quantrs2_device/qec/
config.rs

1//! Quantum Error Correction Configuration Types
2//!
3//! This module defines all configuration types for the QEC system:
4//! - Main QECConfig with all subsystem configurations
5//! - QEC strategies and code types
6//! - ML configuration for pattern recognition and prediction
7//! - Monitoring and optimization configurations
8//! - Internal structures for caching and statistics
9
10use std::collections::{BTreeMap, HashMap, VecDeque};
11use std::hash::Hasher;
12use std::sync::{Arc, Mutex, RwLock};
13use std::time::{Duration, SystemTime};
14
15use quantrs2_core::qubit::QubitId;
16use scirs2_core::ndarray::{Array1, Array2};
17use serde::{Deserialize, Serialize};
18
19use crate::{
20    calibration::CalibrationManager, prelude::SciRS2NoiseModeler, topology::HardwareTopology,
21};
22
23use super::{
24    adaptive::{
25        self, AccessControl, ActivationFunction, AlertSuppression, AlertingConfig,
26        ArchitectureType, ArchivalStrategy, ConnectionPattern, ConsistencyLevel, DashboardConfig,
27        DataAugmentationConfig, DataCollectionConfig, DataPreprocessingConfig, DataRetention,
28        DataSource, DeploymentStrategy, DimensionalityReductionMethod, DriftDetection,
29        EnvironmentConfig, EnvironmentType, EscalationRules, FeatureSelectionMethod,
30        HardwareAcceleration, InferenceCaching, LayerConfig, LayerType, LossFunction,
31        ModelDeployment, ModelManagementConfig, ModelMonitoring, ModelOptimization,
32        ModelVersioning, MonitoringAlertingConfig, NormalizationMethod, OptimizerType,
33        PerformanceMonitoring, ResourceAllocation, RollbackStrategy, ScalingConfig, StorageBackend,
34        StorageConfig, VersionControlSystem,
35    },
36    codes::{QECCodeType, SurfaceCodeLayout},
37    detection, mitigation,
38    types::{CorrectionOperation, DeviceState, ExecutionContext, SyndromePattern},
39};
40
41pub struct QECConfig {
42    /// QEC code type
43    pub code_type: QECCodeType,
44    /// Code distance
45    pub distance: usize,
46    /// QEC strategies
47    pub strategies: Vec<QECStrategy>,
48    /// Enable ML optimization
49    pub enable_ml_optimization: bool,
50    /// Enable adaptive thresholds
51    pub enable_adaptive_thresholds: bool,
52    /// Correction timeout
53    pub correction_timeout: Duration,
54    /// Syndrome detection configuration
55    pub syndrome_detection: detection::SyndromeDetectionConfig,
56    /// ML configuration
57    pub ml_config: QECMLConfig,
58    /// Adaptive configuration
59    pub adaptive_config: adaptive::AdaptiveQECConfig,
60    /// Monitoring configuration
61    pub monitoring_config: QECMonitoringConfig,
62    /// Optimization configuration
63    pub optimization_config: QECOptimizationConfig,
64    /// Error mitigation configuration
65    pub error_mitigation: mitigation::ErrorMitigationConfig,
66    /// Error correction codes to use
67    pub error_codes: Vec<QECCodeType>,
68    /// Error correction strategy
69    pub correction_strategy: QECStrategy,
70    /// Adaptive QEC configuration
71    pub adaptive_qec: adaptive::AdaptiveQECConfig,
72    /// Performance optimization
73    pub performance_optimization: QECOptimizationConfig,
74}
75
76/// Error correction strategies
77#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
78pub enum QECStrategy {
79    /// Active error correction
80    ActiveCorrection,
81    /// Passive monitoring
82    PassiveMonitoring,
83    /// Adaptive threshold adjustment
84    AdaptiveThreshold,
85    /// ML-driven error correction
86    MLDriven,
87    /// Hybrid approach
88    HybridApproach,
89    /// Passive error correction
90    Passive,
91    /// Active error correction with periodic syndrome measurement
92    ActivePeriodic { cycle_time: Duration },
93    /// Adaptive error correction based on noise levels
94    Adaptive,
95    /// Fault-tolerant error correction
96    FaultTolerant,
97    /// Hybrid approach (legacy)
98    Hybrid { strategies: Vec<Self> },
99}
100
101#[derive(Debug, Clone)]
102pub struct ErrorCorrectionCycleResult {
103    pub syndromes_detected: Option<Vec<SyndromePattern>>,
104    pub corrections_applied: Option<Vec<CorrectionOperation>>,
105    pub success: bool,
106}
107
108// Note: SyndromePattern already defined above, removing duplicate
109
110// ExecutionContext and DeviceState are imported from types
111
112/// Error statistics for adaptive learning
113#[derive(Debug, Clone, Serialize, Deserialize)]
114pub struct ErrorStatistics {
115    pub error_rates_by_type: HashMap<String, f64>,
116    pub error_correlations: Array2<f64>,
117    pub temporal_patterns: Vec<TemporalPattern>,
118    pub spatial_patterns: Vec<SpatialPattern>,
119    pub prediction_accuracy: f64,
120    pub last_updated: SystemTime,
121}
122
123/// Temporal error patterns
124#[derive(Debug, Clone, Serialize, Deserialize)]
125pub struct TemporalPattern {
126    pub pattern_type: String,
127    pub frequency: f64,
128    pub amplitude: f64,
129    pub phase: f64,
130    pub confidence: f64,
131}
132
133/// Spatial error patterns
134#[derive(Debug, Clone, Serialize, Deserialize)]
135pub struct SpatialPattern {
136    pub pattern_type: String,
137    pub affected_qubits: Vec<usize>,
138    pub correlation_strength: f64,
139    pub propagation_direction: Option<String>,
140}
141
142/// Adaptive thresholds for QEC
143#[derive(Debug, Clone, Serialize, Deserialize)]
144pub struct AdaptiveThresholds {
145    pub error_detection_threshold: f64,
146    pub correction_confidence_threshold: f64,
147    pub syndrome_pattern_threshold: f64,
148    pub ml_prediction_threshold: f64,
149    pub adaptation_rate: f64,
150    pub stability_window: Duration,
151}
152
153// AdaptiveThresholds already defined above
154
155/// Machine learning model for error correction
156#[derive(Debug, Clone, Serialize, Deserialize)]
157pub struct MLModel {
158    pub model_type: String,
159    pub model_data: Vec<u8>, // Serialized model
160    pub training_accuracy: f64,
161    pub validation_accuracy: f64,
162    pub last_trained: SystemTime,
163    pub feature_importance: HashMap<String, f64>,
164}
165
166/// Correction performance metrics
167#[derive(Debug, Clone, Serialize, Deserialize)]
168pub struct CorrectionMetrics {
169    pub total_corrections: usize,
170    pub successful_corrections: usize,
171    pub false_positives: usize,
172    pub false_negatives: usize,
173    pub average_correction_time: Duration,
174    pub resource_utilization: ResourceUtilization,
175    pub fidelity_improvement: f64,
176}
177
178/// Resource utilization for QEC
179#[derive(Debug, Clone, Serialize, Deserialize)]
180pub struct ResourceUtilization {
181    pub auxiliary_qubits_used: f64,
182    pub measurement_overhead: f64,
183    pub classical_processing_time: f64,
184    pub memory_usage: usize,
185}
186
187/// Cached optimization results
188#[derive(Debug, Clone, Serialize, Deserialize)]
189pub struct CachedOptimization {
190    pub optimization_result: OptimizationResult,
191    pub context_hash: u64,
192    pub timestamp: SystemTime,
193    pub hit_count: usize,
194    pub performance_score: f64,
195}
196
197/// QEC optimization result
198#[derive(Debug, Clone, Serialize, Deserialize)]
199pub struct OptimizationResult {
200    pub optimal_strategy: QECStrategy,
201    pub predicted_performance: f64,
202    pub resource_requirements: ResourceRequirements,
203    pub confidence_score: f64,
204    pub optimization_time: Duration,
205}
206
207/// Resource requirements for QEC strategy
208#[derive(Debug, Clone, Serialize, Deserialize)]
209pub struct ResourceRequirements {
210    pub auxiliary_qubits: usize,
211    pub syndrome_measurements: usize,
212    pub classical_processing: Duration,
213    pub memory_mb: usize,
214    pub power_watts: f64,
215}
216
217// Default implementations for test compatibility
218impl Default for ErrorStatistics {
219    fn default() -> Self {
220        Self {
221            error_rates_by_type: HashMap::new(),
222            error_correlations: Array2::zeros((0, 0)),
223            temporal_patterns: Vec::new(),
224            spatial_patterns: Vec::new(),
225            prediction_accuracy: 0.0,
226            last_updated: SystemTime::now(),
227        }
228    }
229}
230
231impl Default for AdaptiveThresholds {
232    fn default() -> Self {
233        Self {
234            error_detection_threshold: 0.95,
235            correction_confidence_threshold: 0.90,
236            syndrome_pattern_threshold: 0.85,
237            ml_prediction_threshold: 0.80,
238            adaptation_rate: 0.01,
239            stability_window: Duration::from_secs(60),
240        }
241    }
242}
243
244impl Default for CorrectionMetrics {
245    fn default() -> Self {
246        Self {
247            total_corrections: 0,
248            successful_corrections: 0,
249            false_positives: 0,
250            false_negatives: 0,
251            average_correction_time: Duration::from_millis(100),
252            resource_utilization: ResourceUtilization::default(),
253            fidelity_improvement: 0.0,
254        }
255    }
256}
257
258impl Default for ResourceUtilization {
259    fn default() -> Self {
260        Self {
261            auxiliary_qubits_used: 0.0,
262            measurement_overhead: 0.0,
263            classical_processing_time: 0.0,
264            memory_usage: 0,
265        }
266    }
267}
268impl Default for QECConfig {
269    fn default() -> Self {
270        Self {
271            code_type: QECCodeType::SurfaceCode {
272                distance: 5,
273                layout: SurfaceCodeLayout::Square,
274            },
275            distance: 5,
276            strategies: vec![QECStrategy::Adaptive],
277            enable_ml_optimization: true,
278            enable_adaptive_thresholds: true,
279            correction_timeout: Duration::from_millis(100),
280            adaptive_config: adaptive::AdaptiveQECConfig {
281                enable_real_time_adaptation: true,
282                adaptation_window: Duration::from_secs(60),
283                performance_threshold: 0.95,
284                enable_threshold_adaptation: true,
285                enable_strategy_switching: true,
286                learning_rate: 0.01,
287                enable_adaptive: true,
288                strategies: vec![],
289                learning: adaptive::AdaptiveLearningConfig {
290                    algorithms: vec![],
291                    online_learning: adaptive::OnlineLearningConfig {
292                        enable_online: true,
293                        learning_rate_adaptation: adaptive::LearningRateAdaptation::Adaptive,
294                        concept_drift: adaptive::ConceptDriftConfig {
295                            enable_detection: false,
296                            methods: vec![],
297                            responses: vec![],
298                        },
299                        model_updates: adaptive::ModelUpdateConfig {
300                            frequency: adaptive::UpdateFrequency::EventTriggered,
301                            triggers: vec![],
302                            strategies: vec![],
303                        },
304                    },
305                    transfer_learning: adaptive::TransferLearningConfig {
306                        enable_transfer: false,
307                        source_domains: vec![],
308                        strategies: vec![],
309                        domain_adaptation: adaptive::DomainAdaptationConfig {
310                            methods: vec![],
311                            validation: vec![],
312                        },
313                    },
314                    meta_learning: adaptive::MetaLearningConfig {
315                        enable_meta: false,
316                        algorithms: vec![],
317                        task_distribution: adaptive::TaskDistributionConfig {
318                            task_types: vec![],
319                            complexity_range: (0.0, 1.0),
320                            generation_strategy: adaptive::TaskGenerationStrategy::Random,
321                        },
322                        meta_optimization: adaptive::MetaOptimizationConfig {
323                            optimizer: adaptive::MetaOptimizer::Adam,
324                            learning_rates: adaptive::LearningRates {
325                                inner_lr: 0.01,
326                                outer_lr: 0.001,
327                                adaptive: true,
328                            },
329                            regularization: adaptive::MetaRegularization {
330                                regularization_type: adaptive::RegularizationType::L2,
331                                strength: 0.001,
332                            },
333                        },
334                    },
335                },
336                realtime_optimization: adaptive::RealtimeOptimizationConfig {
337                    enable_realtime: true,
338                    objectives: vec![],
339                    algorithms: vec![],
340                    constraints: adaptive::ResourceConstraints {
341                        time_limit: Duration::from_millis(100),
342                        memory_limit: 1024 * 1024,
343                        power_budget: 100.0,
344                        hardware_constraints: adaptive::HardwareConstraints {
345                            connectivity: adaptive::ConnectivityConstraints {
346                                coupling_map: vec![],
347                                max_distance: 10,
348                                routing_overhead: 1.2,
349                            },
350                            gate_fidelities: std::collections::HashMap::new(),
351                            coherence_times: adaptive::CoherenceTimes {
352                                t1_times: std::collections::HashMap::new(),
353                                t2_times: std::collections::HashMap::new(),
354                                gate_times: std::collections::HashMap::new(),
355                            },
356                        },
357                    },
358                },
359                feedback_control: adaptive::FeedbackControlConfig {
360                    enable_feedback: true,
361                    algorithms: vec![],
362                    sensors: adaptive::SensorConfig {
363                        sensor_types: vec![],
364                        sampling_rates: std::collections::HashMap::new(),
365                        noise_characteristics: adaptive::NoiseCharacteristics {
366                            gaussian_noise: 0.01,
367                            systematic_bias: 0.0,
368                            temporal_correlation: 0.1,
369                        },
370                    },
371                    actuators: adaptive::ActuatorConfig {
372                        actuator_types: vec![],
373                        response_times: std::collections::HashMap::new(),
374                        control_ranges: std::collections::HashMap::new(),
375                    },
376                },
377                prediction: adaptive::PredictionConfig::default(),
378                optimization: adaptive::OptimizationConfig::default(),
379            },
380            optimization_config: QECOptimizationConfig {
381                enable_optimization: true,
382                enable_code_optimization: true,
383                enable_layout_optimization: true,
384                enable_scheduling_optimization: true,
385                optimization_algorithm:
386                    crate::unified_benchmarking::config::OptimizationAlgorithm::GradientDescent,
387                optimization_objectives: vec![],
388                constraint_satisfaction: ConstraintSatisfactionConfig {
389                    hardware_constraints: vec![],
390                    resource_constraints: vec![],
391                    performance_constraints: vec![],
392                },
393                targets: vec![],
394                metrics: vec![],
395                strategies: vec![],
396            },
397            error_codes: vec![QECCodeType::SurfaceCode {
398                distance: 5,
399                layout: SurfaceCodeLayout::Square,
400            }],
401            correction_strategy: QECStrategy::Adaptive,
402            syndrome_detection: detection::SyndromeDetectionConfig {
403                enable_parallel_detection: true,
404                detection_rounds: 3,
405                stabilizer_measurement_shots: 1000,
406                enable_syndrome_validation: true,
407                validation_threshold: 0.95,
408                enable_error_correlation: true,
409                enable_detection: true,
410                detection_frequency: 1000.0,
411                detection_methods: vec![],
412                pattern_recognition: detection::PatternRecognitionConfig {
413                    enable_recognition: true,
414                    algorithms: vec![],
415                    training_config: detection::PatternTrainingConfig {
416                        training_size: 1000,
417                        validation_split: 0.2,
418                        epochs: 100,
419                        learning_rate: 0.001,
420                        batch_size: 32,
421                    },
422                    real_time_adaptation: false,
423                },
424                statistical_analysis: detection::SyndromeStatisticsConfig {
425                    enable_statistics: true,
426                    methods: vec![],
427                    confidence_level: 0.95,
428                    data_retention_days: 30,
429                },
430            },
431            error_mitigation: mitigation::ErrorMitigationConfig {
432                enable_zne: true,
433                enable_symmetry_verification: true,
434                enable_readout_correction: true,
435                enable_dynamical_decoupling: true,
436                mitigation_strategies: vec![],
437                zne_config: mitigation::ZNEConfig {
438                    noise_factors: vec![1.0, 1.5, 2.0],
439                    extrapolation_method: mitigation::ExtrapolationMethod::Linear,
440                    circuit_folding: mitigation::CircuitFoldingMethod::GlobalFolding,
441                    enable_zne: true,
442                    noise_scaling_factors: vec![1.0, 1.5, 2.0],
443                    folding: mitigation::FoldingConfig {
444                        folding_type: mitigation::FoldingType::Global,
445                        global_folding: true,
446                        local_folding: mitigation::LocalFoldingConfig {
447                            regions: vec![],
448                            selection_strategy: mitigation::RegionSelectionStrategy::Adaptive,
449                            overlap_handling: mitigation::OverlapHandling::Ignore,
450                        },
451                        gate_specific: mitigation::GateSpecificFoldingConfig {
452                            folding_rules: std::collections::HashMap::new(),
453                            priority_ordering: vec![],
454                            error_rate_weighting: false,
455                            folding_strategies: std::collections::HashMap::new(),
456                            default_strategy: mitigation::DefaultFoldingStrategy::Identity,
457                            prioritized_gates: vec![],
458                        },
459                    },
460                    richardson: mitigation::RichardsonConfig {
461                        enable_richardson: false,
462                        order: 2,
463                        stability_check: true,
464                        error_estimation: mitigation::ErrorEstimationConfig {
465                            method: mitigation::ErrorEstimationMethod::Bootstrap,
466                            bootstrap_samples: 100,
467                            confidence_level: 0.95,
468                        },
469                    },
470                },
471                enable_mitigation: true,
472                strategies: vec![],
473                zne: mitigation::ZNEConfig {
474                    noise_factors: vec![1.0, 1.5, 2.0],
475                    extrapolation_method: mitigation::ExtrapolationMethod::Linear,
476                    circuit_folding: mitigation::CircuitFoldingMethod::GlobalFolding,
477                    enable_zne: true,
478                    noise_scaling_factors: vec![1.0, 1.5, 2.0],
479                    folding: mitigation::FoldingConfig {
480                        folding_type: mitigation::FoldingType::Global,
481                        global_folding: true,
482                        local_folding: mitigation::LocalFoldingConfig {
483                            regions: vec![],
484                            selection_strategy: mitigation::RegionSelectionStrategy::Adaptive,
485                            overlap_handling: mitigation::OverlapHandling::Ignore,
486                        },
487                        gate_specific: mitigation::GateSpecificFoldingConfig {
488                            folding_rules: std::collections::HashMap::new(),
489                            priority_ordering: vec![],
490                            error_rate_weighting: false,
491                            folding_strategies: std::collections::HashMap::new(),
492                            default_strategy: mitigation::DefaultFoldingStrategy::Identity,
493                            prioritized_gates: vec![],
494                        },
495                    },
496                    richardson: mitigation::RichardsonConfig {
497                        enable_richardson: false,
498                        order: 2,
499                        stability_check: true,
500                        error_estimation: mitigation::ErrorEstimationConfig {
501                            method: mitigation::ErrorEstimationMethod::Bootstrap,
502                            bootstrap_samples: 100,
503                            confidence_level: 0.95,
504                        },
505                    },
506                },
507                readout_mitigation: mitigation::ReadoutMitigationConfig {
508                    enable_mitigation: true,
509                    methods: vec![],
510                    calibration: mitigation::ReadoutCalibrationConfig {
511                        frequency: mitigation::CalibrationFrequency::Periodic(
512                            std::time::Duration::from_secs(3600),
513                        ),
514                        states: vec![],
515                        quality_metrics: vec![],
516                    },
517                    matrix_inversion: mitigation::MatrixInversionConfig {
518                        method: mitigation::InversionMethod::PseudoInverse,
519                        regularization: mitigation::RegularizationConfig {
520                            regularization_type: mitigation::RegularizationType::L2,
521                            parameter: 0.001,
522                            adaptive: false,
523                        },
524                        stability: mitigation::NumericalStabilityConfig {
525                            condition_threshold: 1e-12,
526                            pivoting: mitigation::PivotingStrategy::Partial,
527                            scaling: true,
528                        },
529                    },
530                    tensored_mitigation: mitigation::TensoredMitigationConfig {
531                        groups: vec![],
532                        group_strategy: mitigation::GroupFormationStrategy::Topology,
533                        crosstalk_handling: mitigation::CrosstalkHandling::Ignore,
534                    },
535                },
536                gate_mitigation: mitigation::GateMitigationConfig {
537                    enable_mitigation: true,
538                    gate_configs: std::collections::HashMap::new(),
539                    twirling: mitigation::TwirlingConfig {
540                        enable_twirling: true,
541                        twirling_type: mitigation::TwirlingType::Pauli,
542                        groups: vec![],
543                        randomization: mitigation::RandomizationStrategy::FullRandomization,
544                    },
545                    randomized_compiling: mitigation::RandomizedCompilingConfig {
546                        enable_rc: true,
547                        strategies: vec![],
548                        replacement_rules: std::collections::HashMap::new(),
549                        randomization_level: mitigation::RandomizationLevel::Medium,
550                    },
551                },
552                symmetry_verification: mitigation::SymmetryVerificationConfig {
553                    enable_verification: true,
554                    symmetry_types: vec![],
555                    protocols: vec![],
556                    tolerance: mitigation::ToleranceSettings {
557                        symmetry_tolerance: 0.01,
558                        statistical_tolerance: 0.05,
559                        confidence_level: 0.95,
560                    },
561                },
562                virtual_distillation: mitigation::VirtualDistillationConfig {
563                    enable_distillation: true,
564                    protocols: vec![],
565                    resources: mitigation::ResourceRequirements {
566                        auxiliary_qubits: 2,
567                        measurement_rounds: 3,
568                        classical_processing: mitigation::ProcessingRequirements {
569                            memory_mb: 1024,
570                            computation_time: std::time::Duration::from_millis(100),
571                            parallel_processing: false,
572                        },
573                    },
574                    quality_metrics: vec![],
575                },
576            },
577            adaptive_qec: adaptive::AdaptiveQECConfig {
578                enable_real_time_adaptation: true,
579                adaptation_window: Duration::from_secs(60),
580                performance_threshold: 0.95,
581                enable_threshold_adaptation: true,
582                enable_strategy_switching: true,
583                learning_rate: 0.01,
584                enable_adaptive: true,
585                strategies: vec![],
586                learning: adaptive::AdaptiveLearningConfig {
587                    algorithms: vec![],
588                    online_learning: adaptive::OnlineLearningConfig {
589                        enable_online: true,
590                        learning_rate_adaptation: adaptive::LearningRateAdaptation::Adaptive,
591                        concept_drift: adaptive::ConceptDriftConfig {
592                            enable_detection: false,
593                            methods: vec![],
594                            responses: vec![],
595                        },
596                        model_updates: adaptive::ModelUpdateConfig {
597                            frequency: adaptive::UpdateFrequency::EventTriggered,
598                            triggers: vec![],
599                            strategies: vec![],
600                        },
601                    },
602                    transfer_learning: adaptive::TransferLearningConfig {
603                        enable_transfer: false,
604                        source_domains: vec![],
605                        strategies: vec![],
606                        domain_adaptation: adaptive::DomainAdaptationConfig {
607                            methods: vec![],
608                            validation: vec![],
609                        },
610                    },
611                    meta_learning: adaptive::MetaLearningConfig {
612                        enable_meta: false,
613                        algorithms: vec![],
614                        task_distribution: adaptive::TaskDistributionConfig {
615                            task_types: vec![],
616                            complexity_range: (0.0, 1.0),
617                            generation_strategy: adaptive::TaskGenerationStrategy::Random,
618                        },
619                        meta_optimization: adaptive::MetaOptimizationConfig {
620                            optimizer: adaptive::MetaOptimizer::Adam,
621                            learning_rates: adaptive::LearningRates {
622                                inner_lr: 0.01,
623                                outer_lr: 0.001,
624                                adaptive: true,
625                            },
626                            regularization: adaptive::MetaRegularization {
627                                regularization_type: adaptive::RegularizationType::L2,
628                                strength: 0.001,
629                            },
630                        },
631                    },
632                },
633                realtime_optimization: adaptive::RealtimeOptimizationConfig {
634                    enable_realtime: true,
635                    objectives: vec![],
636                    algorithms: vec![],
637                    constraints: adaptive::ResourceConstraints {
638                        time_limit: std::time::Duration::from_millis(100),
639                        memory_limit: 1024 * 1024,
640                        power_budget: 100.0,
641                        hardware_constraints: adaptive::HardwareConstraints {
642                            connectivity: adaptive::ConnectivityConstraints {
643                                coupling_map: vec![],
644                                max_distance: 10,
645                                routing_overhead: 1.2,
646                            },
647                            gate_fidelities: std::collections::HashMap::new(),
648                            coherence_times: adaptive::CoherenceTimes {
649                                t1_times: std::collections::HashMap::new(),
650                                t2_times: std::collections::HashMap::new(),
651                                gate_times: std::collections::HashMap::new(),
652                            },
653                        },
654                    },
655                },
656                feedback_control: adaptive::FeedbackControlConfig {
657                    enable_feedback: true,
658                    algorithms: vec![],
659                    sensors: adaptive::SensorConfig {
660                        sensor_types: vec![],
661                        sampling_rates: std::collections::HashMap::new(),
662                        noise_characteristics: adaptive::NoiseCharacteristics {
663                            gaussian_noise: 0.01,
664                            systematic_bias: 0.0,
665                            temporal_correlation: 0.1,
666                        },
667                    },
668                    actuators: adaptive::ActuatorConfig {
669                        actuator_types: vec![],
670                        response_times: std::collections::HashMap::new(),
671                        control_ranges: std::collections::HashMap::new(),
672                    },
673                },
674                prediction: adaptive::PredictionConfig::default(),
675                optimization: adaptive::OptimizationConfig::default(),
676            },
677            performance_optimization: QECOptimizationConfig {
678                enable_optimization: true,
679                enable_code_optimization: true,
680                enable_layout_optimization: true,
681                enable_scheduling_optimization: true,
682                optimization_algorithm:
683                    crate::unified_benchmarking::config::OptimizationAlgorithm::GradientDescent,
684                optimization_objectives: vec![],
685                constraint_satisfaction: ConstraintSatisfactionConfig {
686                    hardware_constraints: vec![],
687                    resource_constraints: vec![],
688                    performance_constraints: vec![],
689                },
690                targets: vec![],
691                metrics: vec![],
692                strategies: vec![],
693            },
694            ml_config: QECMLConfig {
695                model_type: crate::unified_benchmarking::config::MLModelType::NeuralNetwork,
696                training_data_size: 10000,
697                validation_split: 0.2,
698                enable_online_learning: true,
699                feature_extraction: crate::ml_optimization::FeatureExtractionConfig {
700                    enable_syndrome_history: true,
701                    history_length: 100,
702                    enable_spatial_features: true,
703                    enable_temporal_features: true,
704                    enable_correlation_features: true,
705                    enable_auto_extraction: true,
706                    circuit_features: crate::ml_optimization::CircuitFeatureConfig {
707                        basic_properties: true,
708                        gate_distributions: true,
709                        depth_analysis: true,
710                        connectivity_patterns: true,
711                        entanglement_measures: false,
712                        symmetry_analysis: false,
713                        critical_path_analysis: false,
714                    },
715                    hardware_features: crate::ml_optimization::HardwareFeatureConfig {
716                        topology_features: true,
717                        calibration_features: true,
718                        error_rate_features: true,
719                        timing_features: false,
720                        resource_features: false,
721                        environmental_features: false,
722                    },
723                    temporal_features: crate::ml_optimization::TemporalFeatureConfig {
724                        time_series_analysis: true,
725                        trend_detection: true,
726                        seasonality_analysis: false,
727                        autocorrelation_features: false,
728                        fourier_features: false,
729                    },
730                    statistical_features: crate::ml_optimization::StatisticalFeatureConfig {
731                        moment_features: true,
732                        distribution_fitting: false,
733                        correlation_features: true,
734                        outlier_features: false,
735                        normality_tests: false,
736                    },
737                    graph_features: crate::ml_optimization::GraphFeatureConfig {
738                        centrality_measures: false,
739                        community_features: false,
740                        spectral_features: false,
741                        path_features: false,
742                        clustering_features: false,
743                    },
744                    feature_selection: crate::ml_optimization::FeatureSelectionConfig {
745                        enable_selection: true,
746                        selection_methods: vec![
747                            crate::ml_optimization::FeatureSelectionMethod::VarianceThreshold,
748                        ],
749                        num_features: Some(50),
750                        selection_threshold: 0.01,
751                    },
752                    dimensionality_reduction:
753                        crate::ml_optimization::DimensionalityReductionConfig {
754                            enable_reduction: false,
755                            reduction_methods: vec![],
756                            target_dimensions: None,
757                            variance_threshold: 0.95,
758                        },
759                },
760                model_update_frequency: Duration::from_secs(3600),
761                enable_ml: true,
762                models: vec![],
763                training: MLTrainingConfig {
764                    batch_size: 32,
765                    learning_rate: 0.001,
766                    epochs: 100,
767                    optimization_algorithm: "adam".to_string(),
768                    data: TrainingDataConfig {
769                        sources: vec![],
770                        preprocessing: DataPreprocessingConfig {
771                            normalization: NormalizationMethod::ZScore,
772                            feature_selection: FeatureSelectionMethod::Statistical,
773                            dimensionality_reduction: DimensionalityReductionMethod::PCA,
774                        },
775                        augmentation: DataAugmentationConfig {
776                            enable: false,
777                            techniques: vec![],
778                            ratio: 1.0,
779                        },
780                    },
781                    architecture: ModelArchitectureConfig {
782                        architecture_type: ArchitectureType::Sequential,
783                        layers: vec![LayerConfig {
784                            layer_type: LayerType::Dense,
785                            parameters: [("neurons".to_string(), 128.0)].iter().cloned().collect(),
786                            activation: ActivationFunction::ReLU,
787                        }],
788                        connections: ConnectionPattern::FullyConnected,
789                    },
790                    parameters: TrainingParameters {
791                        optimizer: OptimizerType::Adam,
792                        loss_function: LossFunction::MeanSquaredError,
793                        regularization_strength: 0.01,
794                        learning_rate: 0.001,
795                        batch_size: 32,
796                        epochs: 100,
797                    },
798                    validation: adaptive::ValidationConfig {
799                        method: adaptive::ValidationMethod::HoldOut,
800                        split: 0.2,
801                        cv_folds: 5,
802                    },
803                },
804                inference: MLInferenceConfig {
805                    mode: InferenceMode::Synchronous,
806                    batch_processing: BatchProcessingConfig {
807                        enable: false,
808                        batch_size: 32,
809                        timeout: std::time::Duration::from_secs(30),
810                    },
811                    timeout: std::time::Duration::from_secs(30),
812                    caching: CachingConfig {
813                        enable: true,
814                        cache_size: 512,
815                        ttl: std::time::Duration::from_secs(3600),
816                        eviction_policy: adaptive::CacheEvictionPolicy::LRU,
817                    },
818                    optimization: InferenceOptimizationConfig {
819                        enable_optimization: true,
820                        optimization_strategies: vec!["model_pruning".to_string()],
821                        performance_targets: vec!["latency".to_string()],
822                        model_optimization: ModelOptimization::None,
823                        hardware_acceleration: HardwareAcceleration::CPU,
824                        caching: InferenceCaching {
825                            enable: false,
826                            cache_size: 1000,
827                            eviction_policy: adaptive::CacheEvictionPolicy::LRU,
828                        },
829                    },
830                },
831                model_management: ModelManagementConfig {
832                    versioning: ModelVersioning {
833                        enable: false,
834                        version_control: VersionControlSystem::Git,
835                        rollback: RollbackStrategy::Manual,
836                    },
837                    deployment: ModelDeployment {
838                        strategy: DeploymentStrategy::BlueGreen,
839                        environment: EnvironmentConfig {
840                            environment_type: EnvironmentType::Development,
841                            resources: ResourceAllocation {
842                                cpu: 1.0,
843                                memory: 1024,
844                                gpu: None,
845                            },
846                            dependencies: vec![],
847                        },
848                        scaling: ScalingConfig {
849                            auto_scaling: false,
850                            min_replicas: 1,
851                            max_replicas: 3,
852                            metrics: vec![],
853                        },
854                    },
855                    monitoring: ModelMonitoring {
856                        performance: PerformanceMonitoring {
857                            metrics: vec![],
858                            frequency: std::time::Duration::from_secs(60),
859                            baseline_comparison: false,
860                        },
861                        drift_detection: DriftDetection {
862                            enable: false,
863                            methods: vec![],
864                            sensitivity: 0.05,
865                        },
866                        alerting: AlertingConfig {
867                            channels: vec![],
868                            thresholds: std::collections::HashMap::new(),
869                            escalation: EscalationRules {
870                                levels: vec![],
871                                timeouts: std::collections::HashMap::new(),
872                            },
873                        },
874                    },
875                },
876                optimization: create_stub_ml_optimization_config(),
877                validation: create_default_validation_config(),
878            },
879            monitoring_config: QECMonitoringConfig {
880                enable_performance_tracking: true,
881                enable_error_analysis: true,
882                enable_resource_monitoring: true,
883                reporting_interval: Duration::from_secs(60),
884                enable_predictive_analytics: false,
885                enable_monitoring: true,
886                targets: vec![],
887                dashboard: DashboardConfig {
888                    enable: true,
889                    components: vec![],
890                    update_frequency: std::time::Duration::from_secs(5),
891                    access_control: AccessControl {
892                        authentication: false,
893                        roles: vec![],
894                        permissions: std::collections::HashMap::new(),
895                    },
896                },
897                data_collection: DataCollectionConfig {
898                    frequency: std::time::Duration::from_secs(1),
899                    retention: DataRetention {
900                        period: std::time::Duration::from_secs(3600 * 24 * 30),
901                        archival: ArchivalStrategy::CloudStorage,
902                        compression: false,
903                    },
904                    storage: StorageConfig {
905                        backend: StorageBackend::FileSystem,
906                        replication: 1,
907                        consistency: ConsistencyLevel::Eventual,
908                    },
909                },
910                alerting: MonitoringAlertingConfig {
911                    rules: vec![],
912                    channels: vec![],
913                    suppression: AlertSuppression {
914                        enable: false,
915                        rules: vec![],
916                        default_time: std::time::Duration::from_secs(300),
917                    },
918                },
919            },
920        }
921    }
922}
923
924// Additional configuration types needed for test compatibility
925
926// SyndromeDetectionConfig is now defined in detection module
927
928/// Training data configuration
929#[derive(Debug, Clone, Serialize, Deserialize)]
930pub struct TrainingDataConfig {
931    pub sources: Vec<DataSource>,
932    pub preprocessing: DataPreprocessingConfig,
933    pub augmentation: DataAugmentationConfig,
934}
935
936/// Model architecture configuration
937#[derive(Debug, Clone, Serialize, Deserialize)]
938pub struct ModelArchitectureConfig {
939    pub architecture_type: ArchitectureType,
940    pub layers: Vec<LayerConfig>,
941    pub connections: ConnectionPattern,
942}
943
944/// Training parameters
945#[derive(Debug, Clone, Serialize, Deserialize)]
946pub struct TrainingParameters {
947    pub optimizer: adaptive::OptimizerType,
948    pub loss_function: adaptive::LossFunction,
949    pub regularization_strength: f64,
950    pub learning_rate: f64,
951    pub batch_size: usize,
952    pub epochs: usize,
953}
954
955/// ML Training configuration
956#[derive(Debug, Clone, Serialize, Deserialize)]
957pub struct MLTrainingConfig {
958    pub batch_size: usize,
959    pub learning_rate: f64,
960    pub epochs: usize,
961    pub optimization_algorithm: String,
962    pub data: TrainingDataConfig,
963    pub architecture: ModelArchitectureConfig,
964    pub parameters: TrainingParameters,
965    pub validation: adaptive::ValidationConfig,
966}
967
968/// ML Inference configuration
969#[derive(Debug, Clone, Serialize, Deserialize)]
970pub struct MLInferenceConfig {
971    pub mode: InferenceMode,
972    pub batch_processing: BatchProcessingConfig,
973    pub timeout: Duration,
974    pub caching: CachingConfig,
975    pub optimization: InferenceOptimizationConfig,
976}
977
978/// Inference modes
979#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
980pub enum InferenceMode {
981    Synchronous,
982    Asynchronous,
983    Streaming,
984}
985
986/// Batch processing configuration
987#[derive(Debug, Clone, Serialize, Deserialize)]
988pub struct BatchProcessingConfig {
989    pub enable: bool,
990    pub batch_size: usize,
991    pub timeout: Duration,
992}
993
994/// Caching configuration
995#[derive(Debug, Clone, Serialize, Deserialize)]
996pub struct CachingConfig {
997    pub enable: bool,
998    pub cache_size: usize,
999    pub ttl: Duration,
1000    pub eviction_policy: adaptive::CacheEvictionPolicy,
1001}
1002
1003/// Inference optimization configuration
1004#[derive(Debug, Clone, Serialize, Deserialize)]
1005pub struct InferenceOptimizationConfig {
1006    pub enable_optimization: bool,
1007    pub optimization_strategies: Vec<String>,
1008    pub performance_targets: Vec<String>,
1009    pub model_optimization: adaptive::ModelOptimization,
1010    pub hardware_acceleration: adaptive::HardwareAcceleration,
1011    pub caching: adaptive::InferenceCaching,
1012}
1013
1014/// QEC ML configuration
1015#[derive(Debug, Clone, Serialize, Deserialize)]
1016pub struct QECMLConfig {
1017    pub model_type: crate::unified_benchmarking::config::MLModelType,
1018    pub training_data_size: usize,
1019    pub validation_split: f64,
1020    pub enable_online_learning: bool,
1021    pub feature_extraction: crate::ml_optimization::FeatureExtractionConfig,
1022    pub model_update_frequency: Duration,
1023    // Additional fields for full compatibility
1024    pub enable_ml: bool,
1025    pub inference: MLInferenceConfig,
1026    pub model_management: adaptive::ModelManagementConfig,
1027    pub optimization: crate::ml_optimization::MLOptimizationConfig,
1028    pub validation: crate::ml_optimization::ValidationConfig,
1029    pub models: Vec<String>,
1030    pub training: MLTrainingConfig,
1031}
1032
1033/// QEC monitoring configuration
1034#[derive(Debug, Clone, Serialize, Deserialize)]
1035pub struct QECMonitoringConfig {
1036    pub enable_performance_tracking: bool,
1037    pub enable_error_analysis: bool,
1038    pub enable_resource_monitoring: bool,
1039    pub reporting_interval: Duration,
1040    pub enable_predictive_analytics: bool,
1041    // Additional fields already defined in the complex struct above
1042    pub enable_monitoring: bool,
1043    pub targets: Vec<String>,
1044    pub dashboard: DashboardConfig,
1045    pub data_collection: DataCollectionConfig,
1046    pub alerting: MonitoringAlertingConfig,
1047}
1048
1049/// QEC optimization configuration
1050#[derive(Debug, Clone, Serialize, Deserialize)]
1051pub struct QECOptimizationConfig {
1052    pub enable_code_optimization: bool,
1053    pub enable_layout_optimization: bool,
1054    pub enable_scheduling_optimization: bool,
1055    pub optimization_algorithm: crate::unified_benchmarking::config::OptimizationAlgorithm,
1056    pub optimization_objectives: Vec<OptimizationObjective>,
1057    pub constraint_satisfaction: ConstraintSatisfactionConfig,
1058    pub enable_optimization: bool,
1059    pub targets: Vec<String>,
1060    pub metrics: Vec<String>,
1061    pub strategies: Vec<String>,
1062}
1063
1064/// Optimization objectives for QEC
1065#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1066pub enum OptimizationObjective {
1067    MaximizeLogicalFidelity,
1068    MinimizeOverhead,
1069    MinimizeLatency,
1070    MinimizeResourceUsage,
1071    MaximizeThroughput,
1072}
1073
1074/// Constraint satisfaction configuration
1075#[derive(Debug, Clone, Serialize, Deserialize)]
1076pub struct ConstraintSatisfactionConfig {
1077    pub hardware_constraints: Vec<HardwareConstraint>,
1078    pub resource_constraints: Vec<ResourceConstraint>,
1079    pub performance_constraints: Vec<PerformanceConstraint>,
1080}
1081
1082/// Hardware constraints
1083#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1084pub enum HardwareConstraint {
1085    ConnectivityGraph,
1086    GateTimes,
1087    ErrorRates,
1088    CoherenceTimes,
1089    CouplingStrengths,
1090}
1091
1092/// Resource constraints
1093#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1094pub enum ResourceConstraint {
1095    QubitCount,
1096    CircuitDepth,
1097    ExecutionTime,
1098    MemoryUsage,
1099    PowerConsumption,
1100}
1101
1102/// Performance constraints
1103#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
1104pub enum PerformanceConstraint {
1105    LogicalErrorRate,
1106    ThroughputTarget,
1107    LatencyBound,
1108    FidelityThreshold,
1109    SuccessRate,
1110}
1111
1112// Additional helper structs for config compatibility
1113
1114#[derive(Debug, Clone, Serialize, Deserialize)]
1115pub struct FeedbackControlConfig {
1116    pub enable_feedback: bool,
1117    pub control_loop_frequency: Duration,
1118}
1119
1120#[derive(Debug, Clone, Serialize, Deserialize)]
1121pub struct LearningConfig {
1122    pub algorithms: Vec<String>,
1123    pub hyperparameters: std::collections::HashMap<String, f64>,
1124}
1125
1126// Default implementations for helper configs
1127
1128impl Default for FeedbackControlConfig {
1129    fn default() -> Self {
1130        Self {
1131            enable_feedback: true,
1132            control_loop_frequency: Duration::from_millis(100),
1133        }
1134    }
1135}
1136
1137impl Default for LearningConfig {
1138    fn default() -> Self {
1139        Self {
1140            algorithms: vec!["gradient_descent".to_string()],
1141            hyperparameters: std::collections::HashMap::new(),
1142        }
1143    }
1144}
1145
1146// Additional configuration types for QEC compatibility
1147
1148/// Pattern recognition configuration
1149#[derive(Debug, Clone, Serialize, Deserialize)]
1150pub struct PatternRecognitionConfig {
1151    pub enable_recognition: bool,
1152    pub recognition_methods: Vec<String>,
1153    pub confidence_threshold: f64,
1154    pub ml_model_path: Option<String>,
1155}
1156
1157/// Statistical analysis configuration
1158#[derive(Debug, Clone, Serialize, Deserialize)]
1159pub struct StatisticalAnalysisConfig {
1160    pub enable_statistics: bool,
1161    pub analysis_methods: Vec<String>,
1162    pub statistical_tests: Vec<String>,
1163    pub significance_level: f64,
1164}
1165
1166/// Noise scaling configuration
1167#[derive(Debug, Clone, Serialize, Deserialize)]
1168pub struct NoiseScalingConfig {
1169    pub scaling_factors: Vec<f64>,
1170    pub scaling_methods: Vec<String>,
1171    pub max_scaling: f64,
1172}
1173
1174// Default implementations for new config types
1175
1176impl Default for PatternRecognitionConfig {
1177    fn default() -> Self {
1178        Self {
1179            enable_recognition: true,
1180            recognition_methods: vec!["neural_network".to_string()],
1181            confidence_threshold: 0.9,
1182            ml_model_path: None,
1183        }
1184    }
1185}
1186
1187impl Default for StatisticalAnalysisConfig {
1188    fn default() -> Self {
1189        Self {
1190            enable_statistics: true,
1191            analysis_methods: vec!["correlation".to_string(), "trend_analysis".to_string()],
1192            statistical_tests: vec!["chi_square".to_string()],
1193            significance_level: 0.05,
1194        }
1195    }
1196}
1197
1198impl Default for NoiseScalingConfig {
1199    fn default() -> Self {
1200        Self {
1201            scaling_factors: vec![1.0, 1.5, 2.0, 2.5, 3.0],
1202            scaling_methods: vec!["folding".to_string()],
1203            max_scaling: 5.0,
1204        }
1205    }
1206}
1207
1208// Simplified helper functions for creating basic ML configurations
1209fn create_stub_ml_optimization_config() -> crate::ml_optimization::MLOptimizationConfig {
1210    // Create a minimal configuration using default implementations
1211    crate::ml_optimization::MLOptimizationConfig {
1212        enable_optimization: true,
1213        model_config: crate::ml_optimization::MLModelConfig {
1214            primary_algorithms: vec![crate::ml_optimization::MLAlgorithm::DeepNeuralNetwork],
1215            fallback_algorithms: vec![crate::ml_optimization::MLAlgorithm::RandomForest],
1216            hyperparameters: std::collections::HashMap::new(),
1217            training_config: crate::ml_optimization::TrainingConfig {
1218                max_iterations: 100,
1219                learning_rate: 0.001,
1220                batch_size: 32,
1221                early_stopping: crate::ml_optimization::EarlyStoppingConfig {
1222                    enable_early_stopping: false,
1223                    patience: 10,
1224                    min_improvement: 0.001,
1225                    restore_best_weights: true,
1226                },
1227                cv_folds: 5,
1228                train_test_split: 0.8,
1229                optimizer: crate::ml_optimization::TrainingOptimizer::Adam,
1230            },
1231            model_selection: crate::ml_optimization::ModelSelectionStrategy::CrossValidation,
1232            regularization: crate::ml_optimization::RegularizationConfig {
1233                l1_lambda: 0.0,
1234                l2_lambda: 0.01,
1235                dropout_rate: 0.0,
1236                batch_normalization: false,
1237                weight_decay: 0.0,
1238            },
1239        },
1240        feature_extraction: create_stub_feature_extraction_config(),
1241        hardware_prediction: create_stub_hardware_prediction_config(),
1242        online_learning: create_stub_online_learning_config(),
1243        transfer_learning: create_stub_transfer_learning_config(),
1244        ensemble_config: create_stub_ensemble_config(),
1245        optimization_strategy: create_stub_optimization_strategy_config(),
1246        validation_config: crate::ml_optimization::validation::MLValidationConfig::default(),
1247        monitoring_config: create_stub_ml_monitoring_config(),
1248    }
1249}
1250
1251fn create_stub_feature_extraction_config() -> crate::ml_optimization::FeatureExtractionConfig {
1252    crate::ml_optimization::FeatureExtractionConfig {
1253        enable_syndrome_history: false,
1254        history_length: 5,
1255        enable_spatial_features: false,
1256        enable_temporal_features: false,
1257        enable_correlation_features: false,
1258        enable_auto_extraction: false,
1259        circuit_features: crate::ml_optimization::features::CircuitFeatureConfig {
1260            basic_properties: false,
1261            gate_distributions: false,
1262            depth_analysis: false,
1263            connectivity_patterns: false,
1264            entanglement_measures: false,
1265            symmetry_analysis: false,
1266            critical_path_analysis: false,
1267        },
1268        hardware_features: crate::ml_optimization::features::HardwareFeatureConfig {
1269            topology_features: false,
1270            calibration_features: false,
1271            error_rate_features: false,
1272            timing_features: false,
1273            resource_features: false,
1274            environmental_features: false,
1275        },
1276        temporal_features: crate::ml_optimization::features::TemporalFeatureConfig {
1277            time_series_analysis: false,
1278            trend_detection: false,
1279            seasonality_analysis: false,
1280            autocorrelation_features: false,
1281            fourier_features: false,
1282        },
1283        statistical_features: crate::ml_optimization::features::StatisticalFeatureConfig {
1284            moment_features: false,
1285            distribution_fitting: false,
1286            correlation_features: false,
1287            outlier_features: false,
1288            normality_tests: false,
1289        },
1290        graph_features: crate::ml_optimization::features::GraphFeatureConfig {
1291            centrality_measures: false,
1292            community_features: false,
1293            spectral_features: false,
1294            path_features: false,
1295            clustering_features: false,
1296        },
1297        feature_selection: crate::ml_optimization::features::FeatureSelectionConfig {
1298            enable_selection: false,
1299            selection_methods: vec![
1300                crate::ml_optimization::features::FeatureSelectionMethod::VarianceThreshold,
1301            ],
1302            num_features: None,
1303            selection_threshold: 0.05,
1304        },
1305        dimensionality_reduction: crate::ml_optimization::features::DimensionalityReductionConfig {
1306            enable_reduction: false,
1307            reduction_methods: vec![],
1308            target_dimensions: None,
1309            variance_threshold: 0.95,
1310        },
1311    }
1312}
1313
1314const fn create_stub_hardware_prediction_config() -> crate::ml_optimization::HardwarePredictionConfig
1315{
1316    crate::ml_optimization::HardwarePredictionConfig {
1317        enable_prediction: false,
1318        prediction_targets: vec![],
1319        prediction_horizon: std::time::Duration::from_secs(300),
1320        uncertainty_quantification: false,
1321        multi_step_prediction: false,
1322        hardware_adaptation: crate::ml_optimization::hardware::HardwareAdaptationConfig {
1323            enable_adaptation: false,
1324            adaptation_frequency: std::time::Duration::from_secs(3600),
1325            adaptation_triggers: vec![],
1326            learning_rate_adaptation: false,
1327        },
1328    }
1329}
1330
1331const fn create_stub_online_learning_config() -> crate::ml_optimization::OnlineLearningConfig {
1332    crate::ml_optimization::OnlineLearningConfig {
1333        enable_online_learning: false,
1334        learning_rate_schedule:
1335            crate::ml_optimization::online_learning::LearningRateSchedule::Constant,
1336        memory_management: crate::ml_optimization::online_learning::MemoryManagementConfig {
1337            max_buffer_size: 1000,
1338            eviction_strategy: crate::ml_optimization::online_learning::MemoryEvictionStrategy::LRU,
1339            replay_buffer: false,
1340            experience_prioritization: false,
1341        },
1342        forgetting_prevention:
1343            crate::ml_optimization::online_learning::ForgettingPreventionConfig {
1344                elastic_weight_consolidation: false,
1345                progressive_networks: false,
1346                memory_replay: false,
1347                regularization_strength: 0.0,
1348            },
1349        incremental_learning: crate::ml_optimization::online_learning::IncrementalLearningConfig {
1350            incremental_batch_size: 32,
1351            update_frequency: std::time::Duration::from_secs(300),
1352            stability_plasticity_balance: 0.5,
1353            knowledge_distillation: false,
1354        },
1355    }
1356}
1357
1358const fn create_stub_transfer_learning_config() -> crate::ml_optimization::TransferLearningConfig {
1359    crate::ml_optimization::TransferLearningConfig {
1360        enable_transfer_learning: false,
1361        source_domains: vec![],
1362        transfer_methods: vec![],
1363        domain_adaptation: crate::ml_optimization::DomainAdaptationConfig {
1364            enable_adaptation: false,
1365            adaptation_methods: vec![],
1366            similarity_threshold: 0.5,
1367            max_domain_gap: 1.0,
1368        },
1369        meta_learning: crate::ml_optimization::MetaLearningConfig {
1370            enable_meta_learning: false,
1371            meta_algorithms: vec![],
1372            inner_loop_iterations: 1,
1373            outer_loop_iterations: 1,
1374        },
1375    }
1376}
1377
1378const fn create_stub_ensemble_config() -> crate::ml_optimization::EnsembleConfig {
1379    crate::ml_optimization::EnsembleConfig {
1380        enable_ensemble: false,
1381        ensemble_methods: vec![],
1382        num_models: 1,
1383        voting_strategy: crate::ml_optimization::VotingStrategy::Majority,
1384        diversity_measures: vec![],
1385        dynamic_selection: false,
1386    }
1387}
1388
1389fn create_stub_optimization_strategy_config() -> crate::ml_optimization::OptimizationStrategyConfig
1390{
1391    crate::ml_optimization::OptimizationStrategyConfig {
1392        constraint_handling: crate::ml_optimization::optimization::ConstraintHandlingConfig {
1393            constraint_types: vec![crate::ml_optimization::optimization::ConstraintType::Box],
1394            penalty_methods: vec![
1395                crate::ml_optimization::optimization::PenaltyMethod::ExteriorPenalty,
1396            ],
1397            constraint_tolerance: 0.1,
1398            feasibility_preservation: false,
1399        },
1400        search_strategies: vec![],
1401        exploration_exploitation: crate::ml_optimization::ExplorationExploitationConfig {
1402            initial_exploration_rate: 0.1,
1403            exploration_decay: 0.95,
1404            min_exploration_rate: 0.01,
1405            exploitation_threshold: 0.9,
1406            adaptive_balancing: false,
1407        },
1408        adaptive_strategies: crate::ml_optimization::AdaptiveStrategyConfig {
1409            enable_adaptive: false,
1410            strategy_selection: vec![],
1411            performance_feedback: false,
1412            strategy_mutation: false,
1413        },
1414        multi_objective: crate::ml_optimization::MultiObjectiveConfig {
1415            enable_multi_objective: false,
1416            objectives: std::collections::HashMap::new(),
1417            pareto_optimization: false,
1418            scalarization_methods: vec![],
1419        },
1420    }
1421}
1422
1423fn create_stub_ml_monitoring_config() -> crate::ml_optimization::MLMonitoringConfig {
1424    crate::ml_optimization::MLMonitoringConfig {
1425        enable_real_time_monitoring: false,
1426        performance_tracking: false,
1427        drift_detection: crate::ml_optimization::DriftDetectionConfig {
1428            enable_detection: false,
1429            detection_methods: vec![],
1430            significance_threshold: 0.05,
1431            window_size: 100,
1432        },
1433        anomaly_detection: false,
1434        alert_thresholds: std::collections::HashMap::new(),
1435    }
1436}
1437
1438fn create_default_validation_config() -> crate::ml_optimization::validation::MLValidationConfig {
1439    crate::ml_optimization::validation::MLValidationConfig::default()
1440}
quantrs2_device/qec/config.rs

quantrs2_device/qec/
config.rs
