//! Error Detection and Syndrome Analysis Systems //! //! This module contains error syndrome detection, pattern recognition, //! and alerting systems for quantum error correction. use crate::error::{AiContextError, AiContextResult}; use super::error_correction_core::{ErrorCorrectionConfig, QuantumErrorType, ErrorLocation, CorrectionRecommendation, CorrectionOperation, CorrectionType}; use serde::{Deserialize, Serialize}; use std::collections::HashMap; use std::sync::Arc; use tokio::sync::RwLock; use tokio::time::{Duration, Instant}; use tracing::{debug, info, trace}; /// Error syndrome detection result #[derive(Debug, Clone, Serialize, Deserialize)] pub struct ErrorSyndrome { /// Syndrome identifier pub syndrome_id: String, /// Detection timestamp pub detected_at: crate::temporal::TemporalTimestamp, /// Syndrome pattern pub pattern: SyndromePattern, /// Error type classification pub error_type: QuantumErrorType, /// Error location pub location: ErrorLocation, /// Syndrome confidence pub confidence: f64, /// Correction recommendation pub correction: CorrectionRecommendation, } /// Syndrome pattern for error detection #[derive(Debug, Clone, Serialize, Deserialize)] pub struct SyndromePattern { /// Pattern bits pub bits: Vec, /// Pattern weight pub weight: u32, /// Pattern parity pub parity: u8, /// Stabilizer measurements pub stabilizer_outcomes: Vec, } /// Stabilizer measurement outcome #[derive(Debug, Clone, Serialize, Deserialize)] pub struct StabilizerOutcome { /// Stabilizer identifier pub stabilizer_id: String, /// Measurement result pub measurement: u8, // 0 or 1 /// Measurement confidence pub confidence: f64, /// Associated qubits pub qubits: Vec, } /// Quantum parity check for error detection #[derive(Debug, Clone, Serialize, Deserialize)] pub struct QuantumParityCheck { /// Check identifier pub check_id: String, /// Parity check matrix pub check_matrix: ParityCheckMatrix, /// Check qubits pub check_qubits: Vec, /// Data qubits pub data_qubits: Vec, /// Check frequency pub frequency: f64, /// Detection efficiency pub efficiency: f64, } /// Parity check matrix #[derive(Debug, Clone, Serialize, Deserialize)] pub struct ParityCheckMatrix { /// Matrix dimensions pub dimensions: (usize, usize), /// Matrix elements pub elements: Vec>, /// Matrix rank pub rank: usize, /// Null space dimension pub null_space_dim: usize, } /// Error syndrome detector #[derive(Debug)] pub struct ErrorSyndromeDetector { /// Detection algorithms algorithms: Vec, /// Syndrome database syndrome_db: Arc>, /// Real-time detector realtime_detector: Arc, /// Pattern recognizer pattern_recognizer: Arc, } /// Syndrome detection algorithm #[derive(Debug, Clone)] pub struct SyndromeDetectionAlgorithm { /// Algorithm identifier pub algorithm_id: String, /// Detection method pub method: DetectionMethod, /// Detection accuracy pub accuracy: f64, /// Detection latency pub latency: Duration, /// Resource requirements pub resources: DetectionResources, } /// Detection methods #[derive(Debug, Clone)] pub enum DetectionMethod { ParityCheck, StabilizerMeasurement, ProcessTomography, RandomizedBenchmarking, QuantumVolumeMeasurement, CrossEntropyBenchmarking, } /// Detection resource requirements #[derive(Debug, Clone)] pub struct DetectionResources { /// Measurement shots pub shots: u64, /// Classical processing pub classical_compute: f64, /// Quantum circuit depth pub circuit_depth: u32, /// Measurement time pub measurement_time: Duration, } /// Syndrome database #[derive(Debug)] pub struct SyndromeDatabase { /// Known syndromes syndromes: HashMap, /// Syndrome patterns patterns: Vec, /// Database statistics statistics: SyndromeStatistics, } /// Known syndrome entry #[derive(Debug, Clone)] pub struct KnownSyndrome { /// Syndrome identifier pub syndrome_id: String, /// Syndrome signature pub signature: SyndromeSignature, /// Associated errors pub errors: Vec, /// Frequency of occurrence pub frequency: f64, /// Typical correction pub correction: CorrectionOperation, } /// Syndrome signature #[derive(Debug, Clone)] pub struct SyndromeSignature { /// Signature bits pub bits: Vec, /// Signature hash pub hash: u64, /// Signature confidence pub confidence: f64, } /// Associated error #[derive(Debug, Clone)] pub struct AssociatedError { /// Error description pub description: String, /// Error probability pub probability: f64, /// Error impact pub impact: ErrorImpact, } /// Error impact assessment #[derive(Debug, Clone)] pub struct ErrorImpact { /// Logical error probability pub logical_error_prob: f64, /// Performance degradation pub performance_impact: f64, /// Fidelity reduction pub fidelity_impact: f64, /// Cascading error potential pub cascade_potential: f64, } /// Syndrome pattern entry #[derive(Debug, Clone)] pub struct SyndromePatternEntry { /// Pattern identifier pub pattern_id: String, /// Pattern characteristics pub characteristics: PatternCharacteristics, /// Pattern evolution pub evolution: PatternEvolution, } /// Pattern characteristics #[derive(Debug, Clone)] pub struct PatternCharacteristics { /// Temporal structure pub temporal_structure: TemporalStructure, /// Spatial structure pub spatial_structure: SpatialStructure, /// Correlation structure pub correlation_structure: CorrelationStructure, } /// Temporal structure of patterns #[derive(Debug, Clone)] pub struct TemporalStructure { /// Pattern duration pub duration: Duration, /// Repetition frequency pub frequency: f64, /// Temporal correlations pub correlations: Vec, } /// Temporal correlation #[derive(Debug, Clone)] pub struct TemporalCorrelation { /// Time lag pub lag: Duration, /// Correlation strength pub strength: f64, /// Correlation type pub correlation_type: CorrelationType, } /// Types of correlations #[derive(Debug, Clone)] pub enum CorrelationType { AutoCorrelation, CrossCorrelation, PartialCorrelation, ConditionalCorrelation, } /// Spatial structure of patterns #[derive(Debug, Clone)] pub struct SpatialStructure { /// Geometric layout pub layout: GeometricLayout, /// Connectivity patterns pub connectivity: ConnectivityPattern, /// Spatial correlations pub correlations: Vec, } /// Geometric layout #[derive(Debug, Clone)] pub struct GeometricLayout { /// Layout type pub layout_type: LayoutType, /// Dimensions pub dimensions: Vec, /// Boundary conditions pub boundaries: BoundaryConditions, } /// Types of geometric layouts #[derive(Debug, Clone)] pub enum LayoutType { Square, Hexagonal, Triangular, Cubic, Hypercubic, Irregular, } /// Boundary conditions #[derive(Debug, Clone)] pub struct BoundaryConditions { /// Boundary type pub boundary_type: BoundaryType, /// Periodic boundaries pub periodic: Vec, /// Boundary effects pub effects: Vec, } /// Types of boundaries #[derive(Debug, Clone)] pub enum BoundaryType { Open, Periodic, Reflecting, Absorbing, Mixed, } /// Boundary effect #[derive(Debug, Clone)] pub struct BoundaryEffect { /// Effect type pub effect_type: EffectType, /// Effect strength pub strength: f64, /// Affected region pub region: Vec, } /// Types of boundary effects #[derive(Debug, Clone)] pub enum EffectType { ErrorRateIncrease, DecoherenceIncrease, CorrelationIncrease, FidelityDecrease, } /// Connectivity pattern #[derive(Debug, Clone)] pub struct ConnectivityPattern { /// Pattern type pub pattern_type: ConnectivityType, /// Adjacency matrix pub adjacency: Vec>, /// Connection weights pub weights: Vec>, } /// Types of connectivity #[derive(Debug, Clone)] pub enum ConnectivityType { NearestNeighbor, NextNearestNeighbor, LongRange, AllToAll, Random, SmallWorld, } /// Spatial correlation #[derive(Debug, Clone)] pub struct SpatialCorrelation { /// Correlation range pub range: f64, /// Correlation strength pub strength: f64, /// Correlation function pub function: CorrelationFunction, } /// Correlation functions #[derive(Debug, Clone)] pub enum CorrelationFunction { Exponential, PowerLaw, Gaussian, Polynomial, Custom, } /// Correlation structure #[derive(Debug, Clone)] pub struct CorrelationStructure { /// Correlation matrix pub matrix: Vec>, /// Principal components pub principal_components: Vec, /// Clustering information pub clusters: Vec, } /// Principal component #[derive(Debug, Clone)] pub struct PrincipalComponent { /// Component index pub index: usize, /// Eigenvalue pub eigenvalue: f64, /// Eigenvector pub eigenvector: Vec, /// Explained variance pub explained_variance: f64, } /// Correlation cluster #[derive(Debug, Clone)] pub struct CorrelationCluster { /// Cluster identifier pub cluster_id: String, /// Cluster members pub members: Vec, /// Cluster centroid pub centroid: Vec, /// Intra-cluster correlation pub intra_correlation: f64, } /// Pattern evolution #[derive(Debug, Clone)] pub struct PatternEvolution { /// Evolution trajectory pub trajectory: Vec, /// Evolution rate pub rate: f64, /// Evolution direction pub direction: EvolutionDirection, /// Stability measure pub stability: f64, } /// Evolution point #[derive(Debug, Clone)] pub struct EvolutionPoint { /// Timestamp pub timestamp: crate::temporal::TemporalTimestamp, /// Pattern state pub state: PatternState, /// State transition pub transition: Option, } /// Pattern state #[derive(Debug, Clone)] pub struct PatternState { /// State characteristics pub characteristics: StateCharacteristics, /// State stability pub stability: f64, /// State entropy pub entropy: f64, } /// State characteristics #[derive(Debug, Clone)] pub struct StateCharacteristics { /// Energy level pub energy: f64, /// Complexity measure pub complexity: f64, /// Order parameter pub order: f64, /// Noise level pub noise: f64, } /// State transition #[derive(Debug, Clone)] pub struct StateTransition { /// Transition type pub transition_type: TransitionType, /// Transition probability pub probability: f64, /// Transition rate pub rate: f64, /// Activation energy pub activation_energy: f64, } /// Types of state transitions #[derive(Debug, Clone)] pub enum TransitionType { Continuous, Discontinuous, PhaseTransition, QuantumPhaseTransition, Topological, } /// Evolution directions #[derive(Debug, Clone)] pub enum EvolutionDirection { OrderIncreasing, OrderDecreasing, ComplexityIncreasing, ComplexityDecreasing, Oscillatory, Chaotic, } /// Syndrome statistics #[derive(Debug, Clone)] pub struct SyndromeStatistics { /// Total syndromes recorded pub total_syndromes: u64, /// Unique syndrome patterns pub unique_patterns: u64, /// Average detection time pub avg_detection_time: Duration, /// Detection accuracy pub accuracy: f64, /// False positive rate pub false_positive_rate: f64, } /// Real-time syndrome detector #[derive(Debug)] pub struct RealtimeSyndromeDetector { /// Detection pipeline pipeline: Arc, /// Streaming processor processor: Arc, /// Alert system alert_system: Arc, } /// Detection pipeline #[derive(Debug)] pub struct DetectionPipeline { /// Pipeline stages pub stages: Vec, /// Pipeline throughput pub throughput: f64, /// Pipeline latency pub latency: Duration, } /// Detection stage #[derive(Debug, Clone)] pub struct DetectionStage { /// Stage identifier pub stage_id: String, /// Stage function pub function: DetectionFunction, /// Processing time pub processing_time: Duration, /// Stage accuracy pub accuracy: f64, } /// Detection functions #[derive(Debug, Clone)] pub enum DetectionFunction { DataIngestion, Preprocessing, SyndromeExtraction, PatternMatching, ErrorClassification, ConfidenceAssessment, } /// Streaming syndrome processor #[derive(Debug)] pub struct StreamingSyndromeProcessor { /// Processing buffers buffers: Vec, /// Stream multiplexer multiplexer: Arc, /// Output dispatcher dispatcher: Arc, } /// Processing buffer #[derive(Debug)] pub struct ProcessingBuffer { /// Buffer identifier pub buffer_id: String, /// Buffer capacity pub capacity: usize, /// Current utilization pub utilization: f64, /// Buffer type pub buffer_type: BufferType, } /// Types of processing buffers #[derive(Debug, Clone)] pub enum BufferType { Input, Intermediate, Output, Cache, History, } /// Stream multiplexer #[derive(Debug)] pub struct StreamMultiplexer { /// Input streams pub streams: Vec, /// Multiplexing strategy pub strategy: MultiplexingStrategy, /// Load balancing pub load_balancing: bool, } /// Input stream #[derive(Debug, Clone)] pub struct InputStream { /// Stream identifier pub stream_id: String, /// Stream rate pub rate: f64, /// Stream priority pub priority: u32, /// Quality of service pub qos: QualityOfService, } /// Quality of service parameters #[derive(Debug, Clone)] pub struct QualityOfService { /// Maximum latency pub max_latency: Duration, /// Minimum throughput pub min_throughput: f64, /// Reliability requirement pub reliability: f64, /// Jitter tolerance pub jitter_tolerance: Duration, } /// Multiplexing strategies #[derive(Debug, Clone)] pub enum MultiplexingStrategy { RoundRobin, Priority, WeightedFairQueuing, AdaptiveStrategy, } /// Output dispatcher #[derive(Debug)] pub struct OutputDispatcher { /// Dispatch rules rules: Vec, /// Output channels channels: HashMap, } /// Dispatch rule #[derive(Debug, Clone)] pub struct DispatchRule { /// Rule identifier pub rule_id: String, /// Rule condition pub condition: DispatchCondition, /// Target channel pub target: String, /// Rule priority pub priority: u32, } /// Dispatch condition #[derive(Debug, Clone)] pub struct DispatchCondition { /// Condition type pub condition_type: ConditionType, /// Condition parameters pub parameters: HashMap, /// Evaluation logic pub logic: String, } /// Types of dispatch conditions #[derive(Debug, Clone)] pub enum ConditionType { SeverityThreshold, ErrorType, ConfidenceLevel, TimeWindow, FrequencyThreshold, } /// Output channel #[derive(Debug)] pub struct OutputChannel { /// Channel identifier pub channel_id: String, /// Channel capacity pub capacity: f64, /// Current load pub load: f64, /// Channel reliability pub reliability: f64, } /// Syndrome alert system #[derive(Debug)] pub struct SyndromeAlertSystem { /// Alert rules rules: Vec, /// Active alerts active_alerts: Arc>>, /// Escalation procedures escalation: Vec, } /// Syndrome alert rule #[derive(Debug, Clone)] pub struct SyndromeAlertRule { /// Rule identifier pub rule_id: String, /// Trigger condition pub trigger: AlertTrigger, /// Alert severity pub severity: AlertSeverity, /// Response procedure pub response: ResponseProcedure, } /// Alert trigger conditions #[derive(Debug, Clone)] pub enum AlertTrigger { ErrorRateExceeded(f64), UnknownSyndrome, CorrectionFailure, SystemDegradation, CriticalError, } /// Alert severity levels #[derive(Debug, Clone)] pub enum AlertSeverity { Info, Warning, Error, Critical, Emergency, } /// Response procedure #[derive(Debug, Clone)] pub struct ResponseProcedure { /// Procedure steps pub steps: Vec, /// Escalation threshold pub escalation_threshold: Duration, /// Auto-response enabled pub auto_response: bool, } /// Response step #[derive(Debug, Clone)] pub struct ResponseStep { /// Step description pub description: String, /// Step action pub action: ResponseAction, /// Step timeout pub timeout: Duration, } /// Response actions #[derive(Debug, Clone)] pub enum ResponseAction { LogEvent, NotifyOperator, AttemptCorrection, IsolateError, EscalateAlert, ShutdownSystem, } /// Syndrome alert #[derive(Debug, Clone)] pub struct SyndromeAlert { /// Alert identifier pub alert_id: String, /// Alert timestamp pub timestamp: crate::temporal::TemporalTimestamp, /// Alert severity pub severity: AlertSeverity, /// Alert message pub message: String, /// Associated syndrome pub syndrome: String, /// Response status pub response_status: ResponseStatus, } /// Response status #[derive(Debug, Clone)] pub enum ResponseStatus { Pending, InProgress, Completed, Failed, Escalated, } /// Escalation procedure #[derive(Debug, Clone)] pub struct EscalationProcedure { /// Procedure identifier pub procedure_id: String, /// Escalation trigger pub trigger: EscalationTrigger, /// Escalation target pub target: EscalationTarget, /// Escalation timeline pub timeline: Duration, } /// Escalation triggers #[derive(Debug, Clone)] pub enum EscalationTrigger { TimeElapsed(Duration), SeverityIncrease, RepeatOccurrence, SystemFailure, ManualEscalation, } /// Escalation targets #[derive(Debug, Clone)] pub enum EscalationTarget { NextLevel, TechnicalTeam, ManagementTeam, ExternalSupport, EmergencyProtocol, } /// Syndrome pattern recognizer #[derive(Debug)] pub struct SyndromePatternRecognizer { /// Recognition algorithms algorithms: Vec, /// Pattern library library: Arc, /// Machine learning models ml_models: Vec, } /// Recognition algorithm #[derive(Debug, Clone)] pub struct RecognitionAlgorithm { /// Algorithm identifier pub algorithm_id: String, /// Recognition method pub method: RecognitionMethod, /// Recognition accuracy pub accuracy: f64, /// Processing speed pub speed: f64, } /// Recognition methods #[derive(Debug, Clone)] pub enum RecognitionMethod { TemplateMatching, FeatureMatching, StatisticalMatching, MachineLearning, DeepLearning, EnsembleMethod, } /// Pattern library #[derive(Debug)] pub struct PatternLibrary { /// Library patterns pub patterns: HashMap, /// Pattern hierarchies pub hierarchies: Vec, /// Search index pub index: PatternSearchIndex, } /// Library pattern #[derive(Debug, Clone)] pub struct LibraryPattern { /// Pattern identifier pub pattern_id: String, /// Pattern template pub template: PatternTemplate, /// Usage statistics pub usage: UsageStatistics, /// Pattern metadata pub metadata: PatternMetadata, } /// Pattern template #[derive(Debug, Clone)] pub struct PatternTemplate { /// Template structure pub structure: TemplateStructure, /// Variable parameters pub parameters: Vec, /// Matching criteria pub criteria: MatchingCriteria, } /// Template structure #[derive(Debug, Clone)] pub struct TemplateStructure { /// Structure type pub structure_type: StructureType, /// Structure elements pub elements: Vec, /// Element relationships pub relationships: Vec, } /// Types of template structures #[derive(Debug, Clone)] pub enum StructureType { Sequential, Hierarchical, Network, Tree, Graph, Matrix, } /// Structure element #[derive(Debug, Clone)] pub struct StructureElement { /// Element identifier pub element_id: String, /// Element type pub element_type: ElementType, /// Element properties pub properties: HashMap, } /// Types of structure elements #[derive(Debug, Clone)] pub enum ElementType { Node, Edge, Cluster, Substructure, Marker, } /// Element relationship #[derive(Debug, Clone)] pub struct ElementRelationship { /// Relationship identifier pub relationship_id: String, /// Source element pub source: String, /// Target element pub target: String, /// Relationship type pub relationship_type: RelationshipType, /// Relationship strength pub strength: f64, } /// Types of element relationships #[derive(Debug, Clone)] pub enum RelationshipType { Parent, Child, Sibling, Dependency, Association, Aggregation, } /// Template parameter #[derive(Debug, Clone)] pub struct TemplateParameter { /// Parameter name pub name: String, /// Parameter type pub param_type: TemplateParameterType, /// Default value pub default: f64, /// Value range pub range: (f64, f64), } /// Types of template parameters #[derive(Debug, Clone)] pub enum TemplateParameterType { Continuous, Discrete, Categorical, Boolean, } /// Matching criteria for patterns #[derive(Debug, Clone)] pub struct MatchingCriteria { /// Similarity threshold pub similarity_threshold: f64, /// Required features pub required_features: Vec, /// Optional features pub optional_features: Vec, /// Exclusion criteria pub exclusions: Vec, } /// Usage statistics for patterns #[derive(Debug, Clone)] pub struct UsageStatistics { /// Usage frequency pub frequency: f64, /// Success rate pub success_rate: f64, /// Average processing time pub avg_time: Duration, /// User ratings pub ratings: Vec, } /// Pattern metadata #[derive(Debug, Clone)] pub struct PatternMetadata { /// Creation timestamp pub created: crate::temporal::TemporalTimestamp, /// Last updated pub updated: crate::temporal::TemporalTimestamp, /// Version number pub version: String, /// Author information pub author: String, /// Pattern tags pub tags: Vec, } /// Pattern hierarchy #[derive(Debug, Clone)] pub struct PatternHierarchy { /// Hierarchy identifier pub hierarchy_id: String, /// Root patterns pub roots: Vec, /// Parent-child relationships pub relationships: Vec, /// Hierarchy depth pub depth: u32, } /// Hierarchy relationship #[derive(Debug, Clone)] pub struct HierarchyRelationship { /// Parent pattern pub parent: String, /// Child pattern pub child: String, /// Relationship strength pub strength: f64, /// Inheritance properties pub inheritance: Vec, } /// Pattern search index #[derive(Debug)] pub struct PatternSearchIndex { /// Index entries pub entries: Vec, /// Search algorithms pub algorithms: Vec, } /// Pattern index entry #[derive(Debug, Clone)] pub struct PatternIndexEntry { /// Pattern identifier pub pattern_id: String, /// Search keywords pub keywords: Vec, /// Feature vector pub features: Vec, /// Index score pub score: f64, } /// Pattern search algorithm #[derive(Debug, Clone)] pub struct PatternSearchAlgorithm { /// Algorithm identifier pub algorithm_id: String, /// Search method pub method: PatternSearchMethod, /// Search accuracy pub accuracy: f64, /// Search speed pub speed: f64, } /// Pattern search methods #[derive(Debug, Clone)] pub enum PatternSearchMethod { ExactMatch, FuzzyMatch, SemanticSearch, VectorSimilarity, GraphMatching, MachineLearning, } /// Pattern recognition model #[derive(Debug, Clone)] pub struct PatternRecognitionModel { /// Model identifier pub model_id: String, /// Model architecture pub architecture: ModelArchitecture, /// Training configuration pub training: TrainingConfiguration, /// Performance metrics pub performance: ModelPerformance, } /// Model architecture #[derive(Debug, Clone)] pub struct ModelArchitecture { /// Architecture type pub arch_type: ArchitectureType, /// Layer configuration pub layers: Vec, /// Activation functions pub activations: Vec, /// Regularization pub regularization: RegularizationConfig, } /// Types of model architectures #[derive(Debug, Clone)] pub enum ArchitectureType { FeedForward, Convolutional, Recurrent, Transformer, ResNet, Attention, } /// Layer configuration #[derive(Debug, Clone)] pub struct LayerConfiguration { /// Layer type pub layer_type: LayerType, /// Layer size pub size: usize, /// Activation function pub activation: String, /// Dropout rate pub dropout: f64, } /// Types of neural layers #[derive(Debug, Clone)] pub enum LayerType { Dense, Convolutional, Pooling, Normalization, Dropout, Attention, } /// Activation functions #[derive(Debug, Clone)] pub enum ActivationFunction { ReLU, Sigmoid, Tanh, Softmax, LeakyReLU, ELU, } /// Regularization configuration #[derive(Debug, Clone)] pub struct RegularizationConfig { /// L1 regularization weight pub l1_weight: f64, /// L2 regularization weight pub l2_weight: f64, /// Dropout rate pub dropout_rate: f64, /// Batch normalization pub batch_norm: bool, } /// Training configuration #[derive(Debug, Clone)] pub struct TrainingConfiguration { /// Learning rate pub learning_rate: f64, /// Batch size pub batch_size: usize, /// Number of epochs pub epochs: u32, /// Optimizer type pub optimizer: OptimizerType, /// Loss function pub loss_function: LossFunction, } /// Types of optimizers #[derive(Debug, Clone)] pub enum OptimizerType { SGD, Adam, AdamW, RMSprop, Adagrad, } /// Loss functions #[derive(Debug, Clone)] pub enum LossFunction { MeanSquaredError, CrossEntropy, BinaryCrossEntropy, MeanAbsoluteError, Huber, } /// Model performance metrics #[derive(Debug, Clone)] pub struct ModelPerformance { /// Training accuracy pub training_accuracy: f64, /// Validation accuracy pub validation_accuracy: f64, /// Test accuracy pub test_accuracy: f64, /// F1 score pub f1_score: f64, /// Precision pub precision: f64, /// Recall pub recall: f64, } // Implementation impl ErrorSyndromeDetector { pub async fn new(_config: &ErrorCorrectionConfig) -> AiContextResult { Ok(Self { algorithms: vec![], syndrome_db: Arc::new(RwLock::new(SyndromeDatabase { syndromes: HashMap::new(), patterns: vec![], statistics: SyndromeStatistics { total_syndromes: 0, unique_patterns: 0, avg_detection_time: Duration::from_millis(1), accuracy: 0.95, false_positive_rate: 0.01, }, })), realtime_detector: Arc::new(RealtimeSyndromeDetector::new()), pattern_recognizer: Arc::new(SyndromePatternRecognizer::new()), }) } pub async fn detect_syndromes( &self, _code: &str, potential_errors: &[String], ) -> AiContextResult> { let mut syndromes = Vec::new(); for (i, error) in potential_errors.iter().enumerate() { syndromes.push(ErrorSyndrome { syndrome_id: format!("syndrome_{}", i), detected_at: crate::temporal::TemporalTimestamp::now(), pattern: SyndromePattern { bits: vec![1, 0, 1, 0], // Simplified pattern weight: 2, parity: 0, stabilizer_outcomes: vec![], }, error_type: QuantumErrorType::BitFlipError, location: ErrorLocation { physical_qubits: vec![i], logical_qubit: Some(0), syndrome: vec![1, 0], probability: 0.8, confidence: 0.9, }, confidence: 0.9, correction: CorrectionRecommendation { correction: CorrectionOperation { operation_type: CorrectionType::PauliX, target_qubits: vec![i], parameters: HashMap::new(), effectiveness: 0.95, }, success_probability: 0.95, alternatives: vec![], cost: 0.1, }, }); } Ok(syndromes) } } impl RealtimeSyndromeDetector { pub fn new() -> Self { Self { pipeline: Arc::new(DetectionPipeline { stages: vec![], throughput: 1000.0, latency: Duration::from_micros(100), }), processor: Arc::new(StreamingSyndromeProcessor::new()), alert_system: Arc::new(SyndromeAlertSystem::new()), } } } impl StreamingSyndromeProcessor { pub fn new() -> Self { Self { buffers: vec![], multiplexer: Arc::new(StreamMultiplexer { streams: vec![], strategy: MultiplexingStrategy::Priority, load_balancing: true, }), dispatcher: Arc::new(OutputDispatcher::new()), } } } impl OutputDispatcher { pub fn new() -> Self { Self { rules: vec![], channels: HashMap::new(), } } } impl SyndromeAlertSystem { pub fn new() -> Self { Self { rules: vec![], active_alerts: Arc::new(RwLock::new(Vec::new())), escalation: vec![], } } } impl SyndromePatternRecognizer { pub fn new() -> Self { Self { algorithms: vec![], library: Arc::new(PatternLibrary { patterns: HashMap::new(), hierarchies: vec![], index: PatternSearchIndex { entries: vec![], algorithms: vec![], }, }), ml_models: vec![], } } } #[cfg(test)] mod tests { use super::*; #[test] fn test_error_syndrome_creation() { let syndrome = ErrorSyndrome { syndrome_id: "test_syndrome".to_string(), detected_at: crate::temporal::TemporalTimestamp::now(), pattern: SyndromePattern { bits: vec![1, 0, 1, 1], weight: 3, parity: 1, stabilizer_outcomes: vec![ StabilizerOutcome { stabilizer_id: "X_stabilizer".to_string(), measurement: 1, confidence: 0.95, qubits: vec![0, 1], } ], }, error_type: QuantumErrorType::BitFlipError, location: ErrorLocation { physical_qubits: vec![0, 1], logical_qubit: Some(0), syndrome: vec![1, 0, 1], probability: 0.85, confidence: 0.9, }, confidence: 0.9, correction: CorrectionRecommendation { correction: CorrectionOperation { operation_type: CorrectionType::PauliX, target_qubits: vec![0], parameters: HashMap::new(), effectiveness: 0.98, }, success_probability: 0.98, alternatives: vec![], cost: 0.05, }, }; assert_eq!(syndrome.syndrome_id, "test_syndrome"); assert!(matches!(syndrome.error_type, QuantumErrorType::BitFlipError)); assert_eq!(syndrome.pattern.weight, 3); assert_eq!(syndrome.location.physical_qubits.len(), 2); assert!(matches!(syndrome.correction.correction.operation_type, CorrectionType::PauliX)); } #[test] fn test_syndrome_pattern() { let pattern = SyndromePattern { bits: vec![1, 1, 0, 1, 0], weight: 3, parity: 1, stabilizer_outcomes: vec![], }; assert_eq!(pattern.bits.len(), 5); assert_eq!(pattern.weight, 3); assert_eq!(pattern.parity, 1); assert_eq!(pattern.stabilizer_outcomes.len(), 0); } #[test] fn test_stabilizer_outcome() { let outcome = StabilizerOutcome { stabilizer_id: "Z_stabilizer_test".to_string(), measurement: 0, confidence: 0.87, qubits: vec![1, 2, 3], }; assert_eq!(outcome.stabilizer_id, "Z_stabilizer_test"); assert_eq!(outcome.measurement, 0); assert_eq!(outcome.confidence, 0.87); assert_eq!(outcome.qubits, vec![1, 2, 3]); } #[test] fn test_quantum_parity_check() { let parity_check = QuantumParityCheck { check_id: "surface_code_check".to_string(), check_matrix: ParityCheckMatrix { dimensions: (4, 9), elements: vec![ vec![1, 1, 0, 1, 1, 0, 0, 0, 0], vec![0, 1, 1, 0, 1, 1, 0, 0, 0], vec![0, 0, 0, 1, 1, 0, 1, 1, 0], vec![0, 0, 0, 0, 1, 1, 0, 1, 1], ], rank: 4, null_space_dim: 1, }, check_qubits: vec![0, 1, 2, 3], data_qubits: vec![4, 5, 6, 7, 8], frequency: 1000.0, efficiency: 0.95, }; assert_eq!(parity_check.check_id, "surface_code_check"); assert_eq!(parity_check.check_matrix.dimensions, (4, 9)); assert_eq!(parity_check.check_qubits.len(), 4); assert_eq!(parity_check.data_qubits.len(), 5); assert_eq!(parity_check.frequency, 1000.0); assert_eq!(parity_check.efficiency, 0.95); } #[test] fn test_syndrome_signature() { let signature = SyndromeSignature { bits: vec![1, 0, 1, 1, 0, 0, 1], hash: 0xABCDEF123456789, confidence: 0.92, }; assert_eq!(signature.bits.len(), 7); assert_eq!(signature.hash, 0xABCDEF123456789); assert_eq!(signature.confidence, 0.92); } #[test] fn test_detection_algorithm() { let algorithm = SyndromeDetectionAlgorithm { algorithm_id: "parity_check_detection".to_string(), method: DetectionMethod::ParityCheck, accuracy: 0.99, latency: Duration::from_micros(50), resources: DetectionResources { shots: 1000, classical_compute: 5.0, circuit_depth: 3, measurement_time: Duration::from_micros(10), }, }; assert_eq!(algorithm.algorithm_id, "parity_check_detection"); assert!(matches!(algorithm.method, DetectionMethod::ParityCheck)); assert_eq!(algorithm.accuracy, 0.99); assert_eq!(algorithm.resources.shots, 1000); assert_eq!(algorithm.resources.circuit_depth, 3); } #[test] fn test_temporal_correlation() { let correlation = TemporalCorrelation { lag: Duration::from_millis(5), strength: 0.75, correlation_type: CorrelationType::AutoCorrelation, }; assert_eq!(correlation.lag, Duration::from_millis(5)); assert_eq!(correlation.strength, 0.75); assert!(matches!(correlation.correlation_type, CorrelationType::AutoCorrelation)); } #[test] fn test_spatial_structure() { let layout = GeometricLayout { layout_type: LayoutType::Square, dimensions: vec![7, 7], boundaries: BoundaryConditions { boundary_type: BoundaryType::Open, periodic: vec![false, false], effects: vec![], }, }; assert!(matches!(layout.layout_type, LayoutType::Square)); assert_eq!(layout.dimensions, vec![7, 7]); assert!(matches!(layout.boundaries.boundary_type, BoundaryType::Open)); } #[test] fn test_syndrome_alert() { let alert = SyndromeAlert { alert_id: "critical_error_001".to_string(), timestamp: crate::temporal::TemporalTimestamp::now(), severity: AlertSeverity::Critical, message: "High error rate detected".to_string(), syndrome: "syndrome_xyz".to_string(), response_status: ResponseStatus::Pending, }; assert_eq!(alert.alert_id, "critical_error_001"); assert!(matches!(alert.severity, AlertSeverity::Critical)); assert_eq!(alert.message, "High error rate detected"); assert!(matches!(alert.response_status, ResponseStatus::Pending)); } #[test] fn test_pattern_recognition_model() { let model = PatternRecognitionModel { model_id: "cnn_syndrome_classifier".to_string(), architecture: ModelArchitecture { arch_type: ArchitectureType::Convolutional, layers: vec![ LayerConfiguration { layer_type: LayerType::Convolutional, size: 32, activation: "relu".to_string(), dropout: 0.1, } ], activations: vec![ActivationFunction::ReLU], regularization: RegularizationConfig { l1_weight: 0.01, l2_weight: 0.001, dropout_rate: 0.2, batch_norm: true, }, }, training: TrainingConfiguration { learning_rate: 0.001, batch_size: 32, epochs: 100, optimizer: OptimizerType::Adam, loss_function: LossFunction::CrossEntropy, }, performance: ModelPerformance { training_accuracy: 0.95, validation_accuracy: 0.92, test_accuracy: 0.91, f1_score: 0.90, precision: 0.89, recall: 0.91, }, }; assert_eq!(model.model_id, "cnn_syndrome_classifier"); assert!(matches!(model.architecture.arch_type, ArchitectureType::Convolutional)); assert_eq!(model.training.learning_rate, 0.001); assert_eq!(model.performance.test_accuracy, 0.91); } #[tokio::test] async fn test_syndrome_detector_creation() { let config = ErrorCorrectionConfig::default(); let detector = ErrorSyndromeDetector::new(&config).await; assert!(detector.is_ok()); let detector = detector.unwrap(); assert_eq!(detector.algorithms.len(), 0); // Initially empty } #[tokio::test] async fn test_syndrome_detection() { let config = ErrorCorrectionConfig::default(); let detector = ErrorSyndromeDetector::new(&config).await.unwrap(); let code = "fn test() { vec![].get(0).unwrap(); }"; let errors = vec!["Potential panic".to_string(), "Out of bounds".to_string()]; let syndromes = detector.detect_syndromes(code, &errors).await; assert!(syndromes.is_ok()); let syndromes = syndromes.unwrap(); assert_eq!(syndromes.len(), 2); assert_eq!(syndromes[0].syndrome_id, "syndrome_0"); assert_eq!(syndromes[1].syndrome_id, "syndrome_1"); } }