quantrs2-device 0.1.3

//! Algorithm Optimization Engine
//!
//! This module provides optimization services for quantum algorithms including
//! performance tuning, resource optimization, and adaptive parameter selection.

use super::*;

/// Algorithm optimization engine
pub struct AlgorithmOptimizationEngine {
    config: OptimizationConfig,
    optimization_strategies: Vec<Box<dyn OptimizationStrategy + Send + Sync>>,
    performance_analyzer: PerformanceAnalyzer,
    parameter_optimizer: ParameterOptimizer,
    resource_optimizer: ResourceOptimizer,
}

/// Optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationConfig {
    pub enabled: bool,
    pub optimization_level: OptimizationLevel,
    pub target_metrics: Vec<String>,
    pub optimization_budget: OptimizationBudget,
    pub parallel_optimization: bool,
    pub caching_enabled: bool,
}

/// Optimization levels
#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
pub enum OptimizationLevel {
    None,
    Basic,
    Aggressive,
    Experimental,
    Custom(String),
}

/// Optimization budget
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OptimizationBudget {
    pub max_iterations: usize,
    pub max_time: Duration,
    pub max_cost: f64,
    pub max_resource_usage: f64,
}

/// Optimization strategy trait
pub trait OptimizationStrategy {
    fn optimize(
        &self,
        algorithm: &AlgorithmOptimizationContext,
    ) -> DeviceResult<OptimizationResult>;
    fn get_strategy_name(&self) -> String;
    fn supports_algorithm_type(&self, algorithm_type: &AlgorithmCategory) -> bool;
}

/// Algorithm optimization context
#[derive(Debug, Clone)]
pub struct AlgorithmOptimizationContext {
    pub algorithm_id: String,
    pub algorithm_type: AlgorithmCategory,
    pub current_parameters: HashMap<String, f64>,
    pub performance_history: Vec<PerformanceSnapshot>,
    pub target_platform: String,
    pub constraints: OptimizationConstraints,
    pub objectives: Vec<OptimizationObjective>,
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot {
    pub timestamp: SystemTime,
    pub parameters: HashMap<String, f64>,
    pub metrics: HashMap<String, f64>,
    pub resource_usage: ResourceUsage,
    pub execution_context: ExecutionContext,
}

/// Execution context
#[derive(Debug, Clone)]
pub struct ExecutionContext {
    pub platform: String,
    pub hardware_specs: HashMap<String, String>,
    pub problem_size: usize,
    pub noise_level: f64,
    pub environmental_conditions: HashMap<String, f64>,
}

/// Optimization constraints
#[derive(Debug, Clone)]
pub struct OptimizationConstraints {
    pub parameter_bounds: HashMap<String, (f64, f64)>,
    pub resource_limits: ResourceLimits,
    pub performance_requirements: PerformanceRequirements,
    pub platform_constraints: Vec<String>,
    pub time_constraints: Duration,
}

/// Optimization objectives
#[derive(Debug, Clone)]
pub struct OptimizationObjective {
    pub metric_name: String,
    pub objective_type: ObjectiveType,
    pub weight: f64,
    pub target_value: Option<f64>,
}

/// Objective types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ObjectiveType {
    Minimize,
    Maximize,
    Target,
    Constraint,
}

/// Optimization result
#[derive(Debug, Clone)]
pub struct OptimizationResult {
    pub success: bool,
    pub optimized_parameters: HashMap<String, f64>,
    pub performance_improvement: f64,
    pub iterations_used: usize,
    pub time_taken: Duration,
    pub cost_incurred: f64,
    pub convergence_info: ConvergenceInfo,
    pub recommendations: Vec<OptimizationRecommendation>,
}

/// Convergence information
#[derive(Debug, Clone)]
pub struct ConvergenceInfo {
    pub converged: bool,
    pub final_objective_value: f64,
    pub improvement_rate: f64,
    pub convergence_criteria_met: Vec<String>,
    pub optimization_trajectory: Vec<IterationSnapshot>,
}

/// Iteration snapshot
#[derive(Debug, Clone)]
pub struct IterationSnapshot {
    pub iteration: usize,
    pub parameters: HashMap<String, f64>,
    pub objective_value: f64,
    pub constraint_violations: Vec<ConstraintViolation>,
    pub step_size: f64,
}

/// Constraint violation
#[derive(Debug, Clone)]
pub struct ConstraintViolation {
    pub constraint_name: String,
    pub violation_magnitude: f64,
    pub tolerance: f64,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub recommendation_type: RecommendationType,
    pub description: String,
    pub expected_benefit: f64,
    pub implementation_effort: ImplementationEffort,
    pub confidence: f64,
}

/// Recommendation types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum RecommendationType {
    ParameterAdjustment,
    AlgorithmVariant,
    PlatformChange,
    ResourceReallocation,
    PreprocessingStep,
    PostprocessingStep,
}

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

/// Performance analyzer
pub struct PerformanceAnalyzer {
    analysis_methods: Vec<Box<dyn PerformanceAnalysisMethod + Send + Sync>>,
    benchmark_database: BenchmarkDatabase,
    performance_models: Vec<PerformanceModel>,
}

/// Performance analysis method trait
pub trait PerformanceAnalysisMethod {
    fn analyze(&self, data: &[PerformanceSnapshot]) -> DeviceResult<PerformanceAnalysis>;
    fn get_method_name(&self) -> String;
}

/// Performance analysis
#[derive(Debug, Clone)]
pub struct PerformanceAnalysis {
    pub bottlenecks: Vec<PerformanceBottleneck>,
    pub trends: Vec<PerformanceTrend>,
    pub anomalies: Vec<PerformanceAnomaly>,
    pub suggestions: Vec<PerformanceSuggestion>,
}

/// Performance bottleneck
#[derive(Debug, Clone)]
pub struct PerformanceBottleneck {
    pub bottleneck_type: BottleneckType,
    pub severity: f64,
    pub affected_metrics: Vec<String>,
    pub root_cause: String,
    pub resolution_suggestions: Vec<String>,
}

/// Bottleneck types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum BottleneckType {
    ComputeBottleneck,
    MemoryBottleneck,
    NetworkBottleneck,
    QuantumResourceBottleneck,
    AlgorithmicBottleneck,
}

/// Performance trend
#[derive(Debug, Clone)]
pub struct PerformanceTrend {
    pub metric: String,
    pub trend_direction: TrendDirection,
    pub trend_strength: f64,
    pub statistical_significance: f64,
    pub projected_evolution: Vec<(Duration, f64)>,
}

/// Performance anomaly
#[derive(Debug, Clone)]
pub struct PerformanceAnomaly {
    pub anomaly_type: AnomalyType,
    pub detection_time: SystemTime,
    pub severity: f64,
    pub affected_metrics: Vec<String>,
    pub potential_causes: Vec<String>,
}

/// Anomaly types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AnomalyType {
    SuddenDegradation,
    GradualDecline,
    UnexpectedImprovement,
    PeriodicFluctuation,
    OutlierBehavior,
}

/// Performance suggestion
#[derive(Debug, Clone)]
pub struct PerformanceSuggestion {
    pub suggestion_type: SuggestionType,
    pub description: String,
    pub expected_improvement: f64,
    pub implementation_complexity: f64,
    pub confidence: f64,
}

/// Suggestion types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum SuggestionType {
    ParameterTuning,
    AlgorithmModification,
    ResourceReallocation,
    PlatformOptimization,
    WorkflowImprovement,
}

/// Benchmark database
pub struct BenchmarkDatabase {
    benchmarks: HashMap<String, AlgorithmBenchmark>,
    performance_baselines: HashMap<String, PerformanceBaseline>,
    comparison_metrics: Vec<ComparisonMetric>,
}

/// Algorithm benchmark
#[derive(Debug, Clone)]
pub struct AlgorithmBenchmark {
    pub benchmark_id: String,
    pub algorithm_type: AlgorithmCategory,
    pub problem_instances: Vec<ProblemInstance>,
    pub reference_implementations: Vec<ReferenceImplementation>,
    pub performance_targets: HashMap<String, f64>,
}

/// Problem instance
#[derive(Debug, Clone)]
pub struct ProblemInstance {
    pub instance_id: String,
    pub problem_size: usize,
    pub difficulty_level: DifficultyLevel,
    pub instance_characteristics: HashMap<String, f64>,
    pub optimal_solution: Option<Solution>,
}

/// Difficulty levels
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DifficultyLevel {
    Trivial,
    Easy,
    Medium,
    Hard,
    Extreme,
}

/// Solution
#[derive(Debug, Clone)]
pub struct Solution {
    pub solution_quality: f64,
    pub computation_time: Duration,
    pub resource_requirements: ResourceRequirements,
    pub verification_status: bool,
}

/// Reference implementation
#[derive(Debug, Clone)]
pub struct ReferenceImplementation {
    pub implementation_id: String,
    pub platform: String,
    pub algorithm_variant: String,
    pub performance_characteristics: HashMap<String, f64>,
    pub implementation_notes: String,
}

/// Performance baseline
#[derive(Debug, Clone)]
pub struct PerformanceBaseline {
    pub baseline_id: String,
    pub algorithm_type: AlgorithmCategory,
    pub platform: String,
    pub baseline_metrics: HashMap<String, f64>,
    pub confidence_intervals: HashMap<String, (f64, f64)>,
    pub measurement_conditions: MeasurementConditions,
}

/// Measurement conditions
#[derive(Debug, Clone)]
pub struct MeasurementConditions {
    pub measurement_date: SystemTime,
    pub hardware_configuration: HashMap<String, String>,
    pub software_versions: HashMap<String, String>,
    pub environmental_factors: HashMap<String, f64>,
}

/// Comparison metric
#[derive(Debug, Clone)]
pub struct ComparisonMetric {
    pub metric_name: String,
    pub metric_type: MetricType,
    pub normalization_method: NormalizationMethod,
    pub aggregation_method: AggregationMethod,
}

/// Normalization methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum NormalizationMethod {
    None,
    ZScore,
    MinMax,
    Percentile,
    Custom(String),
}

/// Aggregation methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum AggregationMethod {
    Mean,
    Median,
    WeightedAverage,
    GeometricMean,
    HarmonicMean,
}

/// Performance model
#[derive(Debug, Clone)]
pub struct PerformanceModel {
    pub model_id: String,
    pub model_type: PerformanceModelType,
    pub input_features: Vec<String>,
    pub output_metrics: Vec<String>,
    pub model_parameters: Vec<f64>,
    pub accuracy_metrics: HashMap<String, f64>,
    pub training_data: ModelTrainingData,
}

/// Performance model types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum PerformanceModelType {
    LinearRegression,
    PolynomialRegression,
    RandomForest,
    NeuralNetwork,
    GaussianProcess,
    Custom(String),
}

/// Model training data
#[derive(Debug, Clone)]
pub struct ModelTrainingData {
    pub training_set_size: usize,
    pub validation_set_size: usize,
    pub feature_ranges: HashMap<String, (f64, f64)>,
    pub training_time: Duration,
    pub last_updated: SystemTime,
}

/// Parameter optimizer
pub struct ParameterOptimizer {
    optimization_algorithms: Vec<Box<dyn ParameterOptimizationAlgorithm + Send + Sync>>,
    parameter_space: ParameterSpace,
    optimization_history: Vec<OptimizationRun>,
}

/// Parameter optimization algorithm trait
pub trait ParameterOptimizationAlgorithm {
    fn optimize(
        &self,
        objective: &ObjectiveFunction,
        space: &ParameterSpace,
        budget: &OptimizationBudget,
    ) -> DeviceResult<OptimizationResult>;
    fn get_algorithm_name(&self) -> String;
    fn supports_constraints(&self) -> bool;
}

/// Objective function
pub struct ObjectiveFunction {
    pub function_type: ObjectiveFunctionType,
    pub evaluation_method: EvaluationMethod,
    pub noise_level: f64,
    pub computational_cost: ComputationalCost,
}

/// Objective function types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ObjectiveFunctionType {
    SingleObjective,
    MultiObjective,
    ConstrainedOptimization,
    RobustOptimization,
}

/// Evaluation methods
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum EvaluationMethod {
    DirectEvaluation,
    SimulationBased,
    ModelBased,
    HybridApproach,
}

/// Computational cost
#[derive(Debug, Clone)]
pub struct ComputationalCost {
    pub cost_per_evaluation: f64,
    pub evaluation_time: Duration,
    pub resource_requirements: ResourceRequirements,
    pub parallelization_factor: f64,
}

/// Parameter space
#[derive(Debug, Clone)]
pub struct ParameterSpace {
    pub dimensions: Vec<ParameterDimension>,
    pub constraints: Vec<ParameterConstraint>,
    pub prior_knowledge: Option<PriorKnowledge>,
}

/// Parameter dimension
#[derive(Debug, Clone)]
pub struct ParameterDimension {
    pub name: String,
    pub dimension_type: DimensionType,
    pub bounds: ParameterBounds,
    pub initial_value: Option<f64>,
    pub importance: f64,
}

/// Dimension types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DimensionType {
    Continuous,
    Discrete,
    Categorical,
    Ordinal,
}

/// Parameter bounds
#[derive(Debug, Clone)]
pub enum ParameterBounds {
    Continuous(f64, f64),
    Discrete(Vec<i64>),
    Categorical(Vec<String>),
    Ordinal(Vec<String>),
}

/// Parameter constraint
#[derive(Debug, Clone)]
pub struct ParameterConstraint {
    pub constraint_type: ConstraintType,
    pub parameters: Vec<String>,
    pub constraint_function: String,
    pub tolerance: f64,
}

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

/// Prior knowledge
#[derive(Debug, Clone)]
pub struct PriorKnowledge {
    pub known_good_regions: Vec<ParameterRegion>,
    pub known_bad_regions: Vec<ParameterRegion>,
    pub parameter_correlations: HashMap<(String, String), f64>,
    pub sensitivity_information: HashMap<String, f64>,
}

/// Parameter region
#[derive(Debug, Clone)]
pub struct ParameterRegion {
    pub region_bounds: HashMap<String, (f64, f64)>,
    pub quality_estimate: f64,
    pub confidence: f64,
}

/// Optimization run
#[derive(Debug, Clone)]
pub struct OptimizationRun {
    pub run_id: String,
    pub algorithm_used: String,
    pub start_time: SystemTime,
    pub end_time: SystemTime,
    pub iterations: usize,
    pub best_parameters: HashMap<String, f64>,
    pub best_objective_value: f64,
    pub convergence_history: Vec<ConvergencePoint>,
}

/// Convergence point
#[derive(Debug, Clone)]
pub struct ConvergencePoint {
    pub iteration: usize,
    pub objective_value: f64,
    pub parameters: HashMap<String, f64>,
    pub improvement: f64,
}

/// Resource optimizer
pub struct ResourceOptimizer {
    resource_allocation_strategies: Vec<Box<dyn ResourceAllocationStrategy + Send + Sync>>,
    resource_models: Vec<ResourceModel>,
    cost_models: Vec<CostModel>,
}

/// Resource allocation strategy trait
pub trait ResourceAllocationStrategy {
    fn allocate(
        &self,
        requirements: &ResourceRequirements,
        available: &[AvailableResources],
    ) -> DeviceResult<ResourceAllocation>;
    fn get_strategy_name(&self) -> String;
}

/// Resource model
#[derive(Debug, Clone)]
pub struct ResourceModel {
    pub model_id: String,
    pub resource_type: String,
    pub utilization_model: UtilizationModel,
    pub performance_impact: PerformanceImpactModel,
    pub cost_model: ResourceCostModel,
}

/// Utilization model
#[derive(Debug, Clone)]
pub struct UtilizationModel {
    pub base_utilization: f64,
    pub scaling_factors: HashMap<String, f64>,
    pub utilization_bounds: (f64, f64),
    pub efficiency_curve: Vec<(f64, f64)>,
}

/// Performance impact model
#[derive(Debug, Clone)]
pub struct PerformanceImpactModel {
    pub impact_metrics: HashMap<String, f64>,
    pub bottleneck_thresholds: HashMap<String, f64>,
    pub scaling_relationships: Vec<ScalingRelationship>,
}

/// Scaling relationship
#[derive(Debug, Clone)]
pub struct ScalingRelationship {
    pub input_resource: String,
    pub output_metric: String,
    pub relationship_type: RelationshipType,
    pub parameters: Vec<f64>,
}

/// Relationship types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum RelationshipType {
    Linear,
    Logarithmic,
    Exponential,
    Power,
    Custom(String),
}

/// Resource cost model
#[derive(Debug, Clone)]
pub struct ResourceCostModel {
    pub cost_structure: CostStructure,
    pub pricing_tiers: Vec<PricingTier>,
    pub discount_factors: HashMap<String, f64>,
    pub cost_optimization_rules: Vec<CostOptimizationRule>,
}

/// Cost structure
#[derive(Debug, Clone)]
pub struct CostStructure {
    pub fixed_costs: f64,
    pub variable_costs: HashMap<String, f64>,
    pub tiered_pricing: bool,
    pub volume_discounts: bool,
}

/// Pricing tier
#[derive(Debug, Clone)]
pub struct PricingTier {
    pub tier_name: String,
    pub usage_threshold: f64,
    pub price_per_unit: f64,
    pub included_quota: f64,
}

/// Cost optimization rule
#[derive(Debug, Clone)]
pub struct CostOptimizationRule {
    pub rule_name: String,
    pub condition: String,
    pub action: String,
    pub expected_savings: f64,
}

/// Cost model
#[derive(Debug, Clone)]
pub struct CostModel {
    pub model_id: String,
    pub cost_components: Vec<CostComponent>,
    pub optimization_objectives: Vec<CostOptimizationObjective>,
    pub budget_constraints: BudgetConstraints,
}

/// Cost component
#[derive(Debug, Clone)]
pub struct CostComponent {
    pub component_name: String,
    pub cost_type: CostType,
    pub cost_function: String,
    pub parameters: HashMap<String, f64>,
}

/// Cost types
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum CostType {
    Fixed,
    Variable,
    StepFunction,
    Tiered,
    UsageBased,
}

/// Cost optimization objective
#[derive(Debug, Clone)]
pub struct CostOptimizationObjective {
    pub objective_name: String,
    pub target_metric: String,
    pub optimization_direction: ObjectiveType,
    pub weight: f64,
}

/// Budget constraints
#[derive(Debug, Clone)]
pub struct BudgetConstraints {
    pub total_budget: f64,
    pub time_horizon: Duration,
    pub budget_allocation: HashMap<String, f64>,
    pub cost_limits: HashMap<String, f64>,
}

impl AlgorithmOptimizationEngine {
    /// Create a new optimization engine
    pub fn new(config: &OptimizationConfig) -> DeviceResult<Self> {
        Ok(Self {
            config: config.clone(),
            optimization_strategies: vec![],
            performance_analyzer: PerformanceAnalyzer::new()?,
            parameter_optimizer: ParameterOptimizer::new()?,
            resource_optimizer: ResourceOptimizer::new()?,
        })
    }

    /// Initialize the optimization engine
    pub async fn initialize(&self) -> DeviceResult<()> {
        // Initialize optimization components
        Ok(())
    }
}

impl PerformanceAnalyzer {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            analysis_methods: vec![],
            benchmark_database: BenchmarkDatabase::new(),
            performance_models: vec![],
        })
    }
}

impl BenchmarkDatabase {
    fn new() -> Self {
        Self {
            benchmarks: HashMap::new(),
            performance_baselines: HashMap::new(),
            comparison_metrics: vec![],
        }
    }
}

impl ParameterOptimizer {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            optimization_algorithms: vec![],
            parameter_space: ParameterSpace {
                dimensions: vec![],
                constraints: vec![],
                prior_knowledge: None,
            },
            optimization_history: vec![],
        })
    }
}

impl ResourceOptimizer {
    fn new() -> DeviceResult<Self> {
        Ok(Self {
            resource_allocation_strategies: vec![],
            resource_models: vec![],
            cost_models: vec![],
        })
    }
}

impl Default for OptimizationConfig {
    fn default() -> Self {
        Self {
            enabled: true,
            optimization_level: OptimizationLevel::Basic,
            target_metrics: vec![
                "execution_time".to_string(),
                "resource_efficiency".to_string(),
                "accuracy".to_string(),
            ],
            optimization_budget: OptimizationBudget {
                max_iterations: 100,
                max_time: Duration::from_secs(3600),
                max_cost: 1000.0,
                max_resource_usage: 0.8,
            },
            parallel_optimization: true,
            caching_enabled: true,
        }
    }
}