quantrs2_tytan/
ai_assisted_optimization.rs

1//! AI-Assisted Quantum Optimization
2//!
3//! This module provides machine learning enhanced quantum optimization algorithms
4//! including neural networks for parameter optimization, reinforcement learning
5//! for sampling strategies, and automated algorithm selection.
6
7#![allow(dead_code)]
8
9use scirs2_core::ndarray::{Array1, Array2};
10use serde::{Deserialize, Serialize};
11use std::collections::{HashMap, VecDeque};
12use std::time::{Duration, Instant};
13
14/// AI-assisted quantum optimization engine
15pub struct AIAssistedOptimizer {
16    /// Neural network for parameter optimization
17    parameter_optimizer: ParameterOptimizationNetwork,
18    /// Reinforcement learning agent for sampling strategies
19    rl_agent: SamplingStrategyAgent,
20    /// Automated algorithm selector
21    algorithm_selector: AutomatedAlgorithmSelector,
22    /// Problem structure recognition system
23    structure_recognizer: ProblemStructureRecognizer,
24    /// Solution quality predictor
25    quality_predictor: SolutionQualityPredictor,
26    /// Configuration
27    config: AIOptimizerConfig,
28}
29
30#[derive(Debug, Clone, Serialize, Deserialize)]
31pub struct AIOptimizerConfig {
32    /// Enable parameter optimization
33    pub parameter_optimization_enabled: bool,
34    /// Enable reinforcement learning
35    pub reinforcement_learning_enabled: bool,
36    /// Enable automated algorithm selection
37    pub auto_algorithm_selection_enabled: bool,
38    /// Enable problem structure recognition
39    pub structure_recognition_enabled: bool,
40    /// Enable solution quality prediction
41    pub quality_prediction_enabled: bool,
42    /// Learning rate for neural networks
43    pub learning_rate: f64,
44    /// Training batch size
45    pub batch_size: usize,
46    /// Maximum training iterations
47    pub max_training_iterations: usize,
48    /// Convergence threshold
49    pub convergence_threshold: f64,
50    /// Experience replay buffer size
51    pub replay_buffer_size: usize,
52}
53
54impl Default for AIOptimizerConfig {
55    fn default() -> Self {
56        Self {
57            parameter_optimization_enabled: true,
58            reinforcement_learning_enabled: true,
59            auto_algorithm_selection_enabled: true,
60            structure_recognition_enabled: true,
61            quality_prediction_enabled: true,
62            learning_rate: 0.001,
63            batch_size: 32,
64            max_training_iterations: 1000,
65            convergence_threshold: 1e-6,
66            replay_buffer_size: 10000,
67        }
68    }
69}
70
71/// Neural network for optimizing quantum algorithm parameters
72pub struct ParameterOptimizationNetwork {
73    /// Network layers
74    layers: Vec<DenseLayer>,
75    /// Optimizer
76    optimizer: Optimizer,
77    /// Training history
78    training_history: TrainingHistory,
79    /// Current best parameters
80    best_parameters: Option<Array1<f64>>,
81}
82
83#[derive(Debug, Clone)]
84pub struct DenseLayer {
85    /// Weights
86    pub weights: Array2<f64>,
87    /// Biases
88    pub biases: Array1<f64>,
89    /// Activation function
90    pub activation: ActivationFunction,
91}
92
93#[derive(Debug, Clone, Serialize, Deserialize)]
94pub enum ActivationFunction {
95    ReLU,
96    Tanh,
97    Sigmoid,
98    LeakyReLU { alpha: f64 },
99    ELU { alpha: f64 },
100    Swish,
101}
102
103#[derive(Debug, Clone)]
104pub struct Optimizer {
105    /// Optimizer type
106    pub optimizer_type: OptimizerType,
107    /// Learning rate
108    pub learning_rate: f64,
109    /// Momentum (for momentum-based optimizers)
110    pub momentum: Option<f64>,
111    /// Adam parameters
112    pub adam_params: Option<AdamParams>,
113}
114
115#[derive(Debug, Clone, Serialize, Deserialize)]
116pub enum OptimizerType {
117    SGD,
118    Momentum,
119    Adam,
120    RMSprop,
121    AdaGrad,
122}
123
124#[derive(Debug, Clone)]
125pub struct AdamParams {
126    pub beta1: f64,
127    pub beta2: f64,
128    pub epsilon: f64,
129    pub m: Vec<Array2<f64>>, // First moment estimates
130    pub v: Vec<Array2<f64>>, // Second moment estimates
131    pub t: usize,            // Time step
132}
133
134#[derive(Debug, Clone)]
135pub struct TrainingHistory {
136    pub losses: Vec<f64>,
137    pub validation_losses: Vec<f64>,
138    pub parameter_updates: Vec<Array1<f64>>,
139    pub convergence_metrics: Vec<ConvergenceMetric>,
140}
141
142#[derive(Debug, Clone)]
143pub struct ConvergenceMetric {
144    pub iteration: usize,
145    pub loss: f64,
146    pub gradient_norm: f64,
147    pub parameter_change_norm: f64,
148    pub validation_score: Option<f64>,
149}
150
151/// Reinforcement learning agent for adaptive sampling strategies
152pub struct SamplingStrategyAgent {
153    /// Q-network for value function approximation
154    q_network: QNetwork,
155    /// Target network for stable training
156    target_network: QNetwork,
157    /// Experience replay buffer
158    replay_buffer: ExperienceReplayBuffer,
159    /// Exploration strategy
160    exploration_strategy: ExplorationStrategy,
161    /// Training statistics
162    training_stats: RLTrainingStats,
163}
164
165#[derive(Debug, Clone)]
166pub struct QNetwork {
167    /// State encoder
168    pub state_encoder: StateEncoder,
169    /// Value network
170    pub value_network: Vec<DenseLayer>,
171    /// Action decoder
172    pub action_decoder: ActionDecoder,
173}
174
175#[derive(Debug, Clone)]
176pub struct StateEncoder {
177    /// Problem embedding dimension
178    pub embedding_dim: usize,
179    /// Encoder layers
180    pub layers: Vec<DenseLayer>,
181}
182
183#[derive(Debug, Clone)]
184pub struct ActionDecoder {
185    /// Action space dimension
186    pub action_dim: usize,
187    /// Decoder layers
188    pub layers: Vec<DenseLayer>,
189}
190
191#[derive(Debug, Clone)]
192pub struct ExperienceReplayBuffer {
193    /// Buffer for storing experiences
194    pub buffer: VecDeque<Experience>,
195    /// Maximum buffer size
196    pub max_size: usize,
197    /// Current position in circular buffer
198    pub position: usize,
199}
200
201#[derive(Debug, Clone)]
202pub struct Experience {
203    /// Current state
204    pub state: ProblemState,
205    /// Action taken
206    pub action: SamplingAction,
207    /// Reward received
208    pub reward: f64,
209    /// Next state
210    pub next_state: ProblemState,
211    /// Whether episode terminated
212    pub done: bool,
213    /// Additional metadata
214    pub metadata: HashMap<String, f64>,
215}
216
217#[derive(Debug, Clone)]
218pub struct ProblemState {
219    /// QUBO matrix representation
220    pub qubo_features: Array1<f64>,
221    /// Current solution
222    pub current_solution: Array1<f64>,
223    /// Energy history
224    pub energy_history: Array1<f64>,
225    /// Algorithm state
226    pub algorithm_state: AlgorithmState,
227    /// Time step
228    pub time_step: usize,
229}
230
231#[derive(Debug, Clone)]
232pub struct AlgorithmState {
233    /// Temperature (for annealing)
234    pub temperature: Option<f64>,
235    /// Iteration count
236    pub iteration: usize,
237    /// Convergence indicators
238    pub convergence_indicators: HashMap<String, f64>,
239    /// Performance metrics
240    pub performance_metrics: PerformanceMetrics,
241}
242
243#[derive(Debug, Clone)]
244pub struct PerformanceMetrics {
245    /// Current best energy
246    pub best_energy: f64,
247    /// Average energy over recent iterations
248    pub avg_recent_energy: f64,
249    /// Acceptance rate
250    pub acceptance_rate: f64,
251    /// Solution diversity
252    pub solution_diversity: f64,
253}
254
255#[derive(Debug, Clone, Serialize, Deserialize)]
256pub enum SamplingAction {
257    /// Adjust temperature schedule
258    AdjustTemperature { factor: f64 },
259    /// Change sampling strategy
260    ChangeSamplingStrategy { strategy: SamplingStrategyType },
261    /// Modify exploration parameters
262    ModifyExploration { exploration_rate: f64 },
263    /// Add local search
264    AddLocalSearch { intensity: f64 },
265    /// Change population size (for population-based methods)
266    ChangePopulationSize { size: usize },
267    /// Adjust crossover parameters
268    AdjustCrossover { rate: f64 },
269    /// Modify mutation parameters
270    ModifyMutation { rate: f64 },
271}
272
273#[derive(Debug, Clone, Serialize, Deserialize)]
274pub enum SamplingStrategyType {
275    SimulatedAnnealing,
276    ParallelTempering,
277    PopulationBasedMCMC,
278    AdaptiveMetropolis,
279    HamiltonianMonteCarlo,
280    QuantumWalk,
281}
282
283#[derive(Debug, Clone, Serialize, Deserialize)]
284pub enum ExplorationStrategy {
285    EpsilonGreedy { epsilon: f64 },
286    Boltzmann { temperature: f64 },
287    UCB { exploration_factor: f64 },
288    ThompsonSampling,
289    NoiseNet { noise_scale: f64 },
290}
291
292#[derive(Debug, Clone)]
293pub struct RLTrainingStats {
294    /// Episodes completed
295    pub episodes: usize,
296    /// Total steps
297    pub total_steps: usize,
298    /// Average reward per episode
299    pub avg_episode_reward: f64,
300    /// Best achieved reward
301    pub best_reward: f64,
302    /// Loss history
303    pub loss_history: Vec<f64>,
304    /// Exploration rate history
305    pub exploration_history: Vec<f64>,
306}
307
308/// Automated algorithm selector using machine learning
309pub struct AutomatedAlgorithmSelector {
310    /// Feature extractor for problem characteristics
311    feature_extractor: ProblemFeatureExtractor,
312    /// Classification model
313    classifier: AlgorithmClassifier,
314    /// Performance database
315    performance_database: PerformanceDatabase,
316    /// Active learning component
317    active_learner: ActiveLearner,
318}
319
320#[derive(Debug, Clone)]
321pub struct ProblemFeatureExtractor {
322    /// Feature extraction methods
323    pub extraction_methods: Vec<FeatureExtractionMethod>,
324    /// Feature normalization
325    pub normalization: FeatureNormalization,
326}
327
328#[derive(Debug, Clone, Serialize, Deserialize)]
329pub enum FeatureExtractionMethod {
330    SpectralFeatures,
331    GraphTopologyFeatures,
332    StatisticalFeatures,
333    EnergyLandscapeFeatures,
334    SymmetryFeatures,
335    DensityFeatures,
336    ConnectivityFeatures,
337}
338
339#[derive(Debug, Clone)]
340pub struct FeatureNormalization {
341    /// Normalization type
342    pub normalization_type: NormalizationType,
343    /// Feature statistics
344    pub feature_stats: HashMap<String, FeatureStats>,
345}
346
347#[derive(Debug, Clone, Serialize, Deserialize)]
348pub enum NormalizationType {
349    StandardScaling,
350    MinMaxScaling,
351    RobustScaling,
352    QuantileUniform,
353    PowerTransform,
354}
355
356#[derive(Debug, Clone)]
357pub struct FeatureStats {
358    pub mean: f64,
359    pub std: f64,
360    pub min: f64,
361    pub max: f64,
362    pub median: f64,
363    pub percentiles: Vec<f64>,
364}
365
366#[derive(Debug, Clone)]
367pub struct AlgorithmClassifier {
368    /// Model type
369    pub model_type: ClassificationModel,
370    /// Model parameters
371    pub parameters: ModelParameters,
372    /// Training data
373    pub training_data: Vec<TrainingExample>,
374    /// Model performance metrics
375    pub performance_metrics: ClassificationMetrics,
376}
377
378#[derive(Debug, Clone, Serialize, Deserialize)]
379pub enum ClassificationModel {
380    RandomForest {
381        n_trees: usize,
382        max_depth: Option<usize>,
383    },
384    SVM {
385        kernel: SVMKernel,
386        c: f64,
387    },
388    NeuralNetwork {
389        layers: Vec<usize>,
390    },
391    GradientBoosting {
392        n_estimators: usize,
393        learning_rate: f64,
394    },
395    KNN {
396        k: usize,
397        distance_metric: DistanceMetric,
398    },
399}
400
401#[derive(Debug, Clone, Serialize, Deserialize)]
402pub enum SVMKernel {
403    Linear,
404    RBF { gamma: f64 },
405    Polynomial { degree: usize, gamma: f64 },
406}
407
408#[derive(Debug, Clone, Serialize, Deserialize)]
409pub enum DistanceMetric {
410    Euclidean,
411    Manhattan,
412    Cosine,
413    Hamming,
414}
415
416#[derive(Debug, Clone)]
417pub struct ModelParameters {
418    /// Model-specific parameters
419    pub parameters: HashMap<String, f64>,
420    /// Hyperparameter optimization history
421    pub optimization_history: Vec<HyperparameterTrial>,
422}
423
424#[derive(Debug, Clone)]
425pub struct HyperparameterTrial {
426    pub parameters: HashMap<String, f64>,
427    pub score: f64,
428    pub training_time: Duration,
429    pub validation_score: f64,
430}
431
432#[derive(Debug, Clone)]
433pub struct TrainingExample {
434    /// Problem features
435    pub features: Array1<f64>,
436    /// Optimal algorithm
437    pub optimal_algorithm: String,
438    /// Performance scores for different algorithms
439    pub algorithm_scores: HashMap<String, f64>,
440    /// Problem metadata
441    pub metadata: ProblemMetadata,
442}
443
444#[derive(Debug, Clone)]
445pub struct ProblemMetadata {
446    pub problem_type: String,
447    pub size: usize,
448    pub density: f64,
449    pub source: String,
450    pub difficulty_level: DifficultyLevel,
451}
452
453#[derive(Debug, Clone, Serialize, Deserialize)]
454pub enum DifficultyLevel {
455    Easy,
456    Medium,
457    Hard,
458    ExtremelyHard,
459}
460
461#[derive(Debug, Clone)]
462pub struct ClassificationMetrics {
463    /// Accuracy on test set
464    pub accuracy: f64,
465    /// Precision for each class
466    pub precision: HashMap<String, f64>,
467    /// Recall for each class
468    pub recall: HashMap<String, f64>,
469    /// F1 score for each class
470    pub f1_score: HashMap<String, f64>,
471    /// Confusion matrix
472    pub confusion_matrix: Array2<f64>,
473    /// Cross-validation scores
474    pub cv_scores: Vec<f64>,
475}
476
477#[derive(Debug, Clone)]
478pub struct PerformanceDatabase {
479    /// Stored performance results
480    pub performance_records: Vec<PerformanceRecord>,
481    /// Algorithm rankings
482    pub algorithm_rankings: HashMap<String, AlgorithmRanking>,
483    /// Problem categories
484    pub problem_categories: HashMap<String, ProblemCategory>,
485}
486
487#[derive(Debug, Clone)]
488pub struct PerformanceRecord {
489    /// Problem identifier
490    pub problem_id: String,
491    /// Algorithm used
492    pub algorithm: String,
493    /// Performance metrics
494    pub metrics: AlgorithmPerformanceMetrics,
495    /// Runtime information
496    pub runtime_info: RuntimeInfo,
497    /// Hardware information
498    pub hardware_info: HardwareInfo,
499}
500
501#[derive(Debug, Clone)]
502pub struct AlgorithmPerformanceMetrics {
503    /// Solution quality
504    pub solution_quality: f64,
505    /// Time to solution
506    pub time_to_solution: Duration,
507    /// Success rate
508    pub success_rate: f64,
509    /// Resource usage
510    pub resource_usage: ResourceUsage,
511    /// Scalability metrics
512    pub scalability_metrics: ScalabilityMetrics,
513}
514
515#[derive(Debug, Clone)]
516pub struct RuntimeInfo {
517    /// Execution time
518    pub execution_time: Duration,
519    /// Memory usage
520    pub memory_usage: usize,
521    /// CPU utilization
522    pub cpu_utilization: f64,
523    /// GPU utilization (if applicable)
524    pub gpu_utilization: Option<f64>,
525}
526
527#[derive(Debug, Clone)]
528pub struct HardwareInfo {
529    /// CPU information
530    pub cpu_info: String,
531    /// GPU information
532    pub gpu_info: Option<String>,
533    /// Memory capacity
534    pub memory_capacity: usize,
535    /// Architecture
536    pub architecture: String,
537}
538
539#[derive(Debug, Clone)]
540pub struct ResourceUsage {
541    /// Peak memory usage
542    pub peak_memory: usize,
543    /// Average CPU utilization
544    pub avg_cpu_utilization: f64,
545    /// Energy consumption (if available)
546    pub energy_consumption: Option<f64>,
547    /// Network usage (for distributed algorithms)
548    pub network_usage: Option<NetworkUsage>,
549}
550
551#[derive(Debug, Clone)]
552pub struct NetworkUsage {
553    /// Bytes sent
554    pub bytes_sent: usize,
555    /// Bytes received
556    pub bytes_received: usize,
557    /// Communication overhead
558    pub communication_overhead: f64,
559}
560
561#[derive(Debug, Clone)]
562pub struct ScalabilityMetrics {
563    /// Scaling exponent
564    pub scaling_exponent: f64,
565    /// Parallel efficiency
566    pub parallel_efficiency: Option<f64>,
567    /// Memory scaling
568    pub memory_scaling: f64,
569}
570
571#[derive(Debug, Clone)]
572pub struct AlgorithmRanking {
573    /// Overall rank
574    pub overall_rank: usize,
575    /// Category-specific ranks
576    pub category_ranks: HashMap<String, usize>,
577    /// Performance scores
578    pub performance_scores: HashMap<String, f64>,
579    /// Confidence intervals
580    pub confidence_intervals: HashMap<String, (f64, f64)>,
581}
582
583#[derive(Debug, Clone)]
584pub struct ProblemCategory {
585    /// Category name
586    pub name: String,
587    /// Category description
588    pub description: String,
589    /// Characteristic features
590    pub characteristic_features: Vec<String>,
591    /// Best algorithms for this category
592    pub best_algorithms: Vec<String>,
593    /// Performance statistics
594    pub performance_stats: HashMap<String, f64>,
595}
596
597#[derive(Debug, Clone)]
598pub struct ActiveLearner {
599    /// Uncertainty sampling strategy
600    pub uncertainty_strategy: UncertaintyStrategy,
601    /// Query selection method
602    pub query_selection: QuerySelectionMethod,
603    /// Budget for active learning
604    pub budget: usize,
605    /// Current queries made
606    pub queries_made: usize,
607    /// Query history
608    pub query_history: Vec<QueryRecord>,
609}
610
611#[derive(Debug, Clone, Serialize, Deserialize)]
612pub enum UncertaintyStrategy {
613    LeastConfident,
614    MarginSampling,
615    EntropyBased,
616    VarianceReduction,
617    ExpectedModelChange,
618}
619
620#[derive(Debug, Clone, Serialize, Deserialize)]
621pub enum QuerySelectionMethod {
622    Random,
623    Greedy,
624    DiversityBased,
625    ClusterBased,
626    HybridStrategy,
627}
628
629#[derive(Debug, Clone)]
630pub struct QueryRecord {
631    /// Problem queried
632    pub problem: ProblemMetadata,
633    /// Uncertainty score
634    pub uncertainty_score: f64,
635    /// True label obtained
636    pub true_label: String,
637    /// Model improvement achieved
638    pub model_improvement: f64,
639}
640
641/// Problem structure recognition system
642pub struct ProblemStructureRecognizer {
643    /// Structure detection methods
644    structure_detectors: Vec<Box<dyn StructureDetector>>,
645    /// Pattern database
646    pattern_database: PatternDatabase,
647    /// Graph analysis tools
648    graph_analyzer: GraphAnalyzer,
649}
650
651pub trait StructureDetector {
652    fn detect_structure(&self, qubo: &Array2<f64>) -> Vec<StructurePattern>;
653    fn confidence_score(&self, pattern: &StructurePattern) -> f64;
654    fn detector_name(&self) -> &str;
655}
656
657#[derive(Debug, Clone, Serialize, Deserialize)]
658pub enum StructurePattern {
659    Block {
660        block_size: usize,
661        num_blocks: usize,
662    },
663    Chain {
664        length: usize,
665    },
666    Grid {
667        dimensions: Vec<usize>,
668    },
669    Tree {
670        depth: usize,
671        branching_factor: f64,
672    },
673    SmallWorld {
674        clustering_coefficient: f64,
675        path_length: f64,
676    },
677    ScaleFree {
678        power_law_exponent: f64,
679    },
680    Bipartite {
681        partition_sizes: (usize, usize),
682    },
683    Modular {
684        num_modules: usize,
685        modularity: f64,
686    },
687    Hierarchical {
688        levels: usize,
689        hierarchy_measure: f64,
690    },
691    Random {
692        randomness_measure: f64,
693    },
694}
695
696#[derive(Debug, Clone)]
697pub struct PatternDatabase {
698    /// Known patterns
699    pub patterns: HashMap<String, PatternInfo>,
700    /// Pattern relationships
701    pub pattern_relationships: HashMap<String, Vec<String>>,
702    /// Algorithmic preferences for patterns
703    pub algorithmic_preferences: HashMap<String, Vec<AlgorithmPreference>>,
704}
705
706#[derive(Debug, Clone)]
707pub struct PatternInfo {
708    /// Pattern type
709    pub pattern_type: StructurePattern,
710    /// Characteristic features
711    pub features: Array1<f64>,
712    /// Typical problem sizes
713    pub typical_sizes: Vec<usize>,
714    /// Difficulty indicators
715    pub difficulty_indicators: HashMap<String, f64>,
716}
717
718#[derive(Debug, Clone)]
719pub struct AlgorithmPreference {
720    /// Algorithm name
721    pub algorithm: String,
722    /// Preference score
723    pub preference_score: f64,
724    /// Confidence level
725    pub confidence: f64,
726    /// Supporting evidence
727    pub evidence: Vec<String>,
728}
729
730#[derive(Debug, Clone)]
731pub struct GraphAnalyzer {
732    /// Graph metrics calculator
733    pub metrics_calculator: GraphMetricsCalculator,
734    /// Community detection methods
735    pub community_detectors: Vec<CommunityDetectionMethod>,
736    /// Centrality measures
737    pub centrality_measures: Vec<CentralityMeasure>,
738}
739
740#[derive(Debug, Clone)]
741pub struct GraphMetricsCalculator {
742    /// Available metrics
743    pub available_metrics: Vec<GraphMetric>,
744}
745
746#[derive(Debug, Clone, Serialize, Deserialize)]
747pub enum GraphMetric {
748    ClusteringCoefficient,
749    AveragePathLength,
750    Diameter,
751    Density,
752    Assortativity,
753    Modularity,
754    SmallWorldness,
755}
756
757#[derive(Debug, Clone, Serialize, Deserialize)]
758pub enum CommunityDetectionMethod {
759    Louvain,
760    Leiden,
761    SpinGlass,
762    Walktrap,
763    FastGreedy,
764    EdgeBetweenness,
765}
766
767#[derive(Debug, Clone, Serialize, Deserialize)]
768pub enum CentralityMeasure {
769    Degree,
770    Betweenness,
771    Closeness,
772    Eigenvector,
773    PageRank,
774    Katz,
775}
776
777/// Solution quality predictor
778pub struct SolutionQualityPredictor {
779    /// Prediction model
780    prediction_model: PredictionModel,
781    /// Feature engineering pipeline
782    feature_pipeline: FeaturePipeline,
783    /// Uncertainty quantification
784    uncertainty_quantifier: UncertaintyQuantifier,
785    /// Model ensemble
786    model_ensemble: ModelEnsemble,
787}
788
789#[derive(Debug, Clone)]
790pub struct PredictionModel {
791    /// Model type
792    pub model_type: RegressionModel,
793    /// Model parameters
794    pub parameters: ModelParameters,
795    /// Training history
796    pub training_history: Vec<TrainingMetric>,
797}
798
799#[derive(Debug, Clone, Serialize, Deserialize)]
800pub enum RegressionModel {
801    LinearRegression,
802    RidgeRegression {
803        alpha: f64,
804    },
805    LassoRegression {
806        alpha: f64,
807    },
808    ElasticNet {
809        alpha: f64,
810        l1_ratio: f64,
811    },
812    RandomForestRegressor {
813        n_trees: usize,
814        max_depth: Option<usize>,
815    },
816    GradientBoostingRegressor {
817        n_estimators: usize,
818        learning_rate: f64,
819    },
820    SVMRegressor {
821        kernel: SVMKernel,
822        c: f64,
823        epsilon: f64,
824    },
825    NeuralNetworkRegressor {
826        layers: Vec<usize>,
827        dropout: f64,
828    },
829    GaussianProcessRegressor {
830        kernel: GPKernel,
831    },
832}
833
834#[derive(Debug, Clone, Serialize, Deserialize)]
835pub enum GPKernel {
836    RBF { length_scale: f64 },
837    Matern { nu: f64, length_scale: f64 },
838    Linear { variance: f64 },
839    Periodic { period: f64, length_scale: f64 },
840}
841
842#[derive(Debug, Clone)]
843pub struct TrainingMetric {
844    /// Epoch/iteration
845    pub epoch: usize,
846    /// Training loss
847    pub training_loss: f64,
848    /// Validation loss
849    pub validation_loss: f64,
850    /// R² score
851    pub r2_score: f64,
852    /// Mean absolute error
853    pub mae: f64,
854    /// Root mean squared error
855    pub rmse: f64,
856}
857
858#[derive(Debug, Clone)]
859pub struct FeaturePipeline {
860    /// Feature transformations
861    pub transformations: Vec<FeatureTransformation>,
862    /// Feature selection methods
863    pub feature_selectors: Vec<FeatureSelector>,
864    /// Dimensionality reduction
865    pub dimensionality_reduction: Option<DimensionalityReduction>,
866}
867
868#[derive(Debug, Clone, Serialize, Deserialize)]
869pub enum FeatureTransformation {
870    StandardScaling,
871    MinMaxScaling,
872    RobustScaling,
873    QuantileTransform,
874    PowerTransform,
875    PolynomialFeatures { degree: usize },
876    InteractionFeatures,
877    LogTransform,
878}
879
880#[derive(Debug, Clone, Serialize, Deserialize)]
881pub enum FeatureSelector {
882    VarianceThreshold { threshold: f64 },
883    UnivariateSelection { k: usize },
884    RecursiveFeatureElimination { n_features: usize },
885    LassoSelection { alpha: f64 },
886    MutualInformation { k: usize },
887}
888
889#[derive(Debug, Clone, Serialize, Deserialize)]
890pub enum DimensionalityReduction {
891    PCA {
892        n_components: usize,
893    },
894    KernelPCA {
895        n_components: usize,
896        kernel: PCAAKernel,
897    },
898    ICA {
899        n_components: usize,
900    },
901    TSNE {
902        n_components: usize,
903        perplexity: f64,
904    },
905    UMAP {
906        n_components: usize,
907        n_neighbors: usize,
908    },
909}
910
911#[derive(Debug, Clone, Serialize, Deserialize)]
912pub enum PCAAKernel {
913    Linear,
914    RBF { gamma: f64 },
915    Polynomial { degree: usize },
916    Sigmoid,
917}
918
919#[derive(Debug, Clone)]
920pub struct UncertaintyQuantifier {
921    /// Uncertainty estimation method
922    pub method: UncertaintyMethod,
923    /// Confidence intervals
924    pub confidence_levels: Vec<f64>,
925    /// Calibration data
926    pub calibration_data: Vec<CalibrationPoint>,
927}
928
929#[derive(Debug, Clone, Serialize, Deserialize)]
930pub enum UncertaintyMethod {
931    Bootstrap { n_samples: usize },
932    Bayesian,
933    Ensemble,
934    QuantileRegression { quantiles: Vec<f64> },
935    MonteCarloDropout { n_samples: usize },
936}
937
938#[derive(Debug, Clone)]
939pub struct CalibrationPoint {
940    /// Predicted uncertainty
941    pub predicted_uncertainty: f64,
942    /// Actual error
943    pub actual_error: f64,
944    /// Problem characteristics
945    pub problem_features: Array1<f64>,
946}
947
948#[derive(Debug, Clone)]
949pub struct ModelEnsemble {
950    /// Base models
951    pub base_models: Vec<PredictionModel>,
952    /// Ensemble method
953    pub ensemble_method: EnsembleMethod,
954    /// Model weights
955    pub model_weights: Array1<f64>,
956    /// Ensemble performance
957    pub ensemble_performance: EnsemblePerformance,
958}
959
960#[derive(Debug, Clone, Serialize, Deserialize)]
961pub enum EnsembleMethod {
962    Voting,
963    Stacking { meta_learner: Box<RegressionModel> },
964    Bagging,
965    Boosting,
966    WeightedAverage,
967    DynamicSelection,
968}
969
970#[derive(Debug, Clone)]
971pub struct EnsemblePerformance {
972    /// Individual model performances
973    pub individual_performances: Vec<f64>,
974    /// Ensemble performance
975    pub ensemble_performance: f64,
976    /// Improvement over best individual
977    pub improvement: f64,
978    /// Diversity measures
979    pub diversity_measures: HashMap<String, f64>,
980}
981
982/// AI optimization results
983#[derive(Debug, Clone, Serialize, Deserialize)]
984pub struct AIOptimizationResult {
985    /// Original problem
986    pub problem_info: ProblemInfo,
987    /// Recommended algorithm
988    pub recommended_algorithm: String,
989    /// Optimized parameters
990    pub optimized_parameters: HashMap<String, f64>,
991    /// Predicted solution quality
992    pub predicted_quality: QualityPrediction,
993    /// Confidence in recommendation
994    pub confidence: f64,
995    /// Alternative recommendations
996    pub alternatives: Vec<AlternativeRecommendation>,
997    /// Optimization process statistics
998    pub optimization_stats: OptimizationStatistics,
999}
1000
1001#[derive(Debug, Clone, Serialize, Deserialize)]
1002pub struct ProblemInfo {
1003    /// Problem size
1004    pub size: usize,
1005    /// Problem type
1006    pub problem_type: String,
1007    /// Detected structure patterns
1008    pub structure_patterns: Vec<StructurePattern>,
1009    /// Problem features
1010    pub features: Array1<f64>,
1011    /// Difficulty assessment
1012    pub difficulty_assessment: DifficultyAssessment,
1013}
1014
1015#[derive(Debug, Clone, Serialize, Deserialize)]
1016pub struct DifficultyAssessment {
1017    /// Overall difficulty score
1018    pub difficulty_score: f64,
1019    /// Difficulty factors
1020    pub difficulty_factors: HashMap<String, f64>,
1021    /// Expected solution time
1022    pub expected_solution_time: Duration,
1023    /// Recommended resources
1024    pub recommended_resources: ResourceRecommendation,
1025}
1026
1027#[derive(Debug, Clone, Serialize, Deserialize)]
1028pub struct ResourceRecommendation {
1029    /// CPU cores
1030    pub cpu_cores: usize,
1031    /// Memory (in GB)
1032    pub memory_gb: f64,
1033    /// GPU acceleration recommended
1034    pub gpu_recommended: bool,
1035    /// Distributed computing recommended
1036    pub distributed_recommended: bool,
1037}
1038
1039#[derive(Debug, Clone, Serialize, Deserialize)]
1040pub struct QualityPrediction {
1041    /// Expected solution quality
1042    pub expected_quality: f64,
1043    /// Quality confidence interval
1044    pub confidence_interval: (f64, f64),
1045    /// Probability of finding optimal solution
1046    pub optimal_probability: f64,
1047    /// Expected convergence time
1048    pub expected_convergence_time: Duration,
1049}
1050
1051#[derive(Debug, Clone, Serialize, Deserialize)]
1052pub struct AlternativeRecommendation {
1053    /// Algorithm name
1054    pub algorithm: String,
1055    /// Expected performance
1056    pub expected_performance: f64,
1057    /// Trade-offs
1058    pub trade_offs: HashMap<String, f64>,
1059    /// Use case scenarios
1060    pub use_cases: Vec<String>,
1061}
1062
1063#[derive(Debug, Clone, Serialize, Deserialize)]
1064pub struct OptimizationStatistics {
1065    /// Total optimization time
1066    pub total_time: Duration,
1067    /// Neural network training time
1068    pub nn_training_time: Duration,
1069    /// RL training episodes
1070    pub rl_episodes: usize,
1071    /// Feature extraction time
1072    pub feature_extraction_time: Duration,
1073    /// Model selection time
1074    pub model_selection_time: Duration,
1075    /// Final model accuracy
1076    pub model_accuracy: f64,
1077}
1078
1079impl AIAssistedOptimizer {
1080    /// Create new AI-assisted optimizer
1081    pub fn new(config: AIOptimizerConfig) -> Self {
1082        Self {
1083            parameter_optimizer: ParameterOptimizationNetwork::new(&config),
1084            rl_agent: SamplingStrategyAgent::new(&config),
1085            algorithm_selector: AutomatedAlgorithmSelector::new(&config),
1086            structure_recognizer: ProblemStructureRecognizer::new(),
1087            quality_predictor: SolutionQualityPredictor::new(&config),
1088            config,
1089        }
1090    }
1091
1092    /// Optimize quantum algorithm for given problem
1093    pub fn optimize(
1094        &mut self,
1095        qubo: &Array2<f64>,
1096        target_quality: Option<f64>,
1097        _time_budget: Option<Duration>,
1098    ) -> Result<AIOptimizationResult, String> {
1099        let start_time = Instant::now();
1100
1101        // Extract problem features
1102        let features = self.extract_problem_features(qubo)?;
1103
1104        // Recognize problem structure
1105        let structure_patterns = if self.config.structure_recognition_enabled {
1106            self.structure_recognizer.recognize_structure(qubo)?
1107        } else {
1108            vec![]
1109        };
1110
1111        // Select best algorithm
1112        let recommended_algorithm = if self.config.auto_algorithm_selection_enabled {
1113            self.algorithm_selector
1114                .select_algorithm(&features, &structure_patterns)?
1115        } else {
1116            "SimulatedAnnealing".to_string() // Default fallback
1117        };
1118
1119        // Optimize parameters
1120        let optimized_parameters = if self.config.parameter_optimization_enabled {
1121            self.parameter_optimizer.optimize_parameters(
1122                &features,
1123                &recommended_algorithm,
1124                target_quality,
1125            )?
1126        } else {
1127            HashMap::new()
1128        };
1129
1130        // Predict solution quality
1131        let predicted_quality = if self.config.quality_prediction_enabled {
1132            self.quality_predictor.predict_quality(
1133                &features,
1134                &recommended_algorithm,
1135                &optimized_parameters,
1136            )?
1137        } else {
1138            QualityPrediction {
1139                expected_quality: 0.8,
1140                confidence_interval: (0.7, 0.9),
1141                optimal_probability: 0.1,
1142                expected_convergence_time: Duration::from_secs(60),
1143            }
1144        };
1145
1146        // Generate alternative recommendations
1147        let alternatives = self.generate_alternatives(&features, &recommended_algorithm)?;
1148
1149        // Compute difficulty assessment
1150        let difficulty_assessment = self.assess_difficulty(qubo, &features, &structure_patterns)?;
1151
1152        // Compute confidence
1153        let confidence = self.compute_recommendation_confidence(
1154            &features,
1155            &recommended_algorithm,
1156            &optimized_parameters,
1157        )?;
1158
1159        let total_time = start_time.elapsed();
1160
1161        Ok(AIOptimizationResult {
1162            problem_info: ProblemInfo {
1163                size: qubo.shape()[0],
1164                problem_type: self.infer_problem_type(&features, &structure_patterns),
1165                structure_patterns,
1166                features,
1167                difficulty_assessment,
1168            },
1169            recommended_algorithm,
1170            optimized_parameters,
1171            predicted_quality,
1172            confidence,
1173            alternatives,
1174            optimization_stats: OptimizationStatistics {
1175                total_time,
1176                nn_training_time: Duration::from_millis(100), // Placeholder
1177                rl_episodes: 50,                              // Placeholder
1178                feature_extraction_time: Duration::from_millis(50),
1179                model_selection_time: Duration::from_millis(30),
1180                model_accuracy: 0.85,
1181            },
1182        })
1183    }
1184
1185    /// Train the AI models on historical data
1186    pub fn train(
1187        &mut self,
1188        training_data: &[TrainingExample],
1189        validation_split: f64,
1190    ) -> Result<TrainingResults, String> {
1191        let split_index = (training_data.len() as f64 * (1.0 - validation_split)) as usize;
1192        let (train_data, val_data) = training_data.split_at(split_index);
1193
1194        let mut results = TrainingResults {
1195            parameter_optimizer_results: None,
1196            rl_agent_results: None,
1197            algorithm_selector_results: None,
1198            quality_predictor_results: None,
1199        };
1200
1201        // Train parameter optimizer
1202        if self.config.parameter_optimization_enabled {
1203            let param_results = self.parameter_optimizer.train(train_data, val_data)?;
1204            results.parameter_optimizer_results = Some(param_results);
1205        }
1206
1207        // Train RL agent
1208        if self.config.reinforcement_learning_enabled {
1209            let rl_results = self.rl_agent.train(train_data)?;
1210            results.rl_agent_results = Some(rl_results);
1211        }
1212
1213        // Train algorithm selector
1214        if self.config.auto_algorithm_selection_enabled {
1215            let selector_results = self.algorithm_selector.train(train_data, val_data)?;
1216            results.algorithm_selector_results = Some(selector_results);
1217        }
1218
1219        // Train quality predictor
1220        if self.config.quality_prediction_enabled {
1221            let predictor_results = self.quality_predictor.train(train_data, val_data)?;
1222            results.quality_predictor_results = Some(predictor_results);
1223        }
1224
1225        Ok(results)
1226    }
1227
1228    /// Extract comprehensive features from QUBO problem
1229    pub fn extract_problem_features(&self, qubo: &Array2<f64>) -> Result<Array1<f64>, String> {
1230        let n = qubo.shape()[0];
1231        let mut features = Vec::new();
1232
1233        // Basic statistics
1234        features.push(n as f64); // Problem size
1235
1236        let coeffs: Vec<f64> = qubo.iter().copied().collect();
1237        let mean = coeffs.iter().sum::<f64>() / coeffs.len() as f64;
1238        let variance = coeffs.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / coeffs.len() as f64;
1239        features.push(mean);
1240        features.push(variance);
1241        features.push(variance.sqrt()); // Standard deviation
1242
1243        // Density
1244        let non_zero_count = coeffs.iter().filter(|&&x| x.abs() > 1e-10).count();
1245        let density = non_zero_count as f64 / coeffs.len() as f64;
1246        features.push(density);
1247
1248        // Min/Max values
1249        let max_val = coeffs.iter().map(|x| x.abs()).fold(0.0, f64::max);
1250        let min_val = coeffs
1251            .iter()
1252            .map(|x| x.abs())
1253            .filter(|&x| x > 1e-10)
1254            .fold(f64::INFINITY, f64::min);
1255        features.push(max_val);
1256        features.push(if min_val.is_finite() { min_val } else { 0.0 });
1257        features.push(if min_val > 0.0 {
1258            max_val / min_val
1259        } else {
1260            1.0
1261        }); // Dynamic range
1262
1263        // Connectivity features
1264        let mut degree_sum = 0;
1265        for i in 0..n {
1266            let mut degree = 0;
1267            for j in 0..n {
1268                if i != j && qubo[[i, j]].abs() > 1e-10 {
1269                    degree += 1;
1270                }
1271            }
1272            degree_sum += degree;
1273        }
1274        let avg_degree = degree_sum as f64 / n as f64;
1275        features.push(avg_degree);
1276
1277        // Symmetry check
1278        let mut is_symmetric = true;
1279        for i in 0..n {
1280            for j in 0..n {
1281                if (qubo[[i, j]] - qubo[[j, i]]).abs() > 1e-10 {
1282                    is_symmetric = false;
1283                    break;
1284                }
1285            }
1286            if !is_symmetric {
1287                break;
1288            }
1289        }
1290        features.push(if is_symmetric { 1.0 } else { 0.0 });
1291
1292        // Diagonal dominance
1293        let mut diag_dominance = 0.0;
1294        for i in 0..n {
1295            let diag_val = qubo[[i, i]].abs();
1296            let off_diag_sum: f64 = (0..n).filter(|&j| i != j).map(|j| qubo[[i, j]].abs()).sum();
1297            if off_diag_sum > 0.0 {
1298                diag_dominance += diag_val / off_diag_sum;
1299            }
1300        }
1301        features.push(diag_dominance / n as f64);
1302
1303        // Frustration measure
1304        let mut frustration = 0.0;
1305        for i in 0..n {
1306            for j in i + 1..n {
1307                if qubo[[i, j]] > 0.0 {
1308                    frustration += qubo[[i, j]];
1309                }
1310            }
1311        }
1312        features.push(frustration);
1313
1314        Ok(Array1::from(features))
1315    }
1316
1317    /// Infer problem type from features and structure
1318    fn infer_problem_type(&self, features: &Array1<f64>, patterns: &[StructurePattern]) -> String {
1319        // Simplified problem type inference
1320        let density = features[4]; // Assuming density is at index 4
1321        let avg_degree = features[7]; // Assuming avg_degree is at index 7
1322
1323        if patterns
1324            .iter()
1325            .any(|p| matches!(p, StructurePattern::Grid { .. }))
1326        {
1327            "Grid-based Optimization".to_string()
1328        } else if patterns
1329            .iter()
1330            .any(|p| matches!(p, StructurePattern::Tree { .. }))
1331        {
1332            "Tree-structured Problem".to_string()
1333        } else if density > 0.8 {
1334            "Dense QUBO".to_string()
1335        } else if avg_degree < 4.0 {
1336            "Sparse QUBO".to_string()
1337        } else {
1338            "General QUBO".to_string()
1339        }
1340    }
1341
1342    /// Assess problem difficulty
1343    pub fn assess_difficulty(
1344        &self,
1345        _qubo: &Array2<f64>,
1346        features: &Array1<f64>,
1347        _patterns: &[StructurePattern],
1348    ) -> Result<DifficultyAssessment, String> {
1349        let size = features[0] as usize;
1350        let variance = features[2];
1351        let density = features[4];
1352        let frustration = features[11]; // Frustration is at index 11
1353
1354        // Compute difficulty score (0 = easy, 1 = extremely hard)
1355        let size_factor = ((size as f64).log2() / 10.0).min(1.0); // More aggressive size scaling
1356        let complexity_factor = (variance * density * 10.0).min(1.0); // Scale up complexity
1357        let frustration_factor =
1358            ((frustration / (size as f64 * size as f64 / 2.0)) * 100.0).min(1.0); // Better normalization
1359
1360        let difficulty_score = 0.2f64
1361            .mul_add(
1362                frustration_factor,
1363                0.4f64.mul_add(size_factor, 0.4 * complexity_factor),
1364            )
1365            .min(1.0);
1366
1367        let mut difficulty_factors = HashMap::new();
1368        difficulty_factors.insert("size".to_string(), size_factor);
1369        difficulty_factors.insert("complexity".to_string(), complexity_factor);
1370        difficulty_factors.insert("frustration".to_string(), frustration_factor);
1371
1372        // Estimate solution time based on difficulty
1373        let base_time = Duration::from_secs(1);
1374        let time_multiplier = (difficulty_score * 100.0).exp();
1375        let expected_solution_time = base_time * time_multiplier as u32;
1376
1377        // Resource recommendations
1378        let recommended_resources = ResourceRecommendation {
1379            cpu_cores: if size > 1000 { 8 } else { 4 },
1380            memory_gb: (size as f64 * 0.001).max(1.0),
1381            gpu_recommended: size > 500,
1382            distributed_recommended: size > 5000,
1383        };
1384
1385        Ok(DifficultyAssessment {
1386            difficulty_score,
1387            difficulty_factors,
1388            expected_solution_time,
1389            recommended_resources,
1390        })
1391    }
1392
1393    /// Generate alternative algorithm recommendations
1394    fn generate_alternatives(
1395        &self,
1396        features: &Array1<f64>,
1397        recommended: &str,
1398    ) -> Result<Vec<AlternativeRecommendation>, String> {
1399        let size = features[0] as usize;
1400        let _density = features[4];
1401
1402        let mut alternatives = Vec::new();
1403
1404        // Generate alternatives based on problem characteristics
1405        if recommended != "SimulatedAnnealing" {
1406            alternatives.push(AlternativeRecommendation {
1407                algorithm: "SimulatedAnnealing".to_string(),
1408                expected_performance: 0.75,
1409                trade_offs: {
1410                    let mut map = HashMap::new();
1411                    map.insert("speed".to_string(), 0.8);
1412                    map.insert("quality".to_string(), 0.7);
1413                    map
1414                },
1415                use_cases: vec!["General purpose".to_string(), "Good baseline".to_string()],
1416            });
1417        }
1418
1419        if recommended != "GeneticAlgorithm" && size > 100 {
1420            alternatives.push(AlternativeRecommendation {
1421                algorithm: "GeneticAlgorithm".to_string(),
1422                expected_performance: 0.8,
1423                trade_offs: {
1424                    let mut map = HashMap::new();
1425                    map.insert("speed".to_string(), 0.6);
1426                    map.insert("quality".to_string(), 0.85);
1427                    map
1428                },
1429                use_cases: vec![
1430                    "Large problems".to_string(),
1431                    "Population diversity".to_string(),
1432                ],
1433            });
1434        }
1435
1436        if recommended != "TabuSearch" {
1437            alternatives.push(AlternativeRecommendation {
1438                algorithm: "TabuSearch".to_string(),
1439                expected_performance: 0.85,
1440                trade_offs: {
1441                    let mut map = HashMap::new();
1442                    map.insert("speed".to_string(), 0.7);
1443                    map.insert("quality".to_string(), 0.9);
1444                    map
1445                },
1446                use_cases: vec![
1447                    "Local search".to_string(),
1448                    "Escape local minima".to_string(),
1449                ],
1450            });
1451        }
1452
1453        Ok(alternatives)
1454    }
1455
1456    /// Compute confidence in recommendation
1457    fn compute_recommendation_confidence(
1458        &self,
1459        features: &Array1<f64>,
1460        _algorithm: &str,
1461        parameters: &HashMap<String, f64>,
1462    ) -> Result<f64, String> {
1463        // Simplified confidence computation
1464        let size = features[0] as usize;
1465        let density = features[4];
1466
1467        let base_confidence = 0.7;
1468
1469        // Adjust based on problem characteristics and algorithm match
1470        let size_confidence = if size < 1000 { 0.9 } else { 0.6 };
1471        let density_confidence = if density > 0.1 && density < 0.9 {
1472            0.8
1473        } else {
1474            0.6
1475        };
1476        let param_confidence = if parameters.is_empty() { 0.7 } else { 0.85 };
1477
1478        let overall_confidence: f64 =
1479            base_confidence * size_confidence * density_confidence * param_confidence;
1480
1481        Ok(overall_confidence.min(1.0))
1482    }
1483}
1484
1485#[derive(Debug, Clone)]
1486pub struct RLTrainingResults {
1487    pub episodes: u32,
1488    pub total_steps: u32,
1489    pub avg_episode_reward: f64,
1490    pub best_reward: f64,
1491    pub loss_history: Vec<f64>,
1492    pub exploration_history: Vec<f64>,
1493}
1494
1495/// Training results for AI components
1496#[derive(Debug, Clone)]
1497pub struct TrainingResults {
1498    pub parameter_optimizer_results: Option<ParameterOptimizerTrainingResults>,
1499    pub rl_agent_results: Option<RLTrainingResults>,
1500    pub algorithm_selector_results: Option<AlgorithmSelectorTrainingResults>,
1501    pub quality_predictor_results: Option<QualityPredictorTrainingResults>,
1502}
1503
1504#[derive(Debug, Clone)]
1505pub struct ParameterOptimizerTrainingResults {
1506    pub final_loss: f64,
1507    pub training_time: Duration,
1508    pub convergence_achieved: bool,
1509    pub best_parameters_found: HashMap<String, f64>,
1510}
1511
1512#[derive(Debug, Clone)]
1513pub struct AlgorithmSelectorTrainingResults {
1514    pub accuracy: f64,
1515    pub training_time: Duration,
1516    pub cross_validation_scores: Vec<f64>,
1517    pub feature_importance: HashMap<String, f64>,
1518}
1519
1520#[derive(Debug, Clone)]
1521pub struct QualityPredictorTrainingResults {
1522    pub r2_score: f64,
1523    pub mae: f64,
1524    pub rmse: f64,
1525    pub training_time: Duration,
1526    pub model_complexity: usize,
1527}
1528
1529// Implementation stubs for the complex components
1530impl ParameterOptimizationNetwork {
1531    pub const fn new(config: &AIOptimizerConfig) -> Self {
1532        Self {
1533            layers: vec![], // Would be initialized with actual layers
1534            optimizer: Optimizer {
1535                optimizer_type: OptimizerType::Adam,
1536                learning_rate: config.learning_rate,
1537                momentum: None,
1538                adam_params: Some(AdamParams {
1539                    beta1: 0.9,
1540                    beta2: 0.999,
1541                    epsilon: 1e-8,
1542                    m: vec![],
1543                    v: vec![],
1544                    t: 0,
1545                }),
1546            },
1547            training_history: TrainingHistory {
1548                losses: vec![],
1549                validation_losses: vec![],
1550                parameter_updates: vec![],
1551                convergence_metrics: vec![],
1552            },
1553            best_parameters: None,
1554        }
1555    }
1556
1557    pub fn optimize_parameters(
1558        &mut self,
1559        _features: &Array1<f64>,
1560        algorithm: &str,
1561        _target_quality: Option<f64>,
1562    ) -> Result<HashMap<String, f64>, String> {
1563        // Simplified parameter optimization
1564        let mut params = HashMap::new();
1565
1566        match algorithm {
1567            "SimulatedAnnealing" => {
1568                params.insert("initial_temperature".to_string(), 10.0);
1569                params.insert("cooling_rate".to_string(), 0.95);
1570                params.insert("min_temperature".to_string(), 0.01);
1571            }
1572            "GeneticAlgorithm" => {
1573                params.insert("population_size".to_string(), 100.0);
1574                params.insert("mutation_rate".to_string(), 0.1);
1575                params.insert("crossover_rate".to_string(), 0.8);
1576            }
1577            _ => {
1578                params.insert("iterations".to_string(), 1000.0);
1579            }
1580        }
1581
1582        Ok(params)
1583    }
1584
1585    pub fn train(
1586        &mut self,
1587        _train_data: &[TrainingExample],
1588        _val_data: &[TrainingExample],
1589    ) -> Result<ParameterOptimizerTrainingResults, String> {
1590        // Simplified training
1591        Ok(ParameterOptimizerTrainingResults {
1592            final_loss: 0.01,
1593            training_time: Duration::from_secs(60),
1594            convergence_achieved: true,
1595            best_parameters_found: HashMap::new(),
1596        })
1597    }
1598}
1599
1600impl SamplingStrategyAgent {
1601    pub const fn new(config: &AIOptimizerConfig) -> Self {
1602        Self {
1603            q_network: QNetwork {
1604                state_encoder: StateEncoder {
1605                    embedding_dim: 64,
1606                    layers: vec![],
1607                },
1608                value_network: vec![],
1609                action_decoder: ActionDecoder {
1610                    action_dim: 10,
1611                    layers: vec![],
1612                },
1613            },
1614            target_network: QNetwork {
1615                state_encoder: StateEncoder {
1616                    embedding_dim: 64,
1617                    layers: vec![],
1618                },
1619                value_network: vec![],
1620                action_decoder: ActionDecoder {
1621                    action_dim: 10,
1622                    layers: vec![],
1623                },
1624            },
1625            replay_buffer: ExperienceReplayBuffer {
1626                buffer: VecDeque::new(),
1627                max_size: config.replay_buffer_size,
1628                position: 0,
1629            },
1630            exploration_strategy: ExplorationStrategy::EpsilonGreedy { epsilon: 0.1 },
1631            training_stats: RLTrainingStats {
1632                episodes: 0,
1633                total_steps: 0,
1634                avg_episode_reward: 0.0,
1635                best_reward: 0.0,
1636                loss_history: vec![],
1637                exploration_history: vec![],
1638            },
1639        }
1640    }
1641
1642    pub fn train(&mut self, _data: &[TrainingExample]) -> Result<RLTrainingResults, String> {
1643        // Simplified RL training
1644        Ok(RLTrainingResults {
1645            episodes: 100,
1646            total_steps: 5000,
1647            avg_episode_reward: 10.0,
1648            best_reward: 50.0,
1649            loss_history: vec![1.0, 0.5, 0.2, 0.1],
1650            exploration_history: vec![1.0, 0.8, 0.6, 0.4],
1651        })
1652    }
1653
1654    /// Get a reference to the Q-network
1655    pub const fn q_network(&self) -> &QNetwork {
1656        &self.q_network
1657    }
1658
1659    /// Get a reference to the replay buffer
1660    pub const fn replay_buffer(&self) -> &ExperienceReplayBuffer {
1661        &self.replay_buffer
1662    }
1663
1664    /// Get a reference to the training stats
1665    pub const fn training_stats(&self) -> &RLTrainingStats {
1666        &self.training_stats
1667    }
1668}
1669
1670impl AutomatedAlgorithmSelector {
1671    pub fn new(_config: &AIOptimizerConfig) -> Self {
1672        Self {
1673            feature_extractor: ProblemFeatureExtractor {
1674                extraction_methods: vec![
1675                    FeatureExtractionMethod::SpectralFeatures,
1676                    FeatureExtractionMethod::GraphTopologyFeatures,
1677                    FeatureExtractionMethod::StatisticalFeatures,
1678                ],
1679                normalization: FeatureNormalization {
1680                    normalization_type: NormalizationType::StandardScaling,
1681                    feature_stats: HashMap::new(),
1682                },
1683            },
1684            classifier: AlgorithmClassifier {
1685                model_type: ClassificationModel::RandomForest {
1686                    n_trees: 100,
1687                    max_depth: Some(10),
1688                },
1689                parameters: ModelParameters {
1690                    parameters: HashMap::new(),
1691                    optimization_history: vec![],
1692                },
1693                training_data: vec![],
1694                performance_metrics: ClassificationMetrics {
1695                    accuracy: 0.0,
1696                    precision: HashMap::new(),
1697                    recall: HashMap::new(),
1698                    f1_score: HashMap::new(),
1699                    confusion_matrix: Array2::zeros((0, 0)),
1700                    cv_scores: vec![],
1701                },
1702            },
1703            performance_database: PerformanceDatabase {
1704                performance_records: vec![],
1705                algorithm_rankings: HashMap::new(),
1706                problem_categories: HashMap::new(),
1707            },
1708            active_learner: ActiveLearner {
1709                uncertainty_strategy: UncertaintyStrategy::EntropyBased,
1710                query_selection: QuerySelectionMethod::DiversityBased,
1711                budget: 100,
1712                queries_made: 0,
1713                query_history: vec![],
1714            },
1715        }
1716    }
1717
1718    pub fn select_algorithm(
1719        &self,
1720        features: &Array1<f64>,
1721        patterns: &[StructurePattern],
1722    ) -> Result<String, String> {
1723        // Simplified algorithm selection
1724        let size = features[0] as usize;
1725        let density = features[4];
1726
1727        if size < 100 {
1728            Ok("BranchAndBound".to_string())
1729        } else if density > 0.8 {
1730            Ok("SimulatedAnnealing".to_string())
1731        } else if patterns
1732            .iter()
1733            .any(|p| matches!(p, StructurePattern::Tree { .. }))
1734        {
1735            Ok("DynamicProgramming".to_string())
1736        } else {
1737            Ok("GeneticAlgorithm".to_string())
1738        }
1739    }
1740
1741    pub fn train(
1742        &mut self,
1743        _train_data: &[TrainingExample],
1744        _val_data: &[TrainingExample],
1745    ) -> Result<AlgorithmSelectorTrainingResults, String> {
1746        // Simplified training
1747        Ok(AlgorithmSelectorTrainingResults {
1748            accuracy: 0.85,
1749            training_time: Duration::from_secs(120),
1750            cross_validation_scores: vec![0.82, 0.84, 0.86, 0.83, 0.87],
1751            feature_importance: HashMap::new(),
1752        })
1753    }
1754}
1755
1756impl Default for ProblemStructureRecognizer {
1757    fn default() -> Self {
1758        Self::new()
1759    }
1760}
1761
1762impl ProblemStructureRecognizer {
1763    pub fn new() -> Self {
1764        Self {
1765            structure_detectors: vec![],
1766            pattern_database: PatternDatabase {
1767                patterns: HashMap::new(),
1768                pattern_relationships: HashMap::new(),
1769                algorithmic_preferences: HashMap::new(),
1770            },
1771            graph_analyzer: GraphAnalyzer {
1772                metrics_calculator: GraphMetricsCalculator {
1773                    available_metrics: vec![
1774                        GraphMetric::ClusteringCoefficient,
1775                        GraphMetric::AveragePathLength,
1776                        GraphMetric::Density,
1777                    ],
1778                },
1779                community_detectors: vec![CommunityDetectionMethod::Louvain],
1780                centrality_measures: vec![
1781                    CentralityMeasure::Degree,
1782                    CentralityMeasure::Betweenness,
1783                ],
1784            },
1785        }
1786    }
1787
1788    pub fn recognize_structure(&self, qubo: &Array2<f64>) -> Result<Vec<StructurePattern>, String> {
1789        let n = qubo.shape()[0];
1790        let mut patterns = Vec::new();
1791
1792        // Detect grid pattern
1793        if self.is_grid_like(qubo) {
1794            let grid_dim = (n as f64).sqrt() as usize;
1795            patterns.push(StructurePattern::Grid {
1796                dimensions: vec![grid_dim, grid_dim],
1797            });
1798        }
1799
1800        // Detect block structure
1801        if let Some((block_size, num_blocks)) = self.detect_block_structure(qubo) {
1802            patterns.push(StructurePattern::Block {
1803                block_size,
1804                num_blocks,
1805            });
1806        }
1807
1808        // Detect chain structure
1809        if self.is_chain_like(qubo) {
1810            patterns.push(StructurePattern::Chain { length: n });
1811        }
1812
1813        Ok(patterns)
1814    }
1815
1816    fn is_grid_like(&self, qubo: &Array2<f64>) -> bool {
1817        let n = qubo.shape()[0];
1818        let grid_dim = (n as f64).sqrt() as usize;
1819
1820        // Check if it's a perfect square and has grid-like connectivity
1821        grid_dim * grid_dim == n && self.check_grid_connectivity(qubo, grid_dim)
1822    }
1823
1824    fn check_grid_connectivity(&self, qubo: &Array2<f64>, grid_dim: usize) -> bool {
1825        // Simplified grid connectivity check
1826        let n = qubo.shape()[0];
1827        let mut grid_edges = 0;
1828        let mut total_edges = 0;
1829
1830        for i in 0..n {
1831            for j in 0..n {
1832                if i != j && qubo[[i, j]].abs() > 1e-10 {
1833                    total_edges += 1;
1834
1835                    let row_i = i / grid_dim;
1836                    let col_i = i % grid_dim;
1837                    let row_j = j / grid_dim;
1838                    let col_j = j % grid_dim;
1839
1840                    // Check if it's a valid grid edge (adjacent cells)
1841                    if (row_i == row_j && (col_i as i32 - col_j as i32).abs() == 1)
1842                        || (col_i == col_j && (row_i as i32 - row_j as i32).abs() == 1)
1843                    {
1844                        grid_edges += 1;
1845                    }
1846                }
1847            }
1848        }
1849
1850        if total_edges == 0 {
1851            false
1852        } else {
1853            grid_edges as f64 / total_edges as f64 > 0.8
1854        }
1855    }
1856
1857    fn detect_block_structure(&self, qubo: &Array2<f64>) -> Option<(usize, usize)> {
1858        let n = qubo.shape()[0];
1859
1860        // Try different block sizes
1861        for block_size in 2..=n / 2 {
1862            if n % block_size == 0 {
1863                let num_blocks = n / block_size;
1864                if self.check_block_structure(qubo, block_size, num_blocks) {
1865                    return Some((block_size, num_blocks));
1866                }
1867            }
1868        }
1869
1870        None
1871    }
1872
1873    fn check_block_structure(
1874        &self,
1875        qubo: &Array2<f64>,
1876        block_size: usize,
1877        _num_blocks: usize,
1878    ) -> bool {
1879        let mut intra_block_edges = 0;
1880        let mut inter_block_edges = 0;
1881
1882        for i in 0..qubo.shape()[0] {
1883            for j in 0..qubo.shape()[0] {
1884                if i != j && qubo[[i, j]].abs() > 1e-10 {
1885                    let block_i = i / block_size;
1886                    let block_j = j / block_size;
1887
1888                    if block_i == block_j {
1889                        intra_block_edges += 1;
1890                    } else {
1891                        inter_block_edges += 1;
1892                    }
1893                }
1894            }
1895        }
1896
1897        if intra_block_edges + inter_block_edges == 0 {
1898            false
1899        } else {
1900            // Strong block structure if most edges are within blocks
1901            intra_block_edges as f64 / (intra_block_edges + inter_block_edges) as f64 > 0.7
1902        }
1903    }
1904
1905    fn is_chain_like(&self, qubo: &Array2<f64>) -> bool {
1906        let n = qubo.shape()[0];
1907        let mut chain_edges = 0;
1908        let mut total_edges = 0;
1909
1910        for i in 0..n {
1911            for j in 0..n {
1912                if i != j && qubo[[i, j]].abs() > 1e-10 {
1913                    total_edges += 1;
1914
1915                    // Check if it's a chain edge (consecutive indices)
1916                    if (i as i32 - j as i32).abs() == 1 {
1917                        chain_edges += 1;
1918                    }
1919                }
1920            }
1921        }
1922
1923        if total_edges == 0 {
1924            false
1925        } else {
1926            chain_edges as f64 / total_edges as f64 > 0.8
1927        }
1928    }
1929}
1930
1931impl SolutionQualityPredictor {
1932    pub fn new(_config: &AIOptimizerConfig) -> Self {
1933        Self {
1934            prediction_model: PredictionModel {
1935                model_type: RegressionModel::RandomForestRegressor {
1936                    n_trees: 100,
1937                    max_depth: Some(10),
1938                },
1939                parameters: ModelParameters {
1940                    parameters: HashMap::new(),
1941                    optimization_history: vec![],
1942                },
1943                training_history: vec![],
1944            },
1945            feature_pipeline: FeaturePipeline {
1946                transformations: vec![FeatureTransformation::StandardScaling],
1947                feature_selectors: vec![],
1948                dimensionality_reduction: None,
1949            },
1950            uncertainty_quantifier: UncertaintyQuantifier {
1951                method: UncertaintyMethod::Bootstrap { n_samples: 100 },
1952                confidence_levels: vec![0.95, 0.99],
1953                calibration_data: vec![],
1954            },
1955            model_ensemble: ModelEnsemble {
1956                base_models: vec![],
1957                ensemble_method: EnsembleMethod::WeightedAverage,
1958                model_weights: Array1::ones(1),
1959                ensemble_performance: EnsemblePerformance {
1960                    individual_performances: vec![],
1961                    ensemble_performance: 0.0,
1962                    improvement: 0.0,
1963                    diversity_measures: HashMap::new(),
1964                },
1965            },
1966        }
1967    }
1968
1969    pub fn predict_quality(
1970        &self,
1971        features: &Array1<f64>,
1972        algorithm: &str,
1973        _parameters: &HashMap<String, f64>,
1974    ) -> Result<QualityPrediction, String> {
1975        // Simplified quality prediction
1976        let base_quality: f64 = 0.8;
1977        let size = features[0] as usize;
1978
1979        // Adjust prediction based on algorithm and problem size
1980        let quality_adjustment: f64 = match algorithm {
1981            "SimulatedAnnealing" => {
1982                if size < 100 {
1983                    0.1
1984                } else {
1985                    -0.1
1986                }
1987            }
1988            "GeneticAlgorithm" => {
1989                if size > 500 {
1990                    0.15
1991                } else {
1992                    0.0
1993                }
1994            }
1995            "TabuSearch" => 0.1,
1996            _ => 0.0,
1997        };
1998
1999        let expected_quality: f64 = (base_quality + quality_adjustment).max(0.0).min(1.0);
2000        let confidence_width = 0.1;
2001
2002        Ok(QualityPrediction {
2003            expected_quality,
2004            confidence_interval: (
2005                (expected_quality - confidence_width).max(0.0),
2006                (expected_quality + confidence_width).min(1.0),
2007            ),
2008            optimal_probability: if expected_quality > 0.9 { 0.8 } else { 0.1 },
2009            expected_convergence_time: Duration::from_secs((size as f64 * 0.1) as u64),
2010        })
2011    }
2012
2013    pub const fn train(
2014        &mut self,
2015        _train_data: &[TrainingExample],
2016        _val_data: &[TrainingExample],
2017    ) -> Result<QualityPredictorTrainingResults, String> {
2018        // Simplified training
2019        Ok(QualityPredictorTrainingResults {
2020            r2_score: 0.85,
2021            mae: 0.05,
2022            rmse: 0.08,
2023            training_time: Duration::from_secs(90),
2024            model_complexity: 1000,
2025        })
2026    }
2027}
quantrs2_tytan/ai_assisted_optimization.rs

quantrs2_tytan/
ai_assisted_optimization.rs
