quantrs2_anneal/active_learning_decomposition/

problem_analysis.rs

//! Problem analysis components for active learning decomposition

use std::collections::HashMap;
use std::time::Duration;

use super::{
    BottleneckType, CommunityDetectionAlgorithm, ComplexityClass, ComplexityEstimate,
    ComplexityMetric, ComplexityModelType, ConstraintType, DecomposabilityScore,
    DecompositionAction, DecompositionRecommendation, DecompositionStrategy, DetectedCommunity,
    DetectedStructure, GraphMetrics, MetricComputationConfig, PathFindingAlgorithm,
    PatternMatchingAlgorithm, PatternType, RiskAssessment, RiskLevel, ScoringFunctionType,
    StructureType, WeightCalculationMethod,
};
use crate::ising::IsingModel;

/// Problem analyzer for decomposition
#[derive(Debug, Clone)]
pub struct ProblemAnalyzer {
    /// Graph analyzer
    pub graph_analyzer: GraphAnalyzer,
    /// Structure detector
    pub structure_detector: StructureDetector,
    /// Complexity estimator
    pub complexity_estimator: ComplexityEstimator,
    /// Decomposability scorer
    pub decomposability_scorer: DecomposabilityScorer,
}

impl ProblemAnalyzer {
    pub fn new() -> Result<Self, String> {
        Ok(Self {
            graph_analyzer: GraphAnalyzer::new(),
            structure_detector: StructureDetector::new(),
            complexity_estimator: ComplexityEstimator::new(),
            decomposability_scorer: DecomposabilityScorer::new(),
        })
    }
}

/// Graph analyzer for problem structure
#[derive(Debug, Clone)]
pub struct GraphAnalyzer {
    /// Graph metrics calculator
    pub metrics_calculator: GraphMetricsCalculator,
    /// Community detection algorithm
    pub community_detector: CommunityDetector,
    /// Critical path analyzer
    pub critical_path_analyzer: CriticalPathAnalyzer,
    /// Bottleneck detector
    pub bottleneck_detector: BottleneckDetector,
}

impl GraphAnalyzer {
    #[must_use]
    pub fn new() -> Self {
        Self {
            metrics_calculator: GraphMetricsCalculator::new(),
            community_detector: CommunityDetector::new(),
            critical_path_analyzer: CriticalPathAnalyzer::new(),
            bottleneck_detector: BottleneckDetector::new(),
        }
    }

    pub fn calculate_metrics(&mut self, problem: &IsingModel) -> Result<GraphMetrics, String> {
        let problem_key = format!("problem_{}", problem.num_qubits);

        if let Some(cached_metrics) = self.metrics_calculator.cached_metrics.get(&problem_key) {
            return Ok(cached_metrics.clone());
        }

        // Calculate metrics
        let num_vertices = problem.num_qubits;
        let mut num_edges = 0;

        for i in 0..problem.num_qubits {
            for j in (i + 1)..problem.num_qubits {
                if problem.get_coupling(i, j).unwrap_or(0.0).abs() > 1e-10 {
                    num_edges += 1;
                }
            }
        }

        let max_edges = num_vertices * (num_vertices - 1) / 2;
        let density = if max_edges > 0 {
            num_edges as f64 / max_edges as f64
        } else {
            0.0
        };

        let metrics = GraphMetrics {
            num_vertices,
            num_edges,
            density,
            clustering_coefficient: 0.0, // Would compute actual clustering coefficient
            avg_path_length: 0.0,        // Would compute actual average path length
            modularity: 0.0,             // Would compute actual modularity
            spectral_gap: 0.1,           // Simplified estimate
            treewidth_estimate: (num_vertices as f64).sqrt() as usize, // Rough estimate
        };

        self.metrics_calculator
            .cached_metrics
            .insert(problem_key, metrics.clone());
        Ok(metrics)
    }

    pub fn detect_communities(
        &mut self,
        problem: &IsingModel,
    ) -> Result<Vec<DetectedCommunity>, String> {
        // Simplified community detection - in practice would use sophisticated algorithms
        let n = problem.num_qubits;
        let community_size = (n as f64).sqrt() as usize;
        let mut communities = Vec::new();

        for i in (0..n).step_by(community_size) {
            let end = (i + community_size).min(n);
            let vertices: Vec<usize> = (i..end).collect();

            if vertices.len() >= 2 {
                communities.push(DetectedCommunity {
                    id: communities.len(),
                    vertices,
                    modularity: 0.5, // Simplified
                    internal_density: 0.7,
                    external_density: 0.2,
                });
            }
        }

        Ok(communities)
    }
}

/// Graph metrics calculator
#[derive(Debug, Clone)]
pub struct GraphMetricsCalculator {
    /// Cached metrics
    pub cached_metrics: HashMap<String, GraphMetrics>,
    /// Metric computation config
    pub computation_config: MetricComputationConfig,
}

impl GraphMetricsCalculator {
    #[must_use]
    pub fn new() -> Self {
        Self {
            cached_metrics: HashMap::new(),
            computation_config: MetricComputationConfig::default(),
        }
    }
}

/// Community detection
#[derive(Debug, Clone)]
pub struct CommunityDetector {
    /// Detection algorithm
    pub algorithm: CommunityDetectionAlgorithm,
    /// Resolution parameter
    pub resolution: f64,
    /// Minimum community size
    pub min_community_size: usize,
    /// Maximum community size
    pub max_community_size: usize,
}

impl CommunityDetector {
    #[must_use]
    pub const fn new() -> Self {
        Self {
            algorithm: CommunityDetectionAlgorithm::Louvain,
            resolution: 1.0,
            min_community_size: 2,
            max_community_size: 100,
        }
    }
}

/// Critical path analyzer
#[derive(Debug, Clone)]
pub struct CriticalPathAnalyzer {
    /// Path finding algorithm
    pub algorithm: PathFindingAlgorithm,
    /// Weight calculation method
    pub weight_method: WeightCalculationMethod,
    /// Critical path cache
    pub path_cache: HashMap<String, CriticalPath>,
}

impl CriticalPathAnalyzer {
    #[must_use]
    pub fn new() -> Self {
        Self {
            algorithm: PathFindingAlgorithm::Dijkstra,
            weight_method: WeightCalculationMethod::CouplingStrength,
            path_cache: HashMap::new(),
        }
    }
}

/// Critical path information
#[derive(Debug, Clone)]
pub struct CriticalPath {
    /// Path vertices
    pub vertices: Vec<usize>,
    /// Path weight
    pub weight: f64,
    /// Bottleneck edges
    pub bottleneck_edges: Vec<(usize, usize)>,
    /// Alternative paths
    pub alternative_paths: Vec<AlternativePath>,
}

/// Alternative path
#[derive(Debug, Clone)]
pub struct AlternativePath {
    /// Path vertices
    pub vertices: Vec<usize>,
    /// Path weight
    pub weight: f64,
    /// Overlap with critical path
    pub overlap_ratio: f64,
}

/// Bottleneck detector
#[derive(Debug, Clone)]
pub struct BottleneckDetector {
    /// Detection threshold
    pub detection_threshold: f64,
    /// Bottleneck types to detect
    pub bottleneck_types: Vec<BottleneckType>,
    /// Detected bottlenecks cache
    pub bottlenecks_cache: HashMap<String, Vec<Bottleneck>>,
}

impl BottleneckDetector {
    #[must_use]
    pub fn new() -> Self {
        Self {
            detection_threshold: 0.8,
            bottleneck_types: vec![
                BottleneckType::Vertex,
                BottleneckType::Edge,
                BottleneckType::CommunityBridge,
            ],
            bottlenecks_cache: HashMap::new(),
        }
    }
}

/// Bottleneck information
#[derive(Debug, Clone)]
pub struct Bottleneck {
    /// Bottleneck type
    pub bottleneck_type: BottleneckType,
    /// Affected vertices
    pub affected_vertices: Vec<usize>,
    /// Affected edges
    pub affected_edges: Vec<(usize, usize)>,
    /// Severity score
    pub severity: f64,
    /// Suggested decomposition action
    pub decomposition_action: DecompositionAction,
}

/// Structure detector for problem patterns
#[derive(Debug, Clone)]
pub struct StructureDetector {
    /// Pattern matching algorithms
    pub pattern_matchers: Vec<PatternMatcher>,
    /// Structure templates
    pub structure_templates: Vec<StructureTemplate>,
    /// Detection confidence threshold
    pub confidence_threshold: f64,
    /// Detected structures cache
    pub structures_cache: HashMap<String, Vec<DetectedStructure>>,
}

impl StructureDetector {
    #[must_use]
    pub fn new() -> Self {
        Self {
            pattern_matchers: Vec::new(),
            structure_templates: Vec::new(),
            confidence_threshold: 0.7,
            structures_cache: HashMap::new(),
        }
    }

    pub fn detect_structures(
        &mut self,
        problem: &IsingModel,
    ) -> Result<Vec<DetectedStructure>, String> {
        // Simplified structure detection
        let structures = vec![DetectedStructure {
            structure_type: StructureType::Random,
            vertices: (0..problem.num_qubits).collect(),
            confidence: 0.5,
            recommended_decomposition: DecompositionStrategy::GraphPartitioning,
        }];

        Ok(structures)
    }
}

/// Pattern matcher
#[derive(Debug, Clone)]
pub struct PatternMatcher {
    /// Pattern type
    pub pattern_type: PatternType,
    /// Matching algorithm
    pub algorithm: PatternMatchingAlgorithm,
    /// Matching parameters
    pub parameters: PatternMatchingParameters,
}

/// Pattern matching parameters
#[derive(Debug, Clone)]
pub struct PatternMatchingParameters {
    /// Matching tolerance
    pub tolerance: f64,
    /// Minimum pattern size
    pub min_pattern_size: usize,
    /// Maximum pattern size
    pub max_pattern_size: usize,
    /// Allow overlapping patterns
    pub allow_overlap: bool,
}

/// Structure template
#[derive(Debug, Clone)]
pub struct StructureTemplate {
    /// Template name
    pub name: String,
    /// Template graph
    pub template_graph: TemplateGraph,
    /// Decomposition strategy for this structure
    pub decomposition_strategy: DecompositionStrategy,
    /// Expected performance gain
    pub expected_gain: f64,
}

/// Template graph representation
#[derive(Debug, Clone)]
pub struct TemplateGraph {
    /// Template adjacency matrix
    pub adjacency_matrix: scirs2_core::ndarray::Array2<u8>,
    /// Template features
    pub features: scirs2_core::ndarray::Array1<f64>,
    /// Template constraints
    pub constraints: Vec<TemplateConstraint>,
}

/// Template constraints
#[derive(Debug, Clone)]
pub struct TemplateConstraint {
    /// Constraint type
    pub constraint_type: ConstraintType,
    /// Constraint parameters
    pub parameters: HashMap<String, f64>,
}

/// Complexity estimator
#[derive(Debug, Clone)]
pub struct ComplexityEstimator {
    /// Complexity metrics
    pub complexity_metrics: Vec<ComplexityMetric>,
    /// Estimation models
    pub estimation_models: HashMap<ComplexityMetric, ComplexityModel>,
    /// Complexity cache
    pub complexity_cache: HashMap<String, ComplexityEstimate>,
}

impl ComplexityEstimator {
    #[must_use]
    pub fn new() -> Self {
        Self {
            complexity_metrics: vec![
                ComplexityMetric::TimeComplexity,
                ComplexityMetric::SpaceComplexity,
            ],
            estimation_models: HashMap::new(),
            complexity_cache: HashMap::new(),
        }
    }

    pub fn estimate_complexity(
        &mut self,
        problem: &IsingModel,
    ) -> Result<ComplexityEstimate, String> {
        // Simplified complexity estimation
        let n = problem.num_qubits;
        let complexity_class = if n < 20 {
            ComplexityClass::P
        } else if n < 100 {
            ComplexityClass::NP
        } else {
            ComplexityClass::NPComplete
        };

        Ok(ComplexityEstimate {
            complexity_class,
            numeric_estimate: (n as f64).powi(2),
            confidence_interval: (n as f64, (n * n) as f64),
            estimation_method: "simplified".to_string(),
        })
    }
}

/// Complexity model
#[derive(Debug, Clone)]
pub struct ComplexityModel {
    /// Model type
    pub model_type: ComplexityModelType,
    /// Model parameters
    pub parameters: scirs2_core::ndarray::Array1<f64>,
    /// Prediction accuracy
    pub accuracy: f64,
}

/// Decomposability scorer
#[derive(Debug, Clone)]
pub struct DecomposabilityScorer {
    /// Scoring functions
    pub scoring_functions: Vec<ScoringFunction>,
    /// Score weights
    pub score_weights: scirs2_core::ndarray::Array1<f64>,
    /// Scoring cache
    pub scoring_cache: HashMap<String, DecomposabilityScore>,
}

impl DecomposabilityScorer {
    #[must_use]
    pub fn new() -> Self {
        Self {
            scoring_functions: vec![
                ScoringFunction {
                    function_type: ScoringFunctionType::Modularity,
                    parameters: HashMap::new(),
                    weight: 0.4,
                },
                ScoringFunction {
                    function_type: ScoringFunctionType::CutBased,
                    parameters: HashMap::new(),
                    weight: 0.3,
                },
                ScoringFunction {
                    function_type: ScoringFunctionType::BalanceBased,
                    parameters: HashMap::new(),
                    weight: 0.3,
                },
            ],
            score_weights: scirs2_core::ndarray::Array1::from_vec(vec![0.4, 0.3, 0.3]),
            scoring_cache: HashMap::new(),
        }
    }

    pub fn score_decomposability(
        &mut self,
        problem: &IsingModel,
    ) -> Result<DecomposabilityScore, String> {
        // Simplified decomposability scoring
        let n = problem.num_qubits;
        let overall_score = if n < 10 {
            0.2 // Small problems don't benefit much from decomposition
        } else if n < 50 {
            0.7 // Medium problems benefit significantly
        } else {
            0.9 // Large problems benefit greatly
        };

        let mut component_scores = HashMap::new();
        component_scores.insert("modularity".to_string(), overall_score * 0.8);
        component_scores.insert("cut_quality".to_string(), overall_score * 0.9);
        component_scores.insert("balance".to_string(), overall_score * 0.7);

        let recommendation = DecompositionRecommendation {
            strategy: if n < 10 {
                DecompositionStrategy::NoDecomposition
            } else {
                DecompositionStrategy::GraphPartitioning
            },
            cut_points: Vec::new(),
            expected_benefit: overall_score,
            risk_assessment: RiskAssessment {
                risk_level: RiskLevel::Low,
                risk_factors: Vec::new(),
                mitigation_strategies: Vec::new(),
            },
        };

        Ok(DecomposabilityScore {
            overall_score,
            component_scores,
            recommendation,
            confidence: 0.8,
        })
    }
}

/// Scoring function
#[derive(Debug, Clone)]
pub struct ScoringFunction {
    /// Function type
    pub function_type: ScoringFunctionType,
    /// Function parameters
    pub parameters: HashMap<String, f64>,
    /// Function weight
    pub weight: f64,
}