuantumPlatform, f64>,
    pub break_even_analysis: HashMap<QuantumPlatform, Duration>,
    pub value_metrics: HashMap<QuantumPlatform, ValueMetrics>,
}

/// Value metrics for ROI calculation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ValueMetrics {
    pub time_to_result: Duration,
    pub result_quality: f64,
    pub reliability_score: f64,
    pub innovation_value: f64,
}

/// Platform recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PlatformRecommendation {
    pub recommendation_type: RecommendationType,
    pub target_platform: QuantumPlatform,
    pub use_case: String,
    pub confidence_score: f64,
    pub reasoning: String,
    pub conditions: Vec<String>,
}

/// Types of recommendations
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum RecommendationType {
    BestOverall,
    BestForUseCase,
    MostCostEffective,
    FastestExecution,
    HighestFidelity,
    MostReliable,
    BestScalability,
    Avoid,
}

/// Execution summary
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExecutionSummary {
    pub total_execution_time: Duration,
    pub total_benchmarks_run: usize,
    pub successful_benchmarks: usize,
    pub failed_benchmarks: usize,
    pub platforms_tested: usize,
    pub total_shots_executed: usize,
    pub total_cost: f64,
    pub data_points_collected: usize,
}

impl Default for CrossPlatformBenchmarkConfig {
    fn default() -> Self {
        Self {
            target_platforms: vec![
                QuantumPlatform::IBMQuantum("ibmq_qasm_simulator".to_string()),
                QuantumPlatform::AWSBraket(
                    "arn:aws:braket:::device/quantum-simulator/amazon/sv1".to_string(),
                ),
            ],
            complexity_levels: vec![
                ComplexityLevel {
                    name: "Simple".to_string(),
                    qubit_count: 2,
                    circuit_depth: 5,
                    gate_count_range: (5, 15),
                    two_qubit_gate_ratio: 0.3,
                    description: "Basic circuits for connectivity testing".to_string(),
                },
                ComplexityLevel {
                    name: "Medium".to_string(),
                    qubit_count: 5,
                    circuit_depth: 20,
                    gate_count_range: (20, 50),
                    two_qubit_gate_ratio: 0.4,
                    description: "Intermediate circuits for performance assessment".to_string(),
                },
                ComplexityLevel {
                    name: "Complex".to_string(),
                    qubit_count: 10,
                    circuit_depth: 50,
                    gate_count_range: (50, 150),
                    two_qubit_gate_ratio: 0.5,
                    description: "Complex circuits for scalability testing".to_string(),
                },
            ],
            statistical_config: StatisticalAnalysisConfig::default(),
            parallel_config: ParallelBenchmarkConfig::default(),
            benchmark_timeout: Duration::from_secs(300),
            repetitions: 10,
            enable_cost_analysis: true,
            enable_latency_analysis: true,
            enable_reliability_analysis: true,
            custom_circuits: Vec::new(),
        }
    }
}

impl Default for StatisticalAnalysisConfig {
    fn default() -> Self {
        Self {
            confidence_level: 0.95,
            enable_anova: true,
            enable_pairwise_comparisons: true,
            enable_outlier_detection: true,
            enable_distribution_fitting: true,
            enable_correlation_analysis: true,
            min_sample_size: 5,
        }
    }
}

impl Default for ParallelBenchmarkConfig {
    fn default() -> Self {
        Self {
            enable_parallel: true,
            max_concurrent: 4,
            load_balancing: LoadBalancingStrategy::ResourceBased,
            resource_allocation: HashMap::new(),
        }
    }
}

/// Main cross-platform benchmarking engine
pub struct CrossPlatformBenchmarker {
    config: CrossPlatformBenchmarkConfig,
    calibration_manager: CalibrationManager,
    // Platform clients
    ibm_client: Option<IBMQuantumClient>,
    aws_client: Option<AWSBraketClient>,
    azure_client: Option<AzureQuantumClient>,
}

impl CrossPlatformBenchmarker {
    /// Create a new cross-platform benchmarker
    pub const fn new(
        config: CrossPlatformBenchmarkConfig,
        calibration_manager: CalibrationManager,
    ) -> Self {
        Self {
            config,
            calibration_manager,
            ibm_client: None,
            aws_client: None,
            azure_client: None,
        }
    }

    /// Run comprehensive cross-platform benchmarks
    pub async fn run_comprehensive_benchmark(
        &mut self,
    ) -> DeviceResult<CrossPlatformBenchmarkResult> {
        let start_time = Instant::now();
        let timestamp = SystemTime::now();
        let benchmark_id = format!(
            "benchmark_{}",
            timestamp
                .duration_since(SystemTime::UNIX_EPOCH)
                .expect("System time should be after UNIX epoch")
                .as_secs()
        );

        // Initialize platform clients
        self.initialize_platform_clients().await?;

        // Run benchmarks on each platform
        let mut platform_results = HashMap::new();
        let mut total_benchmarks = 0;
        let mut successful_benchmarks = 0;
        let mut failed_benchmarks = 0;
        let mut total_shots = 0;
        let mut total_cost = 0.0;

        for platform in &self.config.target_platforms {
            match timeout(
                self.config.benchmark_timeout,
                self.run_platform_benchmark(platform),
            )
            .await
            {
                Ok(Ok(result)) => {
                    total_benchmarks += result.circuit_results.len();
                    successful_benchmarks += result
                        .circuit_results
                        .values()
                        .map(|results| results.len())
                        .sum::<usize>();
                    total_shots += result
                        .circuit_results
                        .values()
                        .flat_map(|results| results.iter())
                        .map(|r| r.shots)
                        .sum::<usize>();
                    total_cost += result
                        .circuit_results
                        .values()
                        .flat_map(|results| results.iter())
                        .map(|r| r.cost)
                        .sum::<f64>();
                    platform_results.insert(platform.clone(), result);
                }
                Ok(Err(e)) => {
                    eprintln!("Platform benchmark failed for {platform:?}: {e}");
                    failed_benchmarks += 1;
                }
                Err(_) => {
                    eprintln!("Platform benchmark timed out for {platform:?}");
                    failed_benchmarks += 1;
                }
            }
        }

        // Perform comparative analysis
        let comparative_analysis = self.perform_comparative_analysis(&platform_results)?;

        // Perform statistical analysis
        let statistical_analysis =
            self.perform_cross_platform_statistical_analysis(&platform_results)?;

        // Perform cost analysis
        let cost_analysis = if self.config.enable_cost_analysis {
            Some(self.perform_cost_analysis(&platform_results)?)
        } else {
            None
        };

        // Generate recommendations
        let recommendations = self.generate_platform_recommendations(
            &platform_results,
            &comparative_analysis,
            &statistical_analysis,
        )?;

        let execution_time = start_time.elapsed();
        let execution_summary = ExecutionSummary {
            total_execution_time: execution_time,
            total_benchmarks_run: total_benchmarks,
            successful_benchmarks,
            failed_benchmarks,
            platforms_tested: platform_results.len(),
            total_shots_executed: total_shots,
            total_cost,
            data_points_collected: platform_results
                .values()
                .map(|r| r.circuit_results.values().map(|v| v.len()).sum::<usize>())
                .sum(),
        };

        Ok(CrossPlatformBenchmarkResult {
            benchmark_id,
            timestamp,
            config: self.config.clone(),
            platform_results,
            comparative_analysis,
            statistical_analysis,
            cost_analysis,
            recommendations,
            execution_summary,
        })
    }

    /// Initialize platform clients
    async fn initialize_platform_clients(&mut self) -> DeviceResult<()> {
        // Initialize clients for each platform type
        for platform in &self.config.target_platforms {
            match platform {
                QuantumPlatform::IBMQuantum(_) => {
                    if self.ibm_client.is_none() {
                        // Initialize IBM client (would need actual credentials)
                        // self.ibm_client = Some(IBMQuantumClient::new(credentials)?);
                    }
                }
                QuantumPlatform::AWSBraket(_) => {
                    if self.aws_client.is_none() {
                        // Initialize AWS client (would need actual credentials)
                        // self.aws_client = Some(AWSBraketClient::new(credentials)?);
                    }
                }
                QuantumPlatform::AzureQuantum(_) => {
                    if self.azure_client.is_none() {
                        // Initialize Azure client (would need actual credentials)
                        // self.azure_client = Some(AzureQuantumClient::new(credentials)?);
                    }
                }
                _ => {
                    // Handle other platforms
                }
            }
        }
        Ok(())
    }

    /// Run benchmark on a specific platform
    async fn run_platform_benchmark(
        &self,
        platform: &QuantumPlatform,
    ) -> DeviceResult<PlatformBenchmarkResult> {
        // Get device information
        let device_info = self.get_device_info(platform).await?;

        // Run benchmarks for each complexity level
        let mut circuit_results = HashMap::new();
        let mut all_metrics = Vec::new();

        for complexity in &self.config.complexity_levels {
            let mut level_results = Vec::new();

            // Generate benchmark circuits for this complexity level
            let benchmark_circuits = self.generate_benchmark_circuits(complexity)?;

            for circuit in benchmark_circuits {
                // Run circuit multiple times for statistical significance
                for _ in 0..self.config.repetitions {
                    match self.execute_circuit_on_platform(&circuit, platform).await {
                        Ok(result) => {
                            level_results.push(result.clone());
                            all_metrics.push(result.fidelity);
                        }
                        Err(e) => {
                            eprintln!("Circuit execution failed: {e}");
                        }
                    }
                }
            }

            circuit_results.insert(complexity.name.clone(), level_results);
        }

        // Analyze platform performance
        let benchmark_metrics = self.calculate_platform_metrics(&circuit_results)?;
        let performance_analysis = self.analyze_platform_performance(&circuit_results)?;
        let reliability_metrics = self.calculate_reliability_metrics(&circuit_results)?;
        let latency_analysis = self.analyze_latency(&circuit_results)?;
        let error_analysis = self.analyze_errors(&circuit_results)?;

        Ok(PlatformBenchmarkResult {
            platform: platform.clone(),
            device_info,
            benchmark_metrics,
            circuit_results,
            performance_analysis,
            reliability_metrics,
            latency_analysis,
            error_analysis,
        })
    }

    /// Get device information for a platform
    async fn get_device_info(&self, platform: &QuantumPlatform) -> DeviceResult<DeviceInfo> {
        // This would query the actual device information from the platform
        // For now, return mock data
        let (device_name, provider, technology) = match platform {
            QuantumPlatform::IBMQuantum(name) => (
                name.clone(),
                "IBM".to_string(),
                QuantumTechnology::Superconducting,
            ),
            QuantumPlatform::AWSBraket(arn) => (
                arn.clone(),
                "AWS".to_string(),
                QuantumTechnology::Superconducting,
            ),
            QuantumPlatform::AzureQuantum(target) => (
                target.clone(),
                "Microsoft".to_string(),
                QuantumTechnology::TrappedIon,
            ),
            QuantumPlatform::IonQ(name) => (
                name.clone(),
                "IonQ".to_string(),
                QuantumTechnology::TrappedIon,
            ),
            QuantumPlatform::Rigetti(name) => (
                name.clone(),
                "Rigetti".to_string(),
                QuantumTechnology::Superconducting,
            ),
            QuantumPlatform::GoogleQuantumAI(name) => (
                name.clone(),
                "Google".to_string(),
                QuantumTechnology::Superconducting,
            ),
            QuantumPlatform::Custom(name) => (
                name.clone(),
                "Custom".to_string(),
                QuantumTechnology::Other("Custom".to_string()),
            ),
        };

        Ok(DeviceInfo {
            device_name,
            provider,
            technology,
            qubit_count: 20, // Mock value
            connectivity: ConnectivityInfo {
                topology_type: TopologyType::Heavy,
                connectivity_graph: Array2::eye(20),
                average_connectivity: 2.5,
                max_path_length: 5,
            },
            capabilities: query_backend_capabilities(
                crate::translation::HardwareBackend::IBMQuantum,
            ),
            calibration_date: Some(SystemTime::now()),
        })
    }

    /// Generate benchmark circuits for a complexity level
    fn generate_benchmark_circuits(
        &self,
        complexity: &ComplexityLevel,
    ) -> DeviceResult<Vec<Circuit<16>>> {
        let mut circuits = Vec::new();

        // Generate random circuits within the complexity constraints
        use scirs2_core::random::prelude::*;
        let mut rng = thread_rng();

        let num_circuits = 5; // Generate 5 circuits per complexity level

        for i in 0..num_circuits {
            let mut circuit = Circuit::<16>::new();
            let gate_count =
                rng.gen_range(complexity.gate_count_range.0..=complexity.gate_count_range.1);
            let two_qubit_gates = (gate_count as f64 * complexity.two_qubit_gate_ratio) as usize;
            let single_qubit_gates = gate_count - two_qubit_gates;

            // Add random single-qubit gates
            for _ in 0..single_qubit_gates {
                let qubit = rng.gen_range(0..complexity.qubit_count) as u32;
                match rng.gen_range(0..4) {
                    0 => {
                        circuit.h(QubitId(qubit))?;
                    }
                    1 => {
                        circuit.x(QubitId(qubit))?;
                    }
                    2 => {
                        circuit.y(QubitId(qubit))?;
                    }
                    3 => {
                        circuit.z(QubitId(qubit))?;
                    }
                    _ => unreachable!(),
                }
            }

            // Add random two-qubit gates
            for _ in 0..two_qubit_gates {
                let qubit1 = rng.gen_range(0..complexity.qubit_count) as u32;
                let mut qubit2 = rng.gen_range(0..complexity.qubit_count) as u32;
                while qubit2 == qubit1 {
                    qubit2 = rng.gen_range(0..complexity.qubit_count) as u32;
                }
                let _ = circuit.cnot(QubitId(qubit1), QubitId(qubit2));
            }

            circuits.push(circuit);
        }

        // Add custom circuits if specified
        for custom_circuit in &self.config.custom_circuits {
            if custom_circuit.qubit_count <= complexity.qubit_count {
                // Parse and add custom circuit (simplified)
                let circuit = self.parse_custom_circuit(custom_circuit)?;
                circuits.push(circuit);
            }
        }

        Ok(circuits)
    }

    /// Execute a circuit on a specific platform
    async fn execute_circuit_on_platform(
        &self,
        circuit: &Circuit<16>,
        platform: &QuantumPlatform,
    ) -> DeviceResult<CircuitBenchmarkResult> {
        let start_time = Instant::now();
        let queue_start = Instant::now();

        // Simulate circuit execution (in practice, would use actual platform APIs)
        let shots = 1000;
        let execution_time = Duration::from_millis(thread_rng().gen_range(100..2000));
        let queue_time = Duration::from_millis(thread_rng().gen_range(10..5000));

        // Simulate measurement results
        let mut measurement_counts = HashMap::new();
        let num_qubits = circuit
            .gates()
            .iter()
            .flat_map(|g| g.qubits())
            .map(|q| q.0 as usize)
            .max()
            .unwrap_or(0)
            + 1;

        let num_outcomes = 2_usize.pow(num_qubits.min(8) as u32); // Limit for simulation
        for i in 0..num_outcomes.min(8) {
            let outcome = format!("{:0width$b}", i, width = num_qubits.min(8));
            let count = thread_rng().gen_range(0..shots / num_outcomes * 2);
            if count > 0 {
                measurement_counts.insert(outcome, count);
            }
        }

        // Calculate metrics
        let fidelity = self.calculate_circuit_fidelity(&measurement_counts, circuit)?;
        let success_rate =
            measurement_counts.values().map(|&c| c as f64).sum::<f64>() / shots as f64;
        let cost = self.calculate_execution_cost(platform, shots, execution_time)?;

        Ok(CircuitBenchmarkResult {
            circuit_name: format!("circuit_{}", circuit.gates().len()),
            complexity_level: "medium".to_string(), // Would be passed as parameter
            execution_time,
            queue_time,
            total_time: queue_time + execution_time,
            fidelity,
            success_rate,
            cost,
            shots,
            measurement_counts,
            error_messages: Vec::new(),
        })
    }

    // Helper methods for analysis and calculations

    fn parse_custom_circuit(&self, custom: &CustomBenchmarkCircuit) -> DeviceResult<Circuit<16>> {
        // Simplified circuit parsing - would implement proper QASM/circuit parsing
        Ok(Circuit::<16>::new())
    }

    fn calculate_circuit_fidelity(
        &self,
        _measurement_counts: &HashMap<String, usize>,
        _circuit: &Circuit<16>,
    ) -> DeviceResult<f64> {
        // Simplified fidelity calculation
        Ok(thread_rng().gen_range(0.8..0.99))
    }

    fn calculate_execution_cost(
        &self,
        platform: &QuantumPlatform,
        shots: usize,
        execution_time: Duration,
    ) -> DeviceResult<f64> {
        // Simplified cost calculation based on platform
        let base_cost = match platform {
            QuantumPlatform::IBMQuantum(_) => 0.0001, // Free tier
            QuantumPlatform::AWSBraket(_) => 0.01,
            QuantumPlatform::AzureQuantum(_) => 0.008,
            QuantumPlatform::IonQ(_) => 0.01,
            _ => 0.005,
        };

        Ok(base_cost * shots as f64 + execution_time.as_secs_f64() * 0.001)
    }

    fn calculate_platform_metrics(
        &self,
        circuit_results: &HashMap<String, Vec<CircuitBenchmarkResult>>,
    ) -> DeviceResult<BenchmarkMetrics> {
        let all_results: Vec<&CircuitBenchmarkResult> = circuit_results
            .values()
            .flat_map(|results| results.iter())
            .collect();

        if all_results.is_empty() {
            return Ok(BenchmarkMetrics {
                overall_score: 0.0,
                fidelity_score: 0.0,
                speed_score: 0.0,
                reliability_score: 0.0,
                cost_efficiency_score: 0.0,
                scalability_score: 0.0,
                detailed_metrics: HashMap::new(),
            });
        }

        let avg_fidelity =
            all_results.iter().map(|r| r.fidelity).sum::<f64>() / all_results.len() as f64;
        let avg_execution_time = all_results
            .iter()
            .map(|r| r.execution_time.as_secs_f64())
            .sum::<f64>()
            / all_results.len() as f64;
        let avg_success_rate =
            all_results.iter().map(|r| r.success_rate).sum::<f64>() / all_results.len() as f64;
        let avg_cost = all_results.iter().map(|r| r.cost).sum::<f64>() / all_results.len() as f64;

        let fidelity_score = avg_fidelity * 100.0;
        let speed_score = (1.0 / avg_execution_time * 100.0).min(100.0);
        let reliability_score = avg_success_rate * 100.0;
        let cost_efficiency_score = (1.0 / (avg_cost + 0.001) * 10.0).min(100.0);
        let scalability_score = 75.0; // Would calculate based on circuit complexity scaling

        let overall_score = (fidelity_score * 0.3
            + speed_score * 0.2
            + reliability_score * 0.2
            + cost_efficiency_score * 0.15
            + scalability_score * 0.15)
            .min(100.0);

        let mut detailed_metrics = HashMap::new();
        detailed_metrics.insert("avg_fidelity".to_string(), avg_fidelity);
        detailed_metrics.insert("avg_execution_time".to_string(), avg_execution_time);
        detailed_metrics.insert("avg_success_rate".to_string(), avg_success_rate);
        detailed_metrics.insert("avg_cost".to_string(), avg_cost);

        Ok(BenchmarkMetrics {
            overall_score,
            fidelity_score,
            speed_score,
            reliability_score,
            cost_efficiency_score,
            scalability_score,
            detailed_metrics,
        })
    }

    fn analyze_platform_performance(
        &self,
        circuit_results: &HashMap<String, Vec<CircuitBenchmarkResult>>,
    ) -> DeviceResult<PlatformPerformanceAnalysis> {
        let all_results: Vec<&CircuitBenchmarkResult> = circuit_results
            .values()
            .flat_map(|results| results.iter())
            .collect();

        if all_results.is_empty() {
            return Err(DeviceError::APIError(
                "No results available for analysis".into(),
            ));
        }

        // Calculate throughput (circuits per second)
        let total_time: f64 = all_results.iter().map(|r| r.total_time.as_secs_f64()).sum();
        let throughput = all_results.len() as f64 / total_time;

        // Latency distribution
        let latencies: Vec<f64> = all_results
            .iter()
            .map(|r| r.total_time.as_secs_f64())
            .collect();
        let latency_array = Array1::from_vec(latencies.clone());

        let mean_latency = mean(&latency_array.view()).unwrap_or(0.0);
        let median_latency = {
            let mut sorted_latencies = latencies.clone();
            sorted_latencies.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
            let mid = sorted_latencies.len() / 2;
            if sorted_latencies.len() % 2 == 0 {
                f64::midpoint(sorted_latencies[mid - 1], sorted_latencies[mid])
            } else {
                sorted_latencies[mid]
            }
        };
        let std_dev_latency = std(&latency_array.view(), 1, None).unwrap_or(0.0);

        let mut percentiles = HashMap::new();
        let mut sorted_latencies = latencies;
        sorted_latencies.sort_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal));
        percentiles.insert(50, median_latency);
        percentiles.insert(
            95,
            sorted_latencies[(sorted_latencies.len() as f64 * 0.95) as usize],
        );
        percentiles.insert(
            99,
            sorted_latencies[(sorted_latencies.len() as f64 * 0.99) as usize],
        );

        let latency_distribution = LatencyDistribution {
            mean: mean_latency,
            median: median_latency,
            std_dev: std_dev_latency,
            percentiles,
            distribution_type: "Normal".to_string(), // Would determine actual distribution
        };

        // Simplified scalability analysis
        let scalability_analysis = ScalabilityAnalysis {
            qubit_scaling_factor: 1.2, // Would calculate based on actual scaling tests
            depth_scaling_factor: 1.5,
            theoretical_limits: TheoreticalLimits {
                max_qubit_count: 100,
                max_circuit_depth: 1000,
                max_gate_count: 10000,
                coherence_limited_depth: 50,
            },
            bottleneck_analysis: BottleneckAnalysis {
                primary_bottleneck: BottleneckType::QueueTime,
                bottleneck_severity: 0.3,
                mitigation_strategies: vec!["Optimize timing".to_string()],
            },
        };

        // Resource utilization
        let resource_utilization = ResourceUtilization {
            qubit_utilization: 0.6, // Would calculate based on actual circuit requirements
            gate_efficiency: 0.8,
            shot_efficiency: 0.9,
            time_efficiency: 0.7,
        };

        // Performance trends (simplified)
        let performance_trends = PerformanceTrends {
            fidelity_trend: TrendAnalysis {
                slope: 0.001,
                r_squared: 0.8,
                trend_direction: TrendDirection::Improving,
                confidence_interval: (0.0005, 0.0015),
            },
            latency_trend: TrendAnalysis {
                slope: -0.01,
                r_squared: 0.6,
                trend_direction: TrendDirection::Improving,
                confidence_interval: (-0.02, 0.0),
            },
            reliability_trend: TrendAnalysis {
                slope: 0.005,
                r_squared: 0.7,
                trend_direction: TrendDirection::Stable,
                confidence_interval: (0.0, 0.01),
            },
            cost_trend: TrendAnalysis {
                slope: -0.001,
                r_squared: 0.5,
                trend_direction: TrendDirection::Improving,
                confidence_interval: (-0.002, 0.0),
            },
        };

        Ok(PlatformPerformanceAnalysis {
            throughput,
            latency_distribution,
            scalability_analysis,
            resource_utilization,
            performance_trends,
        })
    }

    fn calculate_reliability_metrics(
        &self,
        circuit_results: &HashMap<String, Vec<CircuitBenchmarkResult>>,
    ) -> DeviceResult<ReliabilityMetrics> {
        let all_results: Vec<&CircuitBenchmarkResult> = circuit_results
            .values()
            .flat_map(|results| results.iter())
            .collect();

        let successful_runs = all_results
            .iter()
            .filter(|r| r.error_messages.is_empty())
            .count();
        let uptime_percentage = (successful_runs as f64 / all_results.len() as f64) * 100.0;

        let mut error_rates = HashMap::new();
        error_rates.insert(
            "execution_error".to_string(),
            (all_results.len() - successful_runs) as f64 / all_results.len() as f64,
        );

        Ok(ReliabilityMetrics {
            uptime_percentage,
            mtbf: Duration::from_secs(3600), // 1 hour average
            mttr: Duration::from_secs(300),  // 5 minutes average
            error_rates,
            consistency_score: uptime_percentage / 100.0,
        })
    }

    fn analyze_latency(
        &self,
        circuit_results: &HashMap<String, Vec<CircuitBenchmarkResult>>,
    ) -> DeviceResult<LatencyAnalysis> {
        let all_results: Vec<&CircuitBenchmarkResult> = circuit_results
            .values()
            .flat_map(|results| results.iter())
            .collect();

        if all_results.is_empty() {
            return Err(DeviceError::APIError(
                "No results for latency analysis".into(),
            ));
        }

        let avg_queue =
            all_results.iter().map(|r| r.queue_time).sum::<Duration>() / all_results.len() as u32;
        let avg_execution = all_results
            .iter()
            .map(|r| r.execution_time)
            .sum::<Duration>()
            / all_results.len() as u32;
        let avg_total =
            all_results.iter().map(|r| r.total_time).sum::<Duration>() / all_results.len() as u32;

        let latency_values: Vec<f64> = all_results
            .iter()
            .map(|r| r.total_time.as_secs_f64())
            .collect();
        let latency_array = Array1::from_vec(latency_values);
        let latency_variability = std(&latency_array.view(), 1, None).unwrap_or(0.0);

        Ok(LatencyAnalysis {
            submission_latency: Duration::from_millis(100), // Mock value
            queue_latency: avg_queue,
            execution_latency: avg_execution,
            retrieval_latency: Duration::from_millis(50), // Mock value
            total_latency: avg_total,
            latency_variability,
        })
    }

    fn analyze_errors(
        &self,
        circuit_results: &HashMap<String, Vec<CircuitBenchmarkResult>>,
    ) -> DeviceResult<ErrorAnalysis> {
        let all_results: Vec<&CircuitBenchmarkResult> = circuit_results
            .values()
            .flat_map(|results| results.iter())
            .collect();

        let mut systematic_errors = HashMap::new();
        let mut random_errors = HashMap::new();

        // Simple error analysis
        systematic_errors.insert("gate_error".to_string(), 0.001);
        random_errors.insert("thermal_noise".to_string(), 0.0005);

        let n_qubits = 5; // Simplified
        let error_correlations = Array2::eye(n_qubits);

        Ok(ErrorAnalysis {
            systematic_errors,
            random_errors,
            coherent_errors: HashMap::new(),
            readout_errors: HashMap::new(),
            gate_errors: HashMap::new(),
            error_correlations,
        })
    }

    fn perform_comparative_analysis(
        &self,
        platform_results: &HashMap<QuantumPlatform, PlatformBenchmarkResult>,
    ) -> DeviceResult<ComparativeAnalysisResult> {
        let mut platform_rankings = HashMap::new();
        let mut performance_matrix_data = Vec::new();
        let mut statistical_comparisons = HashMap::new();

        // Create rankings
        let platforms: Vec<_> = platform_results.keys().collect();
        let n_platforms = platforms.len();

        for (i, (platform, result)) in platform_results.iter().enumerate() {
            let ranking = PlatformRanking {
                overall_rank: i + 1, // Simplified ranking
                category_ranks: HashMap::new(),
                normalized_scores: HashMap::new(),
                relative_performance: result.benchmark_metrics.overall_score / 100.0,
            };
            platform_rankings.insert(format!("{platform:?}"), ranking);

            // Add to performance matrix
            performance_matrix_data.extend(vec![
                result.benchmark_metrics.fidelity_score,
                result.benchmark_metrics.speed_score,
                result.benchmark_metrics.reliability_score,
                result.benchmark_metrics.cost_efficiency_score,
            ]);
        }

        let performance_matrix = Array2::from_shape_vec((n_platforms, 4), performance_matrix_data)
            .map_err(|e| DeviceError::APIError(format!("Matrix creation error: {e}")))?;

        // Cost effectiveness analysis
        let mut cost_per_shot = HashMap::new();
        let mut cost_per_gate = HashMap::new();
        let mut cost_per_successful_result = HashMap::new();
        let mut value_for_money_score = HashMap::new();

        for (platform, result) in platform_results {
            let total_cost: f64 = result
                .circuit_results
                .values()
                .flat_map(|results| results.iter())
                .map(|r| r.cost)
                .sum();
            let total_shots: usize = result
                .circuit_results
                .values()
                .flat_map(|results| results.iter())
                .map(|r| r.shots)
                .sum();

            cost_per_shot.insert(platform.clone(), total_cost / total_shots as f64);
            cost_per_gate.insert(platform.clone(), total_cost / 100.0); // Simplified
            cost_per_successful_result.insert(
                platform.clone(),
                total_cost
                    / (total_shots as f64 * result.benchmark_metrics.reliability_score / 100.0),
            );
            value_for_money_score.insert(
                platform.clone(),
                result.benchmark_metrics.overall_score / (total_cost + 0.001),
            );
        }

        let cost_effectiveness_analysis = CostEffectivenessAnalysis {
            cost_per_shot,
            cost_per_gate,
            cost_per_successful_result,
            value_for_money_score,
            cost_optimization_recommendations: Vec::new(),
        };

        // Use case recommendations (simplified)
        let mut use_case_recommendations = HashMap::new();
        use_case_recommendations.insert(
            "general_purpose".to_string(),
            platforms.iter().map(|&p| p.clone()).collect(),
        );

        Ok(ComparativeAnalysisResult {
            platform_rankings,
            performance_matrix,
            statistical_comparisons,
            cost_effectiveness_analysis,
            use_case_recommendations,
        })
    }

    fn perform_cross_platform_statistical_analysis(
        &self,
        platform_results: &HashMap<QuantumPlatform, PlatformBenchmarkResult>,
    ) -> DeviceResult<CrossPlatformStatisticalAnalysis> {
        // Simplified statistical analysis
        let anova_results = HashMap::new();
        let correlation_analysis = CorrelationAnalysisResult {
            correlationmatrix: Array2::eye(4),
            significant_correlations: Vec::new(),
            correlation_network: HashMap::new(),
        };
        let cluster_analysis = ClusterAnalysisResult {
            cluster_assignments: HashMap::new(),
            cluster_centers: Array2::zeros((2, 4)),
            silhouette_score: 0.7,
            cluster_interpretations: HashMap::new(),
        };
        let principal_component_analysis = PCAResult {
            principal_components: Array2::eye(4),
            explained_variance_ratio: Array1::from_vec(vec![0.4, 0.3, 0.2, 0.1]),
            cumulative_variance: Array1::from_vec(vec![0.4, 0.7, 0.9, 1.0]),
            loadings: Array2::eye(4),
            platform_scores: HashMap::new(),
        };
        let outlier_detection = OutlierDetectionResult {
            outliers: HashMap::new(),
            outlier_scores: HashMap::new(),
            detection_method: "IQR".to_string(),
            threshold: 1.5,
        };
        let distribution_analysis = DistributionAnalysisResult {
            platform_distributions: HashMap::new(),
            distribution_comparisons: HashMap::new(),
            normality_tests: HashMap::new(),
        };

        Ok(CrossPlatformStatisticalAnalysis {
            anova_results,
            correlation_analysis,
            cluster_analysis,
            principal_component_analysis,
            outlier_detection,
            distribution_analysis,
        })
    }

    fn perform_cost_analysis(
        &self,
        platform_results: &HashMap<QuantumPlatform, PlatformBenchmarkResult>,
    ) -> DeviceResult<CrossPlatformCostAnalysis> {
        let mut total_costs = HashMap::new();
        let mut cost_breakdown = HashMap::new();
        let mut cost_trends = HashMap::new();

        for (platform, result) in platform_results {
            let total_cost: f64 = result
                .circuit_results
                .values()
                .flat_map(|results| results.iter())
                .map(|r| r.cost)
                .sum();

            total_costs.insert(platform.clone(), total_cost);

            cost_breakdown.insert(
                platform.clone(),
                CostBreakdown {
                    compute_cost: total_cost * 0.8,
                    queue_cost: total_cost * 0.1,
                    storage_cost: total_cost * 0.05,
                    data_transfer_cost: total_cost * 0.05,
                    other_costs: HashMap::new(),
                },
            );

            cost_trends.insert(
                platform.clone(),
                CostTrend {
                    trend_direction: TrendDirection::Stable,
                    cost_per_month: vec![total_cost; 6], // Mock 6 months of data
                    projected_costs: vec![total_cost; 6],
                    cost_volatility: 0.1,
                },
            );
        }

        let cost_optimization = CostOptimizationAnalysis {
            optimization_opportunities: Vec::new(),
            potential_savings: 100.0,
            optimization_strategies: vec!["Use lower-cost platforms for development".to_string()],
        };

        let roi_analysis = ROIAnalysis {
            platform_roi: HashMap::new(),
            break_even_analysis: HashMap::new(),
            value_metrics: HashMap::new(),
        };

        Ok(CrossPlatformCostAnalysis {
            total_costs,
            cost_breakdown,
            cost_trends,
            cost_optimization,
            roi_analysis,
        })
    }

    fn generate_platform_recommendations(
        &self,
        platform_results: &HashMap<QuantumPlatform, PlatformBenchmarkResult>,
        comparative_analysis: &ComparativeAnalysisResult,
        _statistical_analysis: &CrossPlatformStatisticalAnalysis,
    ) -> DeviceResult<Vec<PlatformRecommendation>> {
        let mut recommendations = Vec::new();

        // Find best overall platform
        if let Some((best_platform, _)) = platform_results.iter().max_by(|a, b| {
            a.1.benchmark_metrics
                .overall_score
                .partial_cmp(&b.1.benchmark_metrics.overall_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        }) {
            recommendations.push(PlatformRecommendation {
                recommendation_type: RecommendationType::BestOverall,
                target_platform: best_platform.clone(),
                use_case: "General quantum computing tasks".to_string(),
                confidence_score: 0.9,
                reasoning: "Highest overall benchmark score".to_string(),
                conditions: vec!["Consider cost constraints".to_string()],
            });
        }

        // Find most cost-effective platform
        if let Some((most_cost_effective, _)) = comparative_analysis
            .cost_effectiveness_analysis
            .value_for_money_score
            .iter()
            .max_by(|a, b| a.1.partial_cmp(b.1).unwrap_or(std::cmp::Ordering::Equal))
        {
            recommendations.push(PlatformRecommendation {
                recommendation_type: RecommendationType::MostCostEffective,
                target_platform: most_cost_effective.clone(),
                use_case: "Budget-conscious development and testing".to_string(),
                confidence_score: 0.8,
                reasoning: "Best value for money ratio".to_string(),
                conditions: vec!["May have lower performance".to_string()],
            });
        }

        Ok(recommendations)
    }
}

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

    #[test]
    fn test_cross_platform_config_default() {
        let config = CrossPlatformBenchmarkConfig::default();
        assert_eq!(config.target_platforms.len(), 2);
        assert_eq!(config.complexity_levels.len(), 3);
        assert!(config.enable_cost_analysis);
    }

    #[test]
    fn test_complexity_level_creation() {
        let complexity = ComplexityLevel {
            name: "Test".to_string(),
            qubit_count: 5,
            circuit_depth: 10,
            gate_count_range: (10, 30),
            two_qubit_gate_ratio: 0.4,
            description: "Test complexity level".to_string(),
        };

        assert_eq!(complexity.name, "Test");
        assert_eq!(complexity.qubit_count, 5);
        assert_eq!(complexity.two_qubit_gate_ratio, 0.4);
    }

    #[test]
    fn test_platform_enum() {
        let ibm_platform = QuantumPlatform::IBMQuantum("ibmq_qasm_simulator".to_string());
        let aws_platform = QuantumPlatform::AWSBraket(
            "arn:aws:braket:::device/quantum-simulator/amazon/sv1".to_string(),
        );

        assert_ne!(ibm_platform, aws_platform);
    }
}