quantrs2_device/

cost_optimization.rs

//! Provider Cost Optimization Engine
//!
//! This module provides sophisticated cost optimization capabilities across different
//! quantum computing providers, including cost estimation, budget management,
//! provider comparison, and automated cost optimization strategies.

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

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

// SciRS2 integration for advanced cost analytics
#[cfg(feature = "scirs2")]
use scirs2_graph::{dijkstra_path, minimum_spanning_tree, Graph};
#[cfg(feature = "scirs2")]
use scirs2_optimize::{differential_evolution, minimize, OptimizeResult};
#[cfg(feature = "scirs2")]
use scirs2_stats::{corrcoef, mean, pearsonr, spearmanr, std};

use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use serde::{Deserialize, Serialize};
use tokio::sync::{Mutex as AsyncMutex, RwLock as AsyncRwLock};

use crate::{
    backend_traits::{query_backend_capabilities, BackendCapabilities},
    job_scheduling::{JobPriority, QuantumJobScheduler, SchedulingStrategy},
    translation::HardwareBackend,
    DeviceError, DeviceResult,
};

/// Cost optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostOptimizationConfig {
    /// Budget management settings
    pub budget_config: BudgetConfig,
    /// Cost estimation settings
    pub estimation_config: CostEstimationConfig,
    /// Optimization strategy
    pub optimization_strategy: CostOptimizationStrategy,
    /// Provider comparison settings
    pub provider_comparison: ProviderComparisonConfig,
    /// Predictive modeling settings
    pub predictive_modeling: PredictiveModelingConfig,
    /// Resource allocation optimization
    pub resource_optimization: ResourceOptimizationConfig,
    /// Real-time monitoring settings
    pub monitoring_config: CostMonitoringConfig,
    /// Alert and notification settings
    pub alert_config: CostAlertConfig,
}

/// Budget management configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BudgetConfig {
    /// Total budget limit
    pub total_budget: f64,
    /// Daily budget limit
    pub daily_budget: Option<f64>,
    /// Monthly budget limit
    pub monthly_budget: Option<f64>,
    /// Budget allocation per provider
    pub provider_budgets: HashMap<HardwareBackend, f64>,
    /// Budget allocation per circuit type
    pub circuit_type_budgets: HashMap<String, f64>,
    /// Enable automatic budget management
    pub auto_budget_management: bool,
    /// Budget rollover policy
    pub rollover_policy: BudgetRolloverPolicy,
}

/// Budget rollover policies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum BudgetRolloverPolicy {
    /// No rollover - unused budget is lost
    NoRollover,
    /// Full rollover - all unused budget carries over
    FullRollover,
    /// Percentage rollover
    PercentageRollover(f64),
    /// Fixed amount rollover
    FixedAmountRollover(f64),
    /// Capped rollover with maximum
    CappedRollover { percentage: f64, max_amount: f64 },
}

/// Cost estimation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostEstimationConfig {
    /// Estimation models per provider
    pub provider_models: HashMap<HardwareBackend, CostModel>,
    /// Include queue time in estimates
    pub include_queue_time: bool,
    /// Include setup/teardown costs
    pub include_overhead_costs: bool,
    /// Estimation accuracy target
    pub accuracy_target: f64,
    /// Update frequency for cost models
    pub model_update_frequency: Duration,
    /// Enable machine learning-based estimation
    pub enable_ml_estimation: bool,
    /// Historical data retention period
    pub data_retention_period: Duration,
}

/// Cost models for different providers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostModel {
    /// Model type
    pub model_type: CostModelType,
    /// Base cost per shot
    pub base_cost_per_shot: f64,
    /// Cost per qubit
    pub cost_per_qubit: f64,
    /// Cost per gate
    pub cost_per_gate: f64,
    /// Cost per second of execution
    pub cost_per_second: f64,
    /// Setup/teardown cost
    pub setup_cost: f64,
    /// Queue time multiplier
    pub queue_time_multiplier: f64,
    /// Peak/off-peak pricing
    pub time_based_pricing: Option<TimeBasedPricing>,
    /// Volume discounts
    pub volume_discounts: Vec<VolumeDiscount>,
    /// Custom cost factors
    pub custom_factors: HashMap<String, f64>,
}

/// Types of cost models
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CostModelType {
    /// Linear cost model
    Linear,
    /// Step-based pricing
    StepBased,
    /// Exponential pricing
    Exponential,
    /// Custom formula
    Custom(String),
    /// Machine learning model
    MachineLearning,
    /// Hybrid model combining multiple approaches
    Hybrid(Vec<CostModelType>),
}

/// Time-based pricing configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeBasedPricing {
    /// Peak hours pricing multiplier
    pub peak_multiplier: f64,
    /// Off-peak hours pricing multiplier
    pub off_peak_multiplier: f64,
    /// Peak hours definition
    pub peak_hours: Vec<(u8, u8)>, // (start_hour, end_hour) pairs
    /// Weekend pricing multiplier
    pub weekend_multiplier: Option<f64>,
    /// Holiday pricing multiplier
    pub holiday_multiplier: Option<f64>,
}

/// Volume discount configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VolumeDiscount {
    /// Minimum volume threshold
    pub min_volume: f64,
    /// Maximum volume threshold (None for unlimited)
    pub max_volume: Option<f64>,
    /// Discount percentage (0.0 to 1.0)
    pub discount_percentage: f64,
    /// Discount type
    pub discount_type: DiscountType,
}

/// Types of volume discounts
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DiscountType {
    /// Percentage discount
    Percentage,
    /// Fixed amount discount
    FixedAmount,
    /// Tiered pricing
    TieredPricing,
    /// Custom discount formula
    Custom(String),
}

/// Cost optimization strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CostOptimizationStrategy {
    /// Minimize total cost
    MinimizeCost,
    /// Maximize cost-performance ratio
    MaximizeCostPerformance,
    /// Stay within budget constraints
    BudgetConstrained,
    /// Optimize for specific metrics
    MetricOptimized {
        cost_weight: f64,
        time_weight: f64,
        quality_weight: f64,
    },
    /// Machine learning-driven optimization
    MLOptimized,
    /// Custom optimization with SciRS2
    SciRS2Optimized {
        objectives: Vec<String>,
        constraints: Vec<String>,
    },
}

/// Provider comparison configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProviderComparisonConfig {
    /// Comparison metrics
    pub comparison_metrics: Vec<ComparisonMetric>,
    /// Normalization method
    pub normalization_method: NormalizationMethod,
    /// Weighting scheme
    pub metric_weights: HashMap<ComparisonMetric, f64>,
    /// Enable real-time comparison
    pub real_time_comparison: bool,
    /// Comparison update frequency
    pub update_frequency: Duration,
    /// Include provider reliability in comparison
    pub include_reliability: bool,
}

/// Metrics for provider comparison
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ComparisonMetric {
    /// Total cost
    TotalCost,
    /// Cost per shot
    CostPerShot,
    /// Cost per qubit
    CostPerQubit,
    /// Queue time
    QueueTime,
    /// Execution time
    ExecutionTime,
    /// Fidelity
    Fidelity,
    /// Availability
    Availability,
    /// Reliability
    Reliability,
    /// Custom metric
    Custom(String),
}

/// Normalization methods for comparison
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum NormalizationMethod {
    /// Min-max normalization
    MinMax,
    /// Z-score normalization
    ZScore,
    /// Robust normalization
    Robust,
    /// Percentile-based normalization
    Percentile(f64),
    /// Custom normalization
    Custom(String),
}

/// Predictive modeling configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictiveModelingConfig {
    /// Enable predictive cost modeling
    pub enabled: bool,
    /// Prediction horizon
    pub prediction_horizon: Duration,
    /// Model types to use
    pub model_types: Vec<PredictiveModelType>,
    /// Feature engineering settings
    pub feature_engineering: FeatureEngineeringConfig,
    /// Model training frequency
    pub training_frequency: Duration,
    /// Prediction confidence threshold
    pub confidence_threshold: f64,
    /// Enable ensemble predictions
    pub enable_ensemble: bool,
}

/// Types of predictive models
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PredictiveModelType {
    /// Linear regression
    LinearRegression,
    /// Random forest
    RandomForest,
    /// Neural network
    NeuralNetwork,
    /// ARIMA time series model
    ARIMA,
    /// Support vector machine
    SVM,
    /// Gradient boosting
    GradientBoosting,
    /// SciRS2-powered models
    SciRS2Enhanced,
}

/// Feature engineering configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureEngineeringConfig {
    /// Time-based features
    pub time_features: Vec<TimeFeature>,
    /// Circuit-based features
    pub circuit_features: Vec<CircuitFeature>,
    /// Provider-based features
    pub provider_features: Vec<ProviderFeature>,
    /// Historical usage features
    pub usage_features: Vec<UsageFeature>,
    /// Feature selection method
    pub feature_selection: FeatureSelectionMethod,
}

/// Time-based features for prediction
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TimeFeature {
    HourOfDay,
    DayOfWeek,
    DayOfMonth,
    Month,
    Season,
    IsWeekend,
    IsHoliday,
    TimeToDeadline,
}

/// Circuit-based features
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CircuitFeature {
    QubitCount,
    GateCount,
    CircuitDepth,
    GateComplexity,
    ConnectivityRequirements,
    EstimatedFidelity,
    CircuitType,
}

/// Provider-based features
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ProviderFeature {
    ProviderType,
    QueueLength,
    SystemLoad,
    ErrorRates,
    Calibration,
    Availability,
    PastPerformance,
}

/// Usage-based features
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum UsageFeature {
    HistoricalCosts,
    UsagePatterns,
    PeakUsageTimes,
    VolumeDiscounts,
    BudgetUtilization,
    CostTrends,
}

/// Feature selection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum FeatureSelectionMethod {
    /// Use all features
    All,
    /// Correlation-based selection
    Correlation(f64),
    /// Mutual information
    MutualInformation,
    /// Recursive feature elimination
    RecursiveElimination,
    /// L1 regularization (Lasso)
    L1Regularization,
    /// Custom selection
    Custom(Vec<String>),
}

/// Resource optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceOptimizationConfig {
    /// Enable resource optimization
    pub enabled: bool,
    /// Optimization algorithms
    pub algorithms: Vec<ResourceOptimizationAlgorithm>,
    /// Constraint types
    pub constraints: Vec<ResourceConstraint>,
    /// Optimization frequency
    pub optimization_frequency: Duration,
    /// Parallel optimization
    pub enable_parallel_optimization: bool,
    /// Multi-objective optimization
    pub multi_objective_config: MultiObjectiveConfig,
}

/// Resource optimization algorithms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ResourceOptimizationAlgorithm {
    /// Genetic algorithm
    GeneticAlgorithm,
    /// Simulated annealing
    SimulatedAnnealing,
    /// Particle swarm optimization
    ParticleSwarmOptimization,
    /// Linear programming
    LinearProgramming,
    /// Integer programming
    IntegerProgramming,
    /// Constraint satisfaction
    ConstraintSatisfaction,
    /// SciRS2-powered optimization
    SciRS2Optimization,
}

/// Resource constraints
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ResourceConstraint {
    /// Budget constraint
    Budget(f64),
    /// Time constraint
    Time(Duration),
    /// Quality constraint
    Quality(f64),
    /// Provider constraint
    Provider(Vec<HardwareBackend>),
    /// Custom constraint
    Custom { name: String, value: f64 },
}

/// Multi-objective optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MultiObjectiveConfig {
    /// Objective functions
    pub objectives: Vec<OptimizationObjective>,
    /// Pareto frontier configuration
    pub pareto_config: ParetoConfig,
    /// Solution selection strategy
    pub selection_strategy: SolutionSelectionStrategy,
}

/// Optimization objectives
#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum OptimizationObjective {
    MinimizeCost,
    MinimizeTime,
    MaximizeQuality,
    MaximizeReliability,
    MinimizeRisk,
    Custom(String),
}

/// Pareto frontier configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParetoConfig {
    /// Maximum solutions to maintain
    pub max_solutions: usize,
    /// Convergence criteria
    pub convergence_criteria: ConvergenceCriteria,
    /// Diversity preservation
    pub diversity_preservation: bool,
}

/// Convergence criteria for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConvergenceCriteria {
    /// Maximum iterations
    pub max_iterations: usize,
    /// Tolerance
    pub tolerance: f64,
    /// Patience (iterations without improvement)
    pub patience: usize,
}

/// Solution selection strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum SolutionSelectionStrategy {
    /// Select by cost
    ByCost,
    /// Select by time
    ByTime,
    /// Select by quality
    ByQuality,
    /// Weighted selection
    Weighted(HashMap<OptimizationObjective, f64>),
    /// User preference
    UserPreference,
}

/// Cost monitoring configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostMonitoringConfig {
    /// Enable real-time monitoring
    pub real_time_monitoring: bool,
    /// Monitoring frequency
    pub monitoring_frequency: Duration,
    /// Metrics to track
    pub tracked_metrics: Vec<MonitoringMetric>,
    /// Reporting configuration
    pub reporting_config: CostReportingConfig,
    /// Dashboard configuration
    pub dashboard_config: Option<DashboardConfig>,
}

/// Metrics for cost monitoring
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum MonitoringMetric {
    TotalCost,
    CostPerProvider,
    CostPerCircuit,
    BudgetUtilization,
    CostTrends,
    CostEfficiency,
    PredictionAccuracy,
    OptimizationSavings,
}

/// Cost reporting configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostReportingConfig {
    /// Enable automated reports
    pub automated_reports: bool,
    /// Report frequency
    pub report_frequency: Duration,
    /// Report types
    pub report_types: Vec<ReportType>,
    /// Report recipients
    pub recipients: Vec<String>,
    /// Report format
    pub format: ReportFormat,
}

/// Types of cost reports
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ReportType {
    CostSummary,
    BudgetAnalysis,
    ProviderComparison,
    TrendAnalysis,
    OptimizationReport,
    ForecastReport,
    AnomalyReport,
}

/// Report formats
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ReportFormat {
    PDF,
    HTML,
    JSON,
    CSV,
    Excel,
}

/// Dashboard configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DashboardConfig {
    /// Enable real-time dashboard
    pub enabled: bool,
    /// Update frequency
    pub update_frequency: Duration,
    /// Dashboard widgets
    pub widgets: Vec<DashboardWidget>,
    /// Custom visualizations
    pub custom_visualizations: Vec<CustomVisualization>,
}

/// Dashboard widgets
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DashboardWidget {
    CostGauge,
    BudgetProgress,
    ProviderComparison,
    CostTrends,
    TopCostConsumers,
    OptimizationSavings,
    PredictionAccuracy,
}

/// Custom visualizations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CustomVisualization {
    /// Visualization name
    pub name: String,
    /// Chart type
    pub chart_type: ChartType,
    /// Data source
    pub data_source: String,
    /// Configuration parameters
    pub parameters: HashMap<String, serde_json::Value>,
}

/// Chart types for visualizations
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ChartType {
    LineChart,
    BarChart,
    PieChart,
    Histogram,
    ScatterPlot,
    Heatmap,
    TreeMap,
    Gauge,
}

/// Cost alert configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostAlertConfig {
    /// Enable alerts
    pub enabled: bool,
    /// Alert rules
    pub alert_rules: Vec<CostAlertRule>,
    /// Notification channels
    pub notification_channels: Vec<NotificationChannel>,
    /// Alert aggregation
    pub aggregation_config: AlertAggregationConfig,
}

/// Cost alert rules
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostAlertRule {
    /// Rule name
    pub name: String,
    /// Alert condition
    pub condition: AlertCondition,
    /// Severity level
    pub severity: AlertSeverity,
    /// Notification frequency
    pub frequency: NotificationFrequency,
    /// Enabled flag
    pub enabled: bool,
}

/// Alert conditions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AlertCondition {
    /// Budget threshold exceeded
    BudgetThreshold { threshold: f64, percentage: bool },
    /// Cost spike detected
    CostSpike { multiplier: f64, window: Duration },
    /// Prediction error high
    PredictionError { threshold: f64 },
    /// Optimization opportunity
    OptimizationOpportunity { savings_threshold: f64 },
    /// Custom condition
    Custom { expression: String },
}

/// Alert severity levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
pub enum AlertSeverity {
    Info,
    Warning,
    Error,
    Critical,
}

/// Notification frequencies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum NotificationFrequency {
    Immediate,
    Throttled(Duration),
    Daily,
    Weekly,
    Custom(Duration),
}

/// Notification channels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NotificationChannel {
    Email {
        recipients: Vec<String>,
        template: String,
    },
    Slack {
        webhook_url: String,
        channel: String,
    },
    Webhook {
        url: String,
        headers: HashMap<String, String>,
    },
    SMS {
        phone_numbers: Vec<String>,
    },
}

/// Alert aggregation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertAggregationConfig {
    /// Enable aggregation
    pub enabled: bool,
    /// Aggregation window
    pub window: Duration,
    /// Maximum alerts per window
    pub max_alerts_per_window: usize,
    /// Aggregation strategy
    pub strategy: AggregationStrategy,
}

/// Alert aggregation strategies
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AggregationStrategy {
    Count,
    SeverityBased,
    TypeBased,
    Custom(String),
}

impl Default for CostOptimizationConfig {
    fn default() -> Self {
        Self {
            budget_config: BudgetConfig {
                total_budget: 10000.0,
                daily_budget: Some(100.0),
                monthly_budget: Some(3000.0),
                provider_budgets: HashMap::new(),
                circuit_type_budgets: HashMap::new(),
                auto_budget_management: true,
                rollover_policy: BudgetRolloverPolicy::PercentageRollover(0.2),
            },
            estimation_config: CostEstimationConfig {
                provider_models: HashMap::new(),
                include_queue_time: true,
                include_overhead_costs: true,
                accuracy_target: 0.9,
                model_update_frequency: Duration::from_secs(3600),
                enable_ml_estimation: true,
                data_retention_period: Duration::from_secs(30 * 24 * 3600), // 30 days
            },
            optimization_strategy: CostOptimizationStrategy::MaximizeCostPerformance,
            provider_comparison: ProviderComparisonConfig {
                comparison_metrics: vec![
                    ComparisonMetric::TotalCost,
                    ComparisonMetric::QueueTime,
                    ComparisonMetric::Fidelity,
                ],
                normalization_method: NormalizationMethod::MinMax,
                metric_weights: HashMap::new(),
                real_time_comparison: true,
                update_frequency: Duration::from_secs(300),
                include_reliability: true,
            },
            predictive_modeling: PredictiveModelingConfig {
                enabled: true,
                prediction_horizon: Duration::from_secs(24 * 3600), // 24 hours
                model_types: vec![
                    PredictiveModelType::RandomForest,
                    PredictiveModelType::SciRS2Enhanced,
                ],
                feature_engineering: FeatureEngineeringConfig {
                    time_features: vec![TimeFeature::HourOfDay, TimeFeature::DayOfWeek],
                    circuit_features: vec![CircuitFeature::QubitCount, CircuitFeature::GateCount],
                    provider_features: vec![
                        ProviderFeature::QueueLength,
                        ProviderFeature::SystemLoad,
                    ],
                    usage_features: vec![
                        UsageFeature::HistoricalCosts,
                        UsageFeature::UsagePatterns,
                    ],
                    feature_selection: FeatureSelectionMethod::Correlation(0.1),
                },
                training_frequency: Duration::from_secs(24 * 3600),
                confidence_threshold: 0.8,
                enable_ensemble: true,
            },
            resource_optimization: ResourceOptimizationConfig {
                enabled: true,
                algorithms: vec![ResourceOptimizationAlgorithm::SciRS2Optimization],
                constraints: vec![],
                optimization_frequency: Duration::from_secs(3600),
                enable_parallel_optimization: true,
                multi_objective_config: MultiObjectiveConfig {
                    objectives: vec![
                        OptimizationObjective::MinimizeCost,
                        OptimizationObjective::MaximizeQuality,
                    ],
                    pareto_config: ParetoConfig {
                        max_solutions: 100,
                        convergence_criteria: ConvergenceCriteria {
                            max_iterations: 1000,
                            tolerance: 1e-6,
                            patience: 50,
                        },
                        diversity_preservation: true,
                    },
                    selection_strategy: SolutionSelectionStrategy::Weighted(
                        [
                            (OptimizationObjective::MinimizeCost, 0.6),
                            (OptimizationObjective::MaximizeQuality, 0.4),
                        ]
                        .iter()
                        .cloned()
                        .collect(),
                    ),
                },
            },
            monitoring_config: CostMonitoringConfig {
                real_time_monitoring: true,
                monitoring_frequency: Duration::from_secs(60),
                tracked_metrics: vec![
                    MonitoringMetric::TotalCost,
                    MonitoringMetric::BudgetUtilization,
                    MonitoringMetric::CostEfficiency,
                ],
                reporting_config: CostReportingConfig {
                    automated_reports: true,
                    report_frequency: Duration::from_secs(24 * 3600), // Daily
                    report_types: vec![ReportType::CostSummary, ReportType::BudgetAnalysis],
                    recipients: vec![],
                    format: ReportFormat::JSON,
                },
                dashboard_config: Some(DashboardConfig {
                    enabled: true,
                    update_frequency: Duration::from_secs(30),
                    widgets: vec![
                        DashboardWidget::CostGauge,
                        DashboardWidget::BudgetProgress,
                        DashboardWidget::ProviderComparison,
                    ],
                    custom_visualizations: vec![],
                }),
            },
            alert_config: CostAlertConfig {
                enabled: true,
                alert_rules: vec![CostAlertRule {
                    name: "Budget threshold".to_string(),
                    condition: AlertCondition::BudgetThreshold {
                        threshold: 80.0,
                        percentage: true,
                    },
                    severity: AlertSeverity::Warning,
                    frequency: NotificationFrequency::Immediate,
                    enabled: true,
                }],
                notification_channels: vec![],
                aggregation_config: AlertAggregationConfig {
                    enabled: true,
                    window: Duration::from_secs(300),
                    max_alerts_per_window: 5,
                    strategy: AggregationStrategy::SeverityBased,
                },
            },
        }
    }
}

/// Cost estimation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostEstimate {
    /// Total estimated cost
    pub total_cost: f64,
    /// Cost breakdown by component
    pub cost_breakdown: CostBreakdown,
    /// Confidence interval
    pub confidence_interval: (f64, f64),
    /// Estimation metadata
    pub metadata: CostEstimationMetadata,
}

/// Cost breakdown by components
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostBreakdown {
    /// Base execution cost
    pub execution_cost: f64,
    /// Queue time cost
    pub queue_cost: f64,
    /// Setup/teardown cost
    pub setup_cost: f64,
    /// Data transfer cost
    pub data_transfer_cost: f64,
    /// Storage cost
    pub storage_cost: f64,
    /// Additional fees
    pub additional_fees: HashMap<String, f64>,
}

/// Cost estimation metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostEstimationMetadata {
    /// Model used for estimation
    pub model_used: String,
    /// Estimation timestamp
    pub timestamp: SystemTime,
    /// Confidence level
    pub confidence_level: f64,
    /// Historical accuracy
    pub historical_accuracy: Option<f64>,
    /// Factors considered
    pub factors_considered: Vec<String>,
}

/// Provider comparison result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProviderComparisonResult {
    /// Comparison scores per provider
    pub provider_scores: HashMap<HardwareBackend, f64>,
    /// Detailed metrics per provider
    pub detailed_metrics: HashMap<HardwareBackend, ProviderMetrics>,
    /// Recommended provider
    pub recommended_provider: HardwareBackend,
    /// Comparison timestamp
    pub timestamp: SystemTime,
}

/// Detailed metrics for provider comparison
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProviderMetrics {
    /// Cost metrics
    pub cost_metrics: HashMap<ComparisonMetric, f64>,
    /// Performance metrics
    pub performance_metrics: HashMap<String, f64>,
    /// Reliability metrics
    pub reliability_metrics: HashMap<String, f64>,
    /// Overall score
    pub overall_score: f64,
}

/// Budget tracking information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BudgetStatus {
    /// Total budget
    pub total_budget: f64,
    /// Used budget
    pub used_budget: f64,
    /// Remaining budget
    pub remaining_budget: f64,
    /// Budget utilization percentage
    pub utilization_percentage: f64,
    /// Daily budget status
    pub daily_status: Option<DailyBudgetStatus>,
    /// Monthly budget status
    pub monthly_status: Option<MonthlyBudgetStatus>,
    /// Provider budget breakdown
    pub provider_breakdown: HashMap<HardwareBackend, f64>,
}

/// Daily budget status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DailyBudgetStatus {
    pub daily_budget: f64,
    pub daily_used: f64,
    pub daily_remaining: f64,
    pub projected_daily_usage: f64,
}

/// Monthly budget status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonthlyBudgetStatus {
    pub monthly_budget: f64,
    pub monthly_used: f64,
    pub monthly_remaining: f64,
    pub projected_monthly_usage: f64,
}

/// Main cost optimization engine
pub struct CostOptimizationEngine {
    config: CostOptimizationConfig,
    cost_estimator: Arc<RwLock<CostEstimator>>,
    budget_manager: Arc<RwLock<BudgetManager>>,
    provider_comparator: Arc<RwLock<ProviderComparator>>,
    predictive_modeler: Arc<RwLock<PredictiveModeler>>,
    resource_optimizer: Arc<RwLock<ResourceOptimizer>>,
    cost_monitor: Arc<RwLock<CostMonitor>>,
    alert_manager: Arc<RwLock<AlertManager>>,
    optimization_cache: Arc<RwLock<HashMap<String, CachedOptimization>>>,
}

/// Cost estimator component
pub struct CostEstimator {
    models: HashMap<HardwareBackend, CostModel>,
    historical_data: VecDeque<CostRecord>,
    ml_models: HashMap<String, Box<dyn MLCostModel + Send + Sync>>,
    estimation_cache: HashMap<String, CachedEstimate>,
}

/// Cost record for historical tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostRecord {
    pub provider: HardwareBackend,
    pub circuit_hash: String,
    pub actual_cost: f64,
    pub estimated_cost: f64,
    pub execution_time: Duration,
    pub timestamp: SystemTime,
    pub metadata: HashMap<String, String>,
}

/// Cached cost estimate
#[derive(Debug, Clone)]
struct CachedEstimate {
    estimate: CostEstimate,
    created_at: SystemTime,
    access_count: usize,
}

/// Machine learning cost model trait
pub trait MLCostModel {
    fn predict(&self, features: &Array1<f64>) -> DeviceResult<f64>;
    fn train(&mut self, features: &Array2<f64>, targets: &Array1<f64>) -> DeviceResult<()>;
    fn get_feature_importance(&self) -> DeviceResult<Array1<f64>>;
}

/// Budget manager component
pub struct BudgetManager {
    current_budget: BudgetStatus,
    budget_history: VecDeque<BudgetSnapshot>,
    spending_patterns: HashMap<String, SpendingPattern>,
    budget_alerts: Vec<ActiveBudgetAlert>,
}

/// Budget snapshot for historical tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
struct BudgetSnapshot {
    timestamp: SystemTime,
    budget_status: BudgetStatus,
    period_type: BudgetPeriod,
}

/// Budget periods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum BudgetPeriod {
    Daily,
    Weekly,
    Monthly,
    Quarterly,
    Yearly,
}

/// Spending patterns analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
struct SpendingPattern {
    pattern_type: PatternType,
    frequency: f64,
    amplitude: f64,
    trend: f64,
    seasonality: Option<SeasonalityPattern>,
}

/// Pattern types in spending
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum PatternType {
    Constant,
    Linear,
    Exponential,
    Periodic,
    Random,
    Composite,
}

/// Seasonality patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
struct SeasonalityPattern {
    period: Duration,
    phase: f64,
    strength: f64,
}

/// Active budget alert
#[derive(Debug, Clone)]
struct ActiveBudgetAlert {
    rule: CostAlertRule,
    triggered_at: SystemTime,
    last_notification: SystemTime,
    trigger_count: usize,
}

/// Provider comparator component
pub struct ProviderComparator {
    comparison_cache: HashMap<String, CachedComparison>,
    real_time_metrics: HashMap<HardwareBackend, RealTimeMetrics>,
    reliability_tracker: ReliabilityTracker,
}

/// Cached provider comparison
#[derive(Debug, Clone)]
struct CachedComparison {
    result: ProviderComparisonResult,
    created_at: SystemTime,
    cache_key: String,
}

/// Real-time metrics per provider
#[derive(Debug, Clone, Serialize, Deserialize)]
struct RealTimeMetrics {
    current_queue_length: usize,
    average_execution_time: Duration,
    current_error_rate: f64,
    availability_status: AvailabilityStatus,
    cost_fluctuation: f64,
    last_updated: SystemTime,
}

/// Availability status
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum AvailabilityStatus {
    Available,
    Busy,
    Maintenance,
    Unavailable,
}

/// Reliability tracker
pub struct ReliabilityTracker {
    provider_reliability: HashMap<HardwareBackend, ReliabilityMetrics>,
    incident_history: VecDeque<ReliabilityIncident>,
}

/// Reliability metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ReliabilityMetrics {
    uptime_percentage: f64,
    mean_time_between_failures: Duration,
    mean_time_to_recovery: Duration,
    error_rate_trend: f64,
    consistency_score: f64,
}

/// Reliability incident
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ReliabilityIncident {
    provider: HardwareBackend,
    incident_type: IncidentType,
    start_time: SystemTime,
    end_time: Option<SystemTime>,
    impact_severity: IncidentSeverity,
    description: String,
}

/// Types of reliability incidents
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum IncidentType {
    Outage,
    Degradation,
    Maintenance,
    ErrorSpike,
    QueueOverload,
}

/// Incident severity levels
#[derive(Debug, Clone, PartialEq, Eq, PartialOrd, Ord, Serialize, Deserialize)]
enum IncidentSeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Predictive modeler component
pub struct PredictiveModeler {
    models: HashMap<String, Box<dyn PredictiveModel + Send + Sync>>,
    feature_store: FeatureStore,
    model_performance: HashMap<String, ModelPerformance>,
    ensemble_config: EnsembleConfig,
}

/// Predictive model trait
pub trait PredictiveModel {
    fn predict(&self, features: &HashMap<String, f64>) -> DeviceResult<PredictionResult>;
    fn train(&mut self, training_data: &TrainingData) -> DeviceResult<TrainingResult>;
    fn get_feature_importance(&self) -> DeviceResult<HashMap<String, f64>>;
    fn cross_validate(
        &self,
        data: &TrainingData,
        folds: usize,
    ) -> DeviceResult<CrossValidationResult>;
}

/// Prediction result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PredictionResult {
    pub predicted_value: f64,
    pub confidence_interval: (f64, f64),
    pub feature_contributions: HashMap<String, f64>,
    pub model_used: String,
    pub prediction_timestamp: SystemTime,
}

/// Training data structure
#[derive(Debug, Clone)]
pub struct TrainingData {
    pub features: Array2<f64>,
    pub targets: Array1<f64>,
    pub feature_names: Vec<String>,
    pub timestamps: Vec<SystemTime>,
}

/// Training result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingResult {
    pub training_score: f64,
    pub validation_score: f64,
    pub feature_importance: HashMap<String, f64>,
    pub training_time: Duration,
    pub model_parameters: HashMap<String, f64>,
}

/// Cross-validation result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CrossValidationResult {
    pub mean_score: f64,
    pub std_score: f64,
    pub fold_scores: Vec<f64>,
    pub best_parameters: HashMap<String, f64>,
}

/// Feature store for predictive modeling
pub struct FeatureStore {
    features: HashMap<String, FeatureTimeSeries>,
    feature_metadata: HashMap<String, FeatureMetadata>,
    derived_features: HashMap<String, DerivedFeature>,
}

/// Time series data for features
#[derive(Debug, Clone)]
struct FeatureTimeSeries {
    values: VecDeque<(SystemTime, f64)>,
    statistics: FeatureStatistics,
}

/// Feature statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
struct FeatureStatistics {
    mean: f64,
    std: f64,
    min: f64,
    max: f64,
    trend: f64,
    autocorrelation: f64,
}

/// Feature metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
struct FeatureMetadata {
    name: String,
    description: String,
    data_type: FeatureDataType,
    update_frequency: Duration,
    importance_score: f64,
}

/// Feature data types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum FeatureDataType {
    Numerical,
    Categorical,
    Binary,
    TimeStamp,
    Text,
}

/// Derived feature definition
struct DerivedFeature {
    name: String,
    computation: Box<dyn Fn(&HashMap<String, f64>) -> f64 + Send + Sync>,
    dependencies: Vec<String>,
}

impl std::fmt::Debug for DerivedFeature {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("DerivedFeature")
            .field("name", &self.name)
            .field("computation", &"<function>")
            .field("dependencies", &self.dependencies)
            .finish()
    }
}

impl Clone for DerivedFeature {
    fn clone(&self) -> Self {
        // Note: computation cannot be cloned, create a placeholder
        Self {
            name: self.name.clone(),
            computation: Box::new(|_| 0.0),
            dependencies: self.dependencies.clone(),
        }
    }
}

/// Model performance tracking
#[derive(Debug, Clone, Serialize, Deserialize)]
struct ModelPerformance {
    accuracy_metrics: AccuracyMetrics,
    performance_over_time: VecDeque<(SystemTime, f64)>,
    prediction_distribution: PredictionDistribution,
    feature_drift: HashMap<String, f64>,
}

/// Accuracy metrics for models
#[derive(Debug, Clone, Serialize, Deserialize)]
struct AccuracyMetrics {
    mae: f64,      // Mean Absolute Error
    mse: f64,      // Mean Squared Error
    rmse: f64,     // Root Mean Squared Error
    mape: f64,     // Mean Absolute Percentage Error
    r2_score: f64, // R-squared
}

/// Prediction distribution analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
struct PredictionDistribution {
    histogram: Vec<(f64, usize)>,
    quantiles: HashMap<String, f64>,
    outlier_threshold: f64,
    outlier_count: usize,
}

/// Ensemble configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
struct EnsembleConfig {
    ensemble_method: EnsembleMethod,
    model_weights: HashMap<String, f64>,
    voting_strategy: VotingStrategy,
    diversity_threshold: f64,
}

/// Ensemble methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum EnsembleMethod {
    Voting,
    Averaging,
    Stacking,
    Boosting,
    Bagging,
}

/// Voting strategies for ensembles
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum VotingStrategy {
    Majority,
    Weighted,
    Confidence,
    Dynamic,
}

/// Resource optimizer component
pub struct ResourceOptimizer {
    optimization_algorithms: HashMap<String, Box<dyn OptimizationAlgorithm + Send + Sync>>,
    constraint_solver: ConstraintSolver,
    optimization_history: VecDeque<OptimizationResult>,
    pareto_frontiers: HashMap<String, ParetoFrontier>,
}

/// Optimization algorithm trait
pub trait OptimizationAlgorithm {
    fn optimize(&self, problem: &OptimizationProblem) -> DeviceResult<OptimizationResult>;
    fn get_algorithm_info(&self) -> AlgorithmInfo;
    fn set_parameters(&mut self, parameters: HashMap<String, f64>) -> DeviceResult<()>;
}

/// Optimization problem definition
#[derive(Debug, Clone)]
pub struct OptimizationProblem {
    pub objectives: Vec<ObjectiveFunction>,
    pub constraints: Vec<Constraint>,
    pub variables: Vec<Variable>,
    pub problem_type: ProblemType,
}

/// Objective function
pub struct ObjectiveFunction {
    pub name: String,
    pub function: Box<dyn Fn(&[f64]) -> f64 + Send + Sync>,
    pub optimization_direction: OptimizationDirection,
    pub weight: f64,
}

impl std::fmt::Debug for ObjectiveFunction {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("ObjectiveFunction")
            .field("name", &self.name)
            .field("function", &"<function>")
            .field("optimization_direction", &self.optimization_direction)
            .field("weight", &self.weight)
            .finish()
    }
}

impl Clone for ObjectiveFunction {
    fn clone(&self) -> Self {
        Self {
            name: self.name.clone(),
            function: Box::new(|_| 0.0),
            optimization_direction: self.optimization_direction.clone(),
            weight: self.weight,
        }
    }
}

/// Optimization directions
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationDirection {
    Minimize,
    Maximize,
}

/// Constraint definition
pub struct Constraint {
    pub name: String,
    pub constraint_function: Box<dyn Fn(&[f64]) -> f64 + Send + Sync>,
    pub constraint_type: ConstraintType,
    pub bound: f64,
}

impl std::fmt::Debug for Constraint {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("Constraint")
            .field("name", &self.name)
            .field("constraint_function", &"<function>")
            .field("constraint_type", &self.constraint_type)
            .field("bound", &self.bound)
            .finish()
    }
}

impl Clone for Constraint {
    fn clone(&self) -> Self {
        Self {
            name: self.name.clone(),
            constraint_function: Box::new(|_| 0.0),
            constraint_type: self.constraint_type.clone(),
            bound: self.bound,
        }
    }
}

/// Constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ConstraintType {
    Equality,
    Inequality,
    Box,
}

/// Variable definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Variable {
    pub name: String,
    pub variable_type: VariableType,
    pub bounds: (f64, f64),
    pub initial_value: Option<f64>,
}

/// Variable types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum VariableType {
    Continuous,
    Integer,
    Binary,
}

/// Problem types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ProblemType {
    LinearProgramming,
    QuadraticProgramming,
    NonlinearProgramming,
    IntegerProgramming,
    ConstraintSatisfaction,
    MultiObjective,
}

/// Optimization result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationResult {
    pub solution: Vec<f64>,
    pub objective_values: Vec<f64>,
    pub constraint_violations: Vec<f64>,
    pub optimization_status: OptimizationStatus,
    pub iterations: usize,
    pub execution_time: Duration,
    pub algorithm_used: String,
}

/// Optimization status
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OptimizationStatus {
    Optimal,
    Feasible,
    Infeasible,
    Unbounded,
    TimeLimit,
    IterationLimit,
    Error(String),
}

/// Algorithm information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlgorithmInfo {
    pub name: String,
    pub description: String,
    pub problem_types: Vec<ProblemType>,
    pub parameters: HashMap<String, ParameterInfo>,
}

/// Parameter information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParameterInfo {
    pub name: String,
    pub description: String,
    pub parameter_type: ParameterType,
    pub default_value: f64,
    pub bounds: Option<(f64, f64)>,
}

/// Parameter types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ParameterType {
    Real,
    Integer,
    Boolean,
    Categorical(Vec<String>),
}

/// Constraint solver
pub struct ConstraintSolver {
    solver_type: SolverType,
    tolerance: f64,
    max_iterations: usize,
}

/// Solver types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum SolverType {
    Simplex,
    InteriorPoint,
    ActiveSet,
    BarrierMethod,
    AugmentedLagrangian,
}

/// Pareto frontier for multi-objective optimization
#[derive(Debug, Clone)]
pub struct ParetoFrontier {
    solutions: Vec<ParetoSolution>,
    objectives: Vec<String>,
    generation: usize,
    last_updated: SystemTime,
}

/// Pareto solution
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParetoSolution {
    pub variables: Vec<f64>,
    pub objectives: Vec<f64>,
    pub dominance_count: usize,
    pub crowding_distance: f64,
}

/// Cost monitor component
pub struct CostMonitor {
    monitoring_metrics: HashMap<MonitoringMetric, MetricTimeSeries>,
    anomaly_detector: AnomalyDetector,
    trend_analyzer: TrendAnalyzer,
    dashboard_data: DashboardData,
}

/// Metric time series data
#[derive(Debug, Clone)]
struct MetricTimeSeries {
    data_points: VecDeque<(SystemTime, f64)>,
    sampling_frequency: Duration,
    aggregation_method: AggregationMethod,
    retention_period: Duration,
}

/// Aggregation methods for metrics
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum AggregationMethod {
    Average,
    Sum,
    Count,
    Min,
    Max,
    Median,
    Percentile(f64),
}

/// Anomaly detector
pub struct AnomalyDetector {
    detection_methods: Vec<AnomalyDetectionMethod>,
    anomaly_threshold: f64,
    detected_anomalies: VecDeque<Anomaly>,
}

/// Anomaly detection methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum AnomalyDetectionMethod {
    StatisticalOutlier,
    IsolationForest,
    OneClassSVM,
    LocalOutlierFactor,
    DBSCAN,
    TimeSeriesDecomposition,
}

/// Detected anomaly
#[derive(Debug, Clone, Serialize, Deserialize)]
struct Anomaly {
    metric: MonitoringMetric,
    timestamp: SystemTime,
    anomaly_score: f64,
    detected_value: f64,
    expected_value: f64,
    description: String,
}

/// Trend analyzer
pub struct TrendAnalyzer {
    trend_models: HashMap<MonitoringMetric, TrendModel>,
    trend_detection_sensitivity: f64,
    forecasting_horizon: Duration,
}

/// Trend model
#[derive(Debug, Clone)]
struct TrendModel {
    model_type: TrendModelType,
    parameters: HashMap<String, f64>,
    goodness_of_fit: f64,
    last_updated: SystemTime,
}

/// Trend model types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum TrendModelType {
    Linear,
    Exponential,
    Polynomial,
    Seasonal,
    ARIMA,
    ExponentialSmoothing,
}

/// Dashboard data structure
#[derive(Debug, Clone)]
struct DashboardData {
    widget_data: HashMap<DashboardWidget, serde_json::Value>,
    last_updated: SystemTime,
    update_frequency: Duration,
}

/// Alert manager component
pub struct AlertManager {
    active_alerts: HashMap<String, ActiveAlert>,
    alert_history: VecDeque<AlertHistoryEntry>,
    notification_handlers: HashMap<String, Box<dyn NotificationHandler + Send + Sync>>,
    escalation_policies: HashMap<AlertSeverity, EscalationPolicy>,
}

/// Active alert
#[derive(Debug, Clone)]
struct ActiveAlert {
    alert_id: String,
    rule: CostAlertRule,
    triggered_at: SystemTime,
    last_notification: SystemTime,
    escalation_level: usize,
    notification_count: usize,
    acknowledgment_status: AcknowledgmentStatus,
}

/// Acknowledgment status
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
enum AcknowledgmentStatus {
    Unacknowledged,
    Acknowledged { by: String, at: SystemTime },
    Resolved { by: String, at: SystemTime },
}

/// Alert history entry
#[derive(Debug, Clone, Serialize, Deserialize)]
struct AlertHistoryEntry {
    alert_id: String,
    rule_name: String,
    triggered_at: SystemTime,
    resolved_at: Option<SystemTime>,
    duration: Option<Duration>,
    severity: AlertSeverity,
    notification_count: usize,
}

/// Notification handler trait
pub trait NotificationHandler {
    fn send_notification(&self, alert: &ActiveAlert, message: &str) -> DeviceResult<()>;
    fn get_handler_info(&self) -> NotificationHandlerInfo;
}

/// Notification handler information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NotificationHandlerInfo {
    pub name: String,
    pub description: String,
    pub supported_formats: Vec<String>,
    pub delivery_guarantee: DeliveryGuarantee,
}

/// Delivery guarantees
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DeliveryGuarantee {
    BestEffort,
    AtLeastOnce,
    ExactlyOnce,
}

/// Escalation policy
#[derive(Debug, Clone, Serialize, Deserialize)]
struct EscalationPolicy {
    escalation_levels: Vec<EscalationLevel>,
    max_escalation_attempts: usize,
    escalation_timeout: Duration,
}

/// Escalation level
#[derive(Debug, Clone, Serialize, Deserialize)]
struct EscalationLevel {
    level: usize,
    notification_channels: Vec<String>,
    delay: Duration,
    repeat_frequency: Option<Duration>,
}

/// Cached optimization result
#[derive(Debug, Clone)]
struct CachedOptimization {
    result: OptimizationResult,
    input_hash: u64,
    created_at: SystemTime,
    access_count: usize,
    expiry_time: SystemTime,
}

impl CostOptimizationEngine {
    /// Create a new cost optimization engine
    pub fn new(config: CostOptimizationConfig) -> Self {
        Self {
            config: config.clone(),
            cost_estimator: Arc::new(RwLock::new(CostEstimator::new(&config.estimation_config))),
            budget_manager: Arc::new(RwLock::new(BudgetManager::new(&config.budget_config))),
            provider_comparator: Arc::new(RwLock::new(ProviderComparator::new(
                &config.provider_comparison,
            ))),
            predictive_modeler: Arc::new(RwLock::new(PredictiveModeler::new(
                &config.predictive_modeling,
            ))),
            resource_optimizer: Arc::new(RwLock::new(ResourceOptimizer::new(
                &config.resource_optimization,
            ))),
            cost_monitor: Arc::new(RwLock::new(CostMonitor::new(&config.monitoring_config))),
            alert_manager: Arc::new(RwLock::new(AlertManager::new(&config.alert_config))),
            optimization_cache: Arc::new(RwLock::new(HashMap::new())),
        }
    }

    /// Estimate cost for a circuit execution
    pub async fn estimate_cost<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        provider: HardwareBackend,
        shots: usize,
    ) -> DeviceResult<CostEstimate> {
        let mut estimator = self.cost_estimator.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on cost_estimator: {e}"
            ))
        })?;
        estimator.estimate_cost(circuit, provider, shots).await
    }

    /// Compare costs across providers
    pub async fn compare_providers<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        providers: Vec<HardwareBackend>,
        shots: usize,
    ) -> DeviceResult<ProviderComparisonResult> {
        let mut comparator = self.provider_comparator.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on provider_comparator: {e}"
            ))
        })?;
        comparator
            .compare_providers(circuit, providers, shots)
            .await
    }

    /// Optimize resource allocation for cost
    pub async fn optimize_resource_allocation(
        &self,
        requirements: &ResourceRequirements,
    ) -> DeviceResult<OptimizationResult> {
        let mut optimizer = self.resource_optimizer.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on resource_optimizer: {e}"
            ))
        })?;
        optimizer.optimize_allocation(requirements).await
    }

    /// Get current budget status
    pub async fn get_budget_status(&self) -> DeviceResult<BudgetStatus> {
        let budget_manager = self.budget_manager.read().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire read lock on budget_manager: {e}"
            ))
        })?;
        Ok(budget_manager.get_current_status())
    }

    /// Predict future costs
    pub async fn predict_costs(
        &self,
        prediction_horizon: Duration,
        features: HashMap<String, f64>,
    ) -> DeviceResult<PredictionResult> {
        let mut modeler = self.predictive_modeler.write().map_err(|e| {
            DeviceError::LockError(format!(
                "Failed to acquire write lock on predictive_modeler: {e}"
            ))
        })?;
        modeler.predict_costs(prediction_horizon, features).await
    }

    /// Get optimization recommendations
    pub async fn get_optimization_recommendations(
        &self,
        context: OptimizationContext,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        // Analyze current usage patterns
        let budget_status = self.get_budget_status().await?;
        let cost_trends = self.analyze_cost_trends().await?;

        // Generate recommendations based on analysis
        let recommendations = self
            .generate_recommendations(&budget_status, &cost_trends, &context)
            .await?;

        Ok(recommendations)
    }

    /// Monitor costs in real-time
    pub async fn start_cost_monitoring(&self) -> DeviceResult<()> {
        let monitor = self.cost_monitor.clone();
        let alert_manager = self.alert_manager.clone();

        tokio::spawn(async move {
            loop {
                // Update monitoring metrics
                {
                    if let Ok(mut monitor_guard) = monitor.write() {
                        // Note: update_metrics is not async in the current implementation
                        // monitor_guard.update_metrics().await;
                        // For now, we'll use a synchronous call
                        monitor_guard.update_metrics_sync();
                    }
                }

                // Check for alerts
                {
                    if let Ok(mut alert_guard) = alert_manager.write() {
                        // Note: check_and_trigger_alerts is not async in the current implementation
                        // alert_guard.check_and_trigger_alerts().await;
                        // For now, we'll use a synchronous call
                        alert_guard.check_and_trigger_alerts_sync();
                    }
                }

                tokio::time::sleep(Duration::from_secs(60)).await;
            }
        });

        Ok(())
    }

    // Helper methods for implementation...

    async fn analyze_cost_trends(&self) -> DeviceResult<CostTrends> {
        // Implementation for cost trend analysis
        Ok(CostTrends::default())
    }

    async fn generate_recommendations(
        &self,
        budget_status: &BudgetStatus,
        cost_trends: &CostTrends,
        context: &OptimizationContext,
    ) -> DeviceResult<Vec<OptimizationRecommendation>> {
        // Implementation for generating optimization recommendations
        Ok(vec![])
    }
}

/// Resource requirements for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResourceRequirements {
    pub circuits: Vec<CircuitRequirement>,
    pub budget_constraints: Vec<BudgetConstraint>,
    pub time_constraints: Vec<TimeConstraint>,
    pub quality_requirements: Vec<QualityRequirement>,
}

/// Circuit requirement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CircuitRequirement {
    pub circuit_id: String,
    pub qubit_count: usize,
    pub gate_count: usize,
    pub shots: usize,
    pub priority: JobPriority,
    pub deadline: Option<SystemTime>,
}

/// Budget constraint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BudgetConstraint {
    pub constraint_type: BudgetConstraintType,
    pub value: f64,
    pub scope: ConstraintScope,
}

/// Budget constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum BudgetConstraintType {
    MaxTotalCost,
    MaxCostPerCircuit,
    MaxCostPerProvider,
    CostPerformanceRatio,
}

/// Constraint scopes
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum ConstraintScope {
    Global,
    PerProvider,
    PerCircuit,
    PerTimeWindow(Duration),
}

/// Time constraint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TimeConstraint {
    pub constraint_type: TimeConstraintType,
    pub value: Duration,
    pub scope: ConstraintScope,
}

/// Time constraint types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum TimeConstraintType {
    MaxExecutionTime,
    MaxQueueTime,
    Deadline,
    PreferredWindow,
}

/// Quality requirement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QualityRequirement {
    pub requirement_type: QualityRequirementType,
    pub value: f64,
    pub scope: ConstraintScope,
}

/// Quality requirement types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum QualityRequirementType {
    MinFidelity,
    MaxErrorRate,
    MinSuccessRate,
    ConsistencyLevel,
}

/// Optimization context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationContext {
    pub user_preferences: UserPreferences,
    pub historical_patterns: HistoricalPatterns,
    pub current_workload: CurrentWorkload,
    pub market_conditions: MarketConditions,
}

/// User preferences for optimization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct UserPreferences {
    pub cost_sensitivity: f64, // 0.0 to 1.0
    pub time_sensitivity: f64,
    pub quality_sensitivity: f64,
    pub preferred_providers: Vec<HardwareBackend>,
    pub risk_tolerance: RiskTolerance,
}

/// Risk tolerance levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RiskTolerance {
    Conservative,
    Moderate,
    Aggressive,
    Custom(f64),
}

/// Historical usage patterns
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HistoricalPatterns {
    pub usage_frequency: HashMap<HardwareBackend, f64>,
    pub cost_patterns: HashMap<String, f64>,
    pub performance_history: HashMap<HardwareBackend, f64>,
    pub error_patterns: HashMap<String, f64>,
}

/// Current workload information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CurrentWorkload {
    pub pending_circuits: usize,
    pub queue_lengths: HashMap<HardwareBackend, usize>,
    pub resource_utilization: HashMap<HardwareBackend, f64>,
    pub estimated_completion_times: HashMap<HardwareBackend, Duration>,
}

/// Market conditions affecting costs
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MarketConditions {
    pub demand_levels: HashMap<HardwareBackend, DemandLevel>,
    pub pricing_trends: HashMap<HardwareBackend, PricingTrend>,
    pub capacity_utilization: HashMap<HardwareBackend, f64>,
    pub promotional_offers: Vec<PromotionalOffer>,
}

/// Demand levels
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DemandLevel {
    Low,
    Normal,
    High,
    Peak,
}

/// Pricing trends
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum PricingTrend {
    Decreasing,
    Stable,
    Increasing,
    Volatile,
}

/// Promotional offers
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PromotionalOffer {
    pub provider: HardwareBackend,
    pub offer_type: OfferType,
    pub discount_percentage: f64,
    pub valid_until: SystemTime,
    pub conditions: Vec<String>,
}

/// Offer types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum OfferType {
    VolumeDiscount,
    FirstTimeUser,
    LoyaltyDiscount,
    OffPeakPricing,
    BundleOffer,
}

/// Cost trends analysis
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct CostTrends {
    pub overall_trend: TrendDirection,
    pub provider_trends: HashMap<HardwareBackend, TrendDirection>,
    pub seasonal_patterns: Vec<SeasonalPattern>,
    pub anomalies: Vec<CostAnomaly>,
}

/// Trend directions
#[derive(Debug, Clone, PartialEq, Default, Serialize, Deserialize)]
pub enum TrendDirection {
    Increasing,
    Decreasing,
    #[default]
    Stable,
    Volatile,
}

/// Seasonal patterns in costs
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SeasonalPattern {
    pub pattern_name: String,
    pub period: Duration,
    pub amplitude: f64,
    pub phase_offset: Duration,
}

/// Cost anomalies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CostAnomaly {
    pub anomaly_type: AnomalyType,
    pub detected_at: SystemTime,
    pub severity: f64,
    pub description: String,
    pub affected_providers: Vec<HardwareBackend>,
}

/// Anomaly types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum AnomalyType {
    CostSpike,
    UnexpectedDiscount,
    ProviderOutage,
    QueueBottleneck,
    PerformanceDegradation,
}

/// Optimization recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationRecommendation {
    pub recommendation_type: RecommendationType,
    pub description: String,
    pub estimated_savings: f64,
    pub implementation_effort: ImplementationEffort,
    pub confidence_score: f64,
    pub action_items: Vec<ActionItem>,
}

/// Recommendation types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum RecommendationType {
    ProviderSwitch,
    TimingOptimization,
    BatchingOptimization,
    ResourceReallocation,
    BudgetAdjustment,
    QualityTradeoff,
}

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

/// Action items for implementing recommendations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ActionItem {
    pub description: String,
    pub priority: ActionPriority,
    pub estimated_time: Duration,
    pub required_resources: Vec<String>,
}

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

// Implementation stubs for component constructors
impl CostEstimator {
    fn new(_config: &CostEstimationConfig) -> Self {
        Self {
            models: HashMap::new(),
            historical_data: VecDeque::new(),
            ml_models: HashMap::new(),
            estimation_cache: HashMap::new(),
        }
    }

    async fn estimate_cost<const N: usize>(
        &mut self,
        _circuit: &Circuit<N>,
        _provider: HardwareBackend,
        _shots: usize,
    ) -> DeviceResult<CostEstimate> {
        // Placeholder implementation
        Ok(CostEstimate {
            total_cost: 10.0,
            cost_breakdown: CostBreakdown {
                execution_cost: 8.0,
                queue_cost: 1.0,
                setup_cost: 0.5,
                data_transfer_cost: 0.3,
                storage_cost: 0.2,
                additional_fees: HashMap::new(),
            },
            confidence_interval: (9.0, 11.0),
            metadata: CostEstimationMetadata {
                model_used: "linear".to_string(),
                timestamp: SystemTime::now(),
                confidence_level: 0.95,
                historical_accuracy: Some(0.92),
                factors_considered: vec!["shots".to_string(), "qubits".to_string()],
            },
        })
    }
}

impl BudgetManager {
    fn new(_config: &BudgetConfig) -> Self {
        Self {
            current_budget: BudgetStatus {
                total_budget: 10000.0,
                used_budget: 2500.0,
                remaining_budget: 7500.0,
                utilization_percentage: 25.0,
                daily_status: None,
                monthly_status: None,
                provider_breakdown: HashMap::new(),
            },
            budget_history: VecDeque::new(),
            spending_patterns: HashMap::new(),
            budget_alerts: Vec::new(),
        }
    }

    fn get_current_status(&self) -> BudgetStatus {
        self.current_budget.clone()
    }
}

impl ProviderComparator {
    fn new(_config: &ProviderComparisonConfig) -> Self {
        Self {
            comparison_cache: HashMap::new(),
            real_time_metrics: HashMap::new(),
            reliability_tracker: ReliabilityTracker {
                provider_reliability: HashMap::new(),
                incident_history: VecDeque::new(),
            },
        }
    }

    async fn compare_providers<const N: usize>(
        &mut self,
        _circuit: &Circuit<N>,
        providers: Vec<HardwareBackend>,
        _shots: usize,
    ) -> DeviceResult<ProviderComparisonResult> {
        // Placeholder implementation
        let mut provider_scores = HashMap::new();
        let mut detailed_metrics = HashMap::new();

        for provider in &providers {
            provider_scores.insert(*provider, 0.8);
            detailed_metrics.insert(
                *provider,
                ProviderMetrics {
                    cost_metrics: HashMap::new(),
                    performance_metrics: HashMap::new(),
                    reliability_metrics: HashMap::new(),
                    overall_score: 0.8,
                },
            );
        }

        Ok(ProviderComparisonResult {
            provider_scores,
            detailed_metrics,
            recommended_provider: providers[0],
            timestamp: SystemTime::now(),
        })
    }
}

impl PredictiveModeler {
    fn new(_config: &PredictiveModelingConfig) -> Self {
        Self {
            models: HashMap::new(),
            feature_store: FeatureStore {
                features: HashMap::new(),
                feature_metadata: HashMap::new(),
                derived_features: HashMap::new(),
            },
            model_performance: HashMap::new(),
            ensemble_config: EnsembleConfig {
                ensemble_method: EnsembleMethod::Averaging,
                model_weights: HashMap::new(),
                voting_strategy: VotingStrategy::Weighted,
                diversity_threshold: 0.1,
            },
        }
    }

    async fn predict_costs(
        &mut self,
        _prediction_horizon: Duration,
        _features: HashMap<String, f64>,
    ) -> DeviceResult<PredictionResult> {
        // Placeholder implementation
        Ok(PredictionResult {
            predicted_value: 15.0,
            confidence_interval: (12.0, 18.0),
            feature_contributions: HashMap::new(),
            model_used: "ensemble".to_string(),
            prediction_timestamp: SystemTime::now(),
        })
    }
}

impl ResourceOptimizer {
    fn new(_config: &ResourceOptimizationConfig) -> Self {
        Self {
            optimization_algorithms: HashMap::new(),
            constraint_solver: ConstraintSolver {
                solver_type: SolverType::InteriorPoint,
                tolerance: 1e-6,
                max_iterations: 1000,
            },
            optimization_history: VecDeque::new(),
            pareto_frontiers: HashMap::new(),
        }
    }

    async fn optimize_allocation(
        &mut self,
        _requirements: &ResourceRequirements,
    ) -> DeviceResult<OptimizationResult> {
        // Placeholder implementation
        Ok(OptimizationResult {
            solution: vec![0.8, 0.2],
            objective_values: vec![12.5],
            constraint_violations: vec![],
            optimization_status: OptimizationStatus::Optimal,
            iterations: 25,
            execution_time: Duration::from_millis(150),
            algorithm_used: "interior_point".to_string(),
        })
    }
}

impl CostMonitor {
    fn new(_config: &CostMonitoringConfig) -> Self {
        Self {
            monitoring_metrics: HashMap::new(),
            anomaly_detector: AnomalyDetector {
                detection_methods: vec![AnomalyDetectionMethod::StatisticalOutlier],
                anomaly_threshold: 2.0,
                detected_anomalies: VecDeque::new(),
            },
            trend_analyzer: TrendAnalyzer {
                trend_models: HashMap::new(),
                trend_detection_sensitivity: 0.1,
                forecasting_horizon: Duration::from_secs(24 * 3600),
            },
            dashboard_data: DashboardData {
                widget_data: HashMap::new(),
                last_updated: SystemTime::now(),
                update_frequency: Duration::from_secs(30),
            },
        }
    }

    async fn update_metrics(&mut self) {
        // Placeholder implementation for updating monitoring metrics
    }

    fn update_metrics_sync(&mut self) {
        // Placeholder implementation for updating monitoring metrics synchronously
    }
}

impl AlertManager {
    fn new(_config: &CostAlertConfig) -> Self {
        Self {
            active_alerts: HashMap::new(),
            alert_history: VecDeque::new(),
            notification_handlers: HashMap::new(),
            escalation_policies: HashMap::new(),
        }
    }

    async fn check_and_trigger_alerts(&mut self) {
        // Placeholder implementation for checking and triggering alerts
    }

    fn check_and_trigger_alerts_sync(&mut self) {
        // Placeholder implementation for checking and triggering alerts synchronously
    }
}

// Default implementation already exists in the struct definition

#[cfg(test)]
#[allow(clippy::pedantic, clippy::field_reassign_with_default)]
mod tests {
    use super::*;

    #[test]
    fn test_cost_optimization_config_default() {
        let config = CostOptimizationConfig::default();
        assert_eq!(config.budget_config.total_budget, 10000.0);
        assert!(config.estimation_config.enable_ml_estimation);
        assert_eq!(
            config.optimization_strategy,
            CostOptimizationStrategy::MaximizeCostPerformance
        );
    }

    #[test]
    fn test_budget_rollover_policy() {
        let policy = BudgetRolloverPolicy::PercentageRollover(0.2);
        match policy {
            BudgetRolloverPolicy::PercentageRollover(percentage) => {
                assert_eq!(percentage, 0.2);
            }
            _ => panic!("Expected PercentageRollover"),
        }
    }

    #[test]
    fn test_cost_model_creation() {
        let model = CostModel {
            model_type: CostModelType::Linear,
            base_cost_per_shot: 0.01,
            cost_per_qubit: 0.1,
            cost_per_gate: 0.001,
            cost_per_second: 0.1,
            setup_cost: 1.0,
            queue_time_multiplier: 0.1,
            time_based_pricing: None,
            volume_discounts: vec![],
            custom_factors: HashMap::new(),
        };

        assert_eq!(model.model_type, CostModelType::Linear);
        assert_eq!(model.base_cost_per_shot, 0.01);
    }

    #[tokio::test]
    async fn test_cost_optimization_engine_creation() {
        let config = CostOptimizationConfig::default();
        let _engine = CostOptimizationEngine::new(config);

        // Test passes if engine creates without error (no panic)
    }

    #[test]
    fn test_optimization_objectives() {
        let objectives = [
            OptimizationObjective::MinimizeCost,
            OptimizationObjective::MaximizeQuality,
            OptimizationObjective::MinimizeTime,
        ];

        assert_eq!(objectives.len(), 3);
        assert_eq!(objectives[0], OptimizationObjective::MinimizeCost);
    }
}