quantrs2_device/
advanced_benchmarking_suite.rs

1//! Advanced Hardware Benchmarking Suite with Enhanced SciRS2 Analysis
2//!
3//! This module provides next-generation benchmarking capabilities with machine learning,
4//! predictive modeling, real-time adaptation, and comprehensive SciRS2 statistical analysis
5//! for quantum hardware characterization and optimization.
6
7use std::collections::{BTreeMap, HashMap, VecDeque};
8use std::time::{Duration, Instant, SystemTime};
9
10use scirs2_core::ndarray::{s, Array1, Array2};
11use scirs2_core::random::prelude::*;
12use serde::{Deserialize, Serialize};
13use std::sync::{Mutex, RwLock};
14
15use quantrs2_circuit::prelude::*;
16use quantrs2_core::{
17    error::{QuantRS2Error, QuantRS2Result},
18    gate::GateOp,
19    qubit::QubitId,
20};
21
22// Enhanced SciRS2 imports for advanced analysis
23#[cfg(feature = "scirs2")]
24use scirs2_stats::{
25    bartlett, chi2_gof,
26    distributions::{beta, chi2 as chi2_dist, exponential, f as f_dist, gamma, norm, t},
27    kendall_tau, ks_2samp, kurtosis, levene, mann_whitney, mean, median, pearsonr, percentile,
28    skew, spearmanr, std, ttest_1samp, ttest_ind, var, wilcoxon, Alternative, TTestResult,
29};
30
31#[cfg(feature = "scirs2")]
32use scirs2_linalg::lowrank::pca;
33#[cfg(feature = "scirs2")]
34use scirs2_linalg::{
35    cond, correlationmatrix, covariancematrix, det, eig, matrix_norm, svd, LinalgResult,
36};
37
38#[cfg(feature = "scirs2")]
39use scirs2_optimize::{differential_evolution, minimize, particle_swarm, OptimizeResult};
40
41#[cfg(feature = "scirs2")]
42use scirs2_graph::spectral::spectral_clustering;
43#[cfg(feature = "scirs2")]
44use scirs2_graph::{
45    betweenness_centrality, closeness_centrality, clustering_coefficient, dijkstra_path,
46    eigenvector_centrality, graph_density, louvain_communities_result, pagerank, Graph,
47};
48
49// TODO: scirs2_ml crate not available yet
50// #[cfg(feature = "scirs2")]
51// use scirs2_ml::{
52//     LinearRegression, PolynomialFeatures, Ridge, Lasso,
53//     RandomForestRegressor, GradientBoostingRegressor,
54//     KMeans, DBSCAN, IsolationForest,
55//     train_test_split, cross_validate, grid_search,
56// };
57
58// Fallback implementations
59#[cfg(not(feature = "scirs2"))]
60// Note: ML optimization types are conditionally available based on scirs2 feature
61use scirs2_core::ndarray::{Array3, ArrayView1, ArrayView2, Axis};
62
63use crate::{
64    backend_traits::{query_backend_capabilities, BackendCapabilities},
65    benchmarking::{BenchmarkConfig, DeviceExecutor, HardwareBenchmarkSuite},
66    calibration::{CalibrationManager, DeviceCalibration},
67    characterization::{AdvancedNoiseCharacterizer, NoiseCharacterizationConfig},
68    ml_optimization::{train_test_split, IsolationForest, KMeans, KMeansResult, DBSCAN},
69    qec::{QECConfig, QuantumErrorCorrector},
70    CircuitResult, DeviceError, DeviceResult,
71};
72
73// Placeholder ML model types
74pub struct LinearRegression {
75    pub coefficients: Array1<f64>,
76}
77
78impl Default for LinearRegression {
79    fn default() -> Self {
80        Self::new()
81    }
82}
83
84impl LinearRegression {
85    pub fn new() -> Self {
86        Self {
87            coefficients: Array1::zeros(1),
88        }
89    }
90
91    pub const fn fit(&mut self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<&Self, String> {
92        Ok(self)
93    }
94
95    pub fn predict(&self, _x: &Array2<f64>) -> Array1<f64> {
96        Array1::zeros(1)
97    }
98
99    pub const fn score(&self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<f64, String> {
100        Ok(0.95) // Mock score
101    }
102}
103
104pub struct RandomForestRegressor {
105    pub n_estimators: usize,
106}
107
108impl RandomForestRegressor {
109    pub const fn new(n_estimators: usize) -> Self {
110        Self { n_estimators }
111    }
112
113    pub const fn fit(&mut self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<&Self, String> {
114        Ok(self)
115    }
116
117    pub fn predict(&self, _x: &Array2<f64>) -> Array1<f64> {
118        Array1::zeros(1)
119    }
120
121    pub const fn score(&self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<f64, String> {
122        Ok(0.92) // Mock score
123    }
124}
125
126pub struct GradientBoostingRegressor {
127    pub n_estimators: usize,
128    pub learning_rate: f64,
129}
130
131impl GradientBoostingRegressor {
132    pub const fn new(n_estimators: usize, learning_rate: f64) -> Self {
133        Self {
134            n_estimators,
135            learning_rate,
136        }
137    }
138
139    pub const fn fit(&mut self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<&Self, String> {
140        Ok(self)
141    }
142
143    pub fn predict(&self, _x: &Array2<f64>) -> Array1<f64> {
144        Array1::zeros(1)
145    }
146
147    pub const fn score(&self, _x: &Array2<f64>, _y: &Array1<f64>) -> Result<f64, String> {
148        Ok(0.89) // Mock score
149    }
150}
151
152/// Advanced benchmarking suite configuration with ML and real-time capabilities
153#[derive(Debug, Clone, Serialize, Deserialize)]
154pub struct AdvancedBenchmarkConfig {
155    /// Base benchmarking configuration
156    pub base_config: BenchmarkConfig,
157    /// Machine learning configuration
158    pub ml_config: MLBenchmarkConfig,
159    /// Real-time adaptation configuration
160    pub realtime_config: RealtimeBenchmarkConfig,
161    /// Predictive modeling configuration
162    pub prediction_config: PredictiveModelingConfig,
163    /// Anomaly detection configuration
164    pub anomaly_config: AnomalyDetectionConfig,
165    /// Advanced statistical analysis configuration
166    pub advanced_stats_config: AdvancedStatsConfig,
167    /// Performance optimization configuration
168    pub optimization_config: BenchmarkOptimizationConfig,
169}
170
171/// Machine learning configuration for benchmarking
172#[derive(Debug, Clone, Serialize, Deserialize)]
173pub struct MLBenchmarkConfig {
174    /// Enable ML-driven benchmark selection
175    pub enable_adaptive_selection: bool,
176    /// Enable performance prediction
177    pub enable_prediction: bool,
178    /// Enable clustering analysis
179    pub enable_clustering: bool,
180    /// Model types to use
181    pub model_types: Vec<MLModelType>,
182    /// Training configuration
183    pub training_config: MLTrainingConfig,
184    /// Feature engineering configuration
185    pub feature_config: FeatureEngineeringConfig,
186}
187
188/// ML model types for benchmarking
189#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
190pub enum MLModelType {
191    LinearRegression,
192    PolynomialRegression {
193        degree: usize,
194    },
195    RandomForest {
196        n_estimators: usize,
197    },
198    GradientBoosting {
199        n_estimators: usize,
200        learning_rate: f64,
201    },
202    NeuralNetwork {
203        hidden_layers: Vec<usize>,
204    },
205    SupportVectorMachine {
206        kernel: String,
207    },
208    GaussianProcess,
209}
210
211/// ML training configuration
212#[derive(Debug, Clone, Serialize, Deserialize)]
213pub struct MLTrainingConfig {
214    /// Training/test split ratio
215    pub test_size: f64,
216    /// Cross-validation folds
217    pub cv_folds: usize,
218    /// Random state for reproducibility
219    pub random_state: Option<u64>,
220    /// Enable hyperparameter tuning
221    pub enable_hyperparameter_tuning: bool,
222    /// Grid search parameters
223    pub grid_search_params: HashMap<String, Vec<f64>>,
224}
225
226/// Feature engineering configuration
227#[derive(Debug, Clone, Serialize, Deserialize)]
228pub struct FeatureEngineeringConfig {
229    /// Enable polynomial features
230    pub enable_polynomial_features: bool,
231    /// Polynomial degree
232    pub polynomial_degree: usize,
233    /// Enable interaction features
234    pub enable_interactions: bool,
235    /// Enable feature selection
236    pub enable_feature_selection: bool,
237    /// Feature selection method
238    pub selection_method: FeatureSelectionMethod,
239}
240
241/// Feature selection methods
242#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
243pub enum FeatureSelectionMethod {
244    VarianceThreshold { threshold: f64 },
245    UnivariateSelection { k_best: usize },
246    RecursiveFeatureElimination { n_features: usize },
247    LassoSelection { alpha: f64 },
248}
249
250/// Real-time benchmarking configuration
251#[derive(Debug, Clone, Serialize, Deserialize)]
252pub struct RealtimeBenchmarkConfig {
253    /// Enable real-time monitoring
254    pub enable_realtime: bool,
255    /// Monitoring interval
256    pub monitoring_interval: Duration,
257    /// Adaptive threshold adjustment
258    pub enable_adaptive_thresholds: bool,
259    /// Performance degradation threshold
260    pub degradation_threshold: f64,
261    /// Automatic retraining triggers
262    pub retrain_triggers: Vec<RetrainTrigger>,
263    /// Real-time notification configuration
264    pub notification_config: NotificationConfig,
265}
266
267/// Triggers for model retraining
268#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
269pub enum RetrainTrigger {
270    PerformanceDegradation { threshold: f64 },
271    DataDrift { sensitivity: f64 },
272    TimeBasedInterval { interval: Duration },
273    NewDataThreshold { min_samples: usize },
274}
275
276/// Notification configuration
277#[derive(Debug, Clone, Serialize, Deserialize)]
278pub struct NotificationConfig {
279    /// Enable performance alerts
280    pub enable_alerts: bool,
281    /// Alert thresholds
282    pub alert_thresholds: HashMap<String, f64>,
283    /// Notification channels
284    pub channels: Vec<NotificationChannel>,
285}
286
287/// Notification channels
288#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
289pub enum NotificationChannel {
290    Log { level: String },
291    Email { recipients: Vec<String> },
292    Webhook { url: String },
293    Database { table: String },
294}
295
296/// Predictive modeling configuration
297#[derive(Debug, Clone, Serialize, Deserialize)]
298pub struct PredictiveModelingConfig {
299    /// Enable predictive modeling
300    pub enable_prediction: bool,
301    /// Prediction horizon (time steps)
302    pub prediction_horizon: usize,
303    /// Time series analysis configuration
304    pub time_series_config: TimeSeriesConfig,
305    /// Confidence interval level
306    pub confidence_level: f64,
307    /// Enable uncertainty quantification
308    pub enable_uncertainty: bool,
309}
310
311/// Time series analysis configuration
312#[derive(Debug, Clone, Serialize, Deserialize)]
313pub struct TimeSeriesConfig {
314    /// Enable trend analysis
315    pub enable_trend: bool,
316    /// Enable seasonality detection
317    pub enable_seasonality: bool,
318    /// Seasonality period
319    pub seasonality_period: usize,
320    /// Enable change point detection
321    pub enable_changepoint: bool,
322    /// Smoothing parameters
323    pub smoothing_params: SmoothingParams,
324}
325
326/// Smoothing parameters for time series
327#[derive(Debug, Clone, Serialize, Deserialize)]
328pub struct SmoothingParams {
329    /// Alpha (level smoothing)
330    pub alpha: f64,
331    /// Beta (trend smoothing)
332    pub beta: f64,
333    /// Gamma (seasonal smoothing)
334    pub gamma: f64,
335}
336
337/// Anomaly detection configuration
338#[derive(Debug, Clone, Serialize, Deserialize)]
339pub struct AnomalyDetectionConfig {
340    /// Enable anomaly detection
341    pub enable_detection: bool,
342    /// Detection methods
343    pub methods: Vec<AnomalyDetectionMethod>,
344    /// Sensitivity threshold
345    pub sensitivity: f64,
346    /// Rolling window size
347    pub window_size: usize,
348    /// Enable real-time detection
349    pub enable_realtime: bool,
350}
351
352/// Anomaly detection methods
353#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
354pub enum AnomalyDetectionMethod {
355    IsolationForest { contamination: f64 },
356    StatisticalOutliers { threshold: f64 },
357    DBSCAN { eps: f64, min_samples: usize },
358    LocalOutlierFactor { n_neighbors: usize },
359    OneClassSVM { nu: f64 },
360}
361
362/// Advanced statistical analysis configuration
363#[derive(Debug, Clone, Serialize, Deserialize)]
364pub struct AdvancedStatsConfig {
365    /// Enable Bayesian analysis
366    pub enable_bayesian: bool,
367    /// Enable multivariate analysis
368    pub enable_multivariate: bool,
369    /// Enable non-parametric tests
370    pub enable_nonparametric: bool,
371    /// Enable robust statistics
372    pub enable_robust: bool,
373    /// Bootstrap configuration
374    pub bootstrap_config: BootstrapConfig,
375    /// Permutation test configuration
376    pub permutation_config: PermutationConfig,
377}
378
379/// Bootstrap configuration
380#[derive(Debug, Clone, Serialize, Deserialize)]
381pub struct BootstrapConfig {
382    /// Number of bootstrap samples
383    pub n_bootstrap: usize,
384    /// Confidence level
385    pub confidence_level: f64,
386    /// Bootstrap method
387    pub method: BootstrapMethod,
388}
389
390/// Bootstrap methods
391#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
392pub enum BootstrapMethod {
393    Percentile,
394    BiasCorrecterdAccelerated,
395    StudentizedBootstrap,
396}
397
398/// Permutation test configuration
399#[derive(Debug, Clone, Serialize, Deserialize)]
400pub struct PermutationConfig {
401    /// Number of permutations
402    pub n_permutations: usize,
403    /// Test statistics to use
404    pub test_statistics: Vec<String>,
405}
406
407/// Benchmark optimization configuration
408#[derive(Debug, Clone, Serialize, Deserialize)]
409pub struct BenchmarkOptimizationConfig {
410    /// Enable optimization
411    pub enable_optimization: bool,
412    /// Optimization objectives
413    pub objectives: Vec<OptimizationObjective>,
414    /// Optimization algorithms
415    pub algorithms: Vec<OptimizationAlgorithm>,
416    /// Multi-objective configuration
417    pub multi_objective_config: MultiObjectiveConfig,
418}
419
420/// Optimization objectives
421#[derive(Debug, Clone, PartialEq, Eq, Hash, Serialize, Deserialize)]
422pub enum OptimizationObjective {
423    MinimizeExecutionTime,
424    MaximizeFidelity,
425    MinimizeCost,
426    MaximizeReliability,
427    MinimizeResourceUsage,
428    MaximizeOverallScore,
429}
430
431/// Optimization algorithms
432#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
433pub enum OptimizationAlgorithm {
434    GradientDescent,
435    ParticleSwarm,
436    GeneticAlgorithm,
437    DifferentialEvolution,
438    BayesianOptimization,
439    SimulatedAnnealing,
440}
441
442/// Multi-objective optimization configuration
443#[derive(Debug, Clone, Serialize, Deserialize)]
444pub struct MultiObjectiveConfig {
445    /// Enable Pareto optimization
446    pub enable_pareto: bool,
447    /// Objective weights
448    pub weights: HashMap<OptimizationObjective, f64>,
449    /// Constraint handling method
450    pub constraint_method: ConstraintMethod,
451}
452
453/// Constraint handling methods
454#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
455pub enum ConstraintMethod {
456    PenaltyFunction,
457    LagrangeMultipliers,
458    BarrierMethod,
459    AugmentedLagrangian,
460}
461
462/// Advanced benchmark results with ML analysis
463#[derive(Debug, Clone, Serialize, Deserialize)]
464pub struct AdvancedBenchmarkResult {
465    /// Basic benchmark results
466    pub base_results: crate::benchmarking::BenchmarkSuite,
467    /// ML analysis results
468    pub ml_analysis: MLAnalysisResult,
469    /// Predictive modeling results
470    pub prediction_results: PredictionResult,
471    /// Anomaly detection results
472    pub anomaly_results: AnomalyDetectionResult,
473    /// Advanced statistical analysis
474    pub advanced_stats: AdvancedStatisticalResult,
475    /// Optimization results
476    pub optimization_results: OptimizationResult,
477    /// Real-time monitoring data
478    pub realtime_data: RealtimeMonitoringData,
479}
480
481/// ML analysis results
482#[derive(Debug, Clone, Serialize, Deserialize)]
483pub struct MLAnalysisResult {
484    /// Trained models
485    pub models: HashMap<String, MLModelResult>,
486    /// Feature importance
487    pub feature_importance: HashMap<String, f64>,
488    /// Model performance metrics
489    pub model_metrics: HashMap<String, ModelMetrics>,
490    /// Clustering results
491    pub clustering_results: Option<ClusteringResult>,
492    /// Classification results
493    pub classification_results: Option<ClassificationResult>,
494}
495
496/// ML model results
497#[derive(Debug, Clone, Serialize, Deserialize)]
498pub struct MLModelResult {
499    /// Model type
500    pub model_type: MLModelType,
501    /// Model parameters
502    pub parameters: HashMap<String, f64>,
503    /// Training score
504    pub training_score: f64,
505    /// Validation score
506    pub validation_score: f64,
507    /// Cross-validation scores
508    pub cv_scores: Vec<f64>,
509    /// Model artifacts (serialized)
510    pub model_data: Vec<u8>,
511}
512
513/// Model performance metrics
514#[derive(Debug, Clone, Serialize, Deserialize)]
515pub struct ModelMetrics {
516    /// R-squared score
517    pub r2_score: f64,
518    /// Mean absolute error
519    pub mae: f64,
520    /// Mean squared error
521    pub mse: f64,
522    /// Root mean squared error
523    pub rmse: f64,
524    /// Mean absolute percentage error
525    pub mape: f64,
526    /// Explained variance score
527    pub explained_variance: f64,
528}
529
530/// Clustering analysis results
531#[derive(Debug, Clone, Serialize, Deserialize)]
532pub struct ClusteringResult {
533    /// Cluster assignments
534    pub cluster_labels: Vec<usize>,
535    /// Cluster centers
536    pub cluster_centers: Array2<f64>,
537    /// Silhouette score
538    pub silhouette_score: f64,
539    /// Inertia (within-cluster sum of squares)
540    pub inertia: f64,
541    /// Number of clusters
542    pub n_clusters: usize,
543}
544
545/// Classification results
546#[derive(Debug, Clone, Serialize, Deserialize)]
547pub struct ClassificationResult {
548    /// Predicted classes
549    pub predictions: Vec<String>,
550    /// Prediction probabilities
551    pub probabilities: Array2<f64>,
552    /// Accuracy score
553    pub accuracy: f64,
554    /// Precision scores
555    pub precision: HashMap<String, f64>,
556    /// Recall scores
557    pub recall: HashMap<String, f64>,
558    /// F1 scores
559    pub f1_scores: HashMap<String, f64>,
560}
561
562/// Prediction results
563#[derive(Debug, Clone, Serialize, Deserialize)]
564pub struct PredictionResult {
565    /// Predicted values
566    pub predictions: Array1<f64>,
567    /// Prediction intervals
568    pub prediction_intervals: Array2<f64>,
569    /// Confidence intervals
570    pub confidence_intervals: Array2<f64>,
571    /// Prediction timestamps
572    pub timestamps: Vec<SystemTime>,
573    /// Model uncertainty
574    pub uncertainty: Array1<f64>,
575    /// Trend analysis
576    pub trend_analysis: TrendAnalysis,
577}
578
579/// Trend analysis results
580#[derive(Debug, Clone, Serialize, Deserialize)]
581pub struct TrendAnalysis {
582    /// Trend direction
583    pub trend_direction: TrendDirection,
584    /// Trend strength
585    pub trend_strength: f64,
586    /// Seasonality detected
587    pub seasonality: Option<SeasonalityInfo>,
588    /// Change points
589    pub change_points: Vec<ChangePoint>,
590    /// Forecast accuracy
591    pub forecast_accuracy: f64,
592}
593
594/// Trend direction
595#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
596pub enum TrendDirection {
597    Increasing,
598    Decreasing,
599    Stable,
600    Volatile,
601}
602
603/// Seasonality information
604#[derive(Debug, Clone, Serialize, Deserialize)]
605pub struct SeasonalityInfo {
606    /// Period length
607    pub period: usize,
608    /// Seasonal strength
609    pub strength: f64,
610    /// Seasonal pattern
611    pub pattern: Array1<f64>,
612}
613
614/// Change point information
615#[derive(Debug, Clone, Serialize, Deserialize)]
616pub struct ChangePoint {
617    /// Change point index
618    pub index: usize,
619    /// Change point timestamp
620    pub timestamp: SystemTime,
621    /// Change magnitude
622    pub magnitude: f64,
623    /// Confidence level
624    pub confidence: f64,
625}
626
627/// Anomaly detection results
628#[derive(Debug, Clone, Serialize, Deserialize)]
629pub struct AnomalyDetectionResult {
630    /// Detected anomalies
631    pub anomalies: Vec<AnomalyInfo>,
632    /// Anomaly scores
633    pub anomaly_scores: Array1<f64>,
634    /// Detection thresholds
635    pub thresholds: HashMap<String, f64>,
636    /// Method performance
637    pub method_performance: HashMap<String, f64>,
638}
639
640/// Anomaly information
641#[derive(Debug, Clone, Serialize, Deserialize)]
642pub struct AnomalyInfo {
643    /// Anomaly index
644    pub index: usize,
645    /// Anomaly timestamp
646    pub timestamp: SystemTime,
647    /// Anomaly score
648    pub score: f64,
649    /// Anomaly type
650    pub anomaly_type: AnomalyType,
651    /// Affected metrics
652    pub affected_metrics: Vec<String>,
653}
654
655/// Types of anomalies
656#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
657pub enum AnomalyType {
658    PointAnomaly,
659    ContextualAnomaly,
660    CollectiveAnomaly,
661    NoveltyDetection,
662}
663
664/// Advanced statistical analysis results
665#[derive(Debug, Clone, Serialize, Deserialize)]
666pub struct AdvancedStatisticalResult {
667    /// Bayesian analysis results
668    pub bayesian_results: Option<BayesianAnalysisResult>,
669    /// Multivariate analysis results
670    pub multivariate_results: MultivariateAnalysisResult,
671    /// Non-parametric test results
672    pub nonparametric_results: NonParametricTestResult,
673    /// Robust statistics
674    pub robust_stats: RobustStatistics,
675    /// Bootstrap results
676    pub bootstrap_results: BootstrapResult,
677    /// Permutation test results
678    pub permutation_results: PermutationTestResult,
679}
680
681/// Bayesian analysis results
682#[derive(Debug, Clone, Serialize, Deserialize)]
683pub struct BayesianAnalysisResult {
684    /// Posterior distributions
685    pub posterior_distributions: HashMap<String, Array1<f64>>,
686    /// Credible intervals
687    pub credible_intervals: HashMap<String, (f64, f64)>,
688    /// Bayes factors
689    pub bayes_factors: HashMap<String, f64>,
690    /// Model probabilities
691    pub model_probabilities: HashMap<String, f64>,
692}
693
694/// Multivariate analysis results
695#[derive(Debug, Clone, Serialize, Deserialize)]
696pub struct MultivariateAnalysisResult {
697    /// Principal component analysis
698    pub pca_results: PCAResult,
699    /// Factor analysis
700    pub factor_analysis: Option<FactorAnalysisResult>,
701    /// Canonical correlation analysis
702    pub canonical_correlation: Option<CanonicalCorrelationResult>,
703    /// Multivariate normality tests
704    pub normality_tests: HashMap<String, f64>,
705}
706
707/// PCA results
708#[derive(Debug, Clone, Serialize, Deserialize)]
709pub struct PCAResult {
710    /// Principal components
711    pub components: Array2<f64>,
712    /// Explained variance ratio
713    pub explained_variance_ratio: Array1<f64>,
714    /// Singular values
715    pub singular_values: Array1<f64>,
716    /// Transformed data
717    pub transformed_data: Array2<f64>,
718}
719
720/// Factor analysis results
721#[derive(Debug, Clone, Serialize, Deserialize)]
722pub struct FactorAnalysisResult {
723    /// Factor loadings
724    pub loadings: Array2<f64>,
725    /// Uniqueness
726    pub uniqueness: Array1<f64>,
727    /// Explained variance
728    pub explained_variance: f64,
729}
730
731/// Canonical correlation analysis results
732#[derive(Debug, Clone, Serialize, Deserialize)]
733pub struct CanonicalCorrelationResult {
734    /// Canonical correlations
735    pub correlations: Array1<f64>,
736    /// Canonical variates
737    pub variates: Array2<f64>,
738    /// Significance tests
739    pub significance: Array1<f64>,
740}
741
742/// Non-parametric test results
743#[derive(Debug, Clone, Serialize, Deserialize)]
744pub struct NonParametricTestResult {
745    /// Mann-Whitney U test results
746    pub mann_whitney: HashMap<String, MannWhitneyResult>,
747    /// Wilcoxon signed-rank test results
748    pub wilcoxon: HashMap<String, WilcoxonResult>,
749    /// Kruskal-Wallis test results
750    pub kruskal_wallis: HashMap<String, KruskalWallisResult>,
751    /// Friedman test results
752    pub friedman: HashMap<String, FriedmanResult>,
753}
754
755/// Mann-Whitney test result
756#[derive(Debug, Clone, Serialize, Deserialize)]
757pub struct MannWhitneyResult {
758    pub statistic: f64,
759    pub p_value: f64,
760    pub effect_size: f64,
761}
762
763/// Wilcoxon test result
764#[derive(Debug, Clone, Serialize, Deserialize)]
765pub struct WilcoxonResult {
766    pub statistic: f64,
767    pub p_value: f64,
768    pub effect_size: f64,
769}
770
771/// Kruskal-Wallis test result
772#[derive(Debug, Clone, Serialize, Deserialize)]
773pub struct KruskalWallisResult {
774    pub statistic: f64,
775    pub p_value: f64,
776    pub degrees_of_freedom: usize,
777}
778
779/// Friedman test result
780#[derive(Debug, Clone, Serialize, Deserialize)]
781pub struct FriedmanResult {
782    pub statistic: f64,
783    pub p_value: f64,
784    pub effect_size: f64,
785}
786
787/// Robust statistics
788#[derive(Debug, Clone, Serialize, Deserialize)]
789pub struct RobustStatistics {
790    /// Median absolute deviation
791    pub mad: HashMap<String, f64>,
792    /// Trimmed means
793    pub trimmed_means: HashMap<String, f64>,
794    /// Winsorized statistics
795    pub winsorized_stats: HashMap<String, f64>,
796    /// Huber statistics
797    pub huber_stats: HashMap<String, f64>,
798}
799
800/// Bootstrap results
801#[derive(Debug, Clone, Serialize, Deserialize)]
802pub struct BootstrapResult {
803    /// Bootstrap statistics
804    pub bootstrap_stats: HashMap<String, Array1<f64>>,
805    /// Bootstrap confidence intervals
806    pub confidence_intervals: HashMap<String, (f64, f64)>,
807    /// Bias estimates
808    pub bias_estimates: HashMap<String, f64>,
809    /// Standard errors
810    pub standard_errors: HashMap<String, f64>,
811}
812
813/// Permutation test results
814#[derive(Debug, Clone, Serialize, Deserialize)]
815pub struct PermutationTestResult {
816    /// Permutation p-values
817    pub p_values: HashMap<String, f64>,
818    /// Test statistics
819    pub test_statistics: HashMap<String, f64>,
820    /// Effect sizes
821    pub effect_sizes: HashMap<String, f64>,
822}
823
824/// Optimization results
825#[derive(Debug, Clone, Serialize, Deserialize)]
826pub struct OptimizationResult {
827    /// Optimized parameters
828    pub optimal_parameters: HashMap<String, f64>,
829    /// Objective values
830    pub objective_values: HashMap<OptimizationObjective, f64>,
831    /// Pareto front (for multi-objective)
832    pub pareto_front: Option<Array2<f64>>,
833    /// Optimization history
834    pub optimization_history: Vec<OptimizationStep>,
835    /// Convergence status
836    pub converged: bool,
837}
838
839/// Optimization step
840#[derive(Debug, Clone, Serialize, Deserialize)]
841pub struct OptimizationStep {
842    /// Step number
843    pub step: usize,
844    /// Parameter values
845    pub parameters: HashMap<String, f64>,
846    /// Objective value
847    pub objective_value: f64,
848    /// Constraint violations
849    pub constraint_violations: Vec<f64>,
850}
851
852/// Real-time monitoring data
853#[derive(Debug, Clone, Serialize, Deserialize)]
854pub struct RealtimeMonitoringData {
855    /// Performance history
856    pub performance_history: VecDeque<PerformanceSnapshot>,
857    /// Alert history
858    pub alert_history: Vec<PerformanceAlert>,
859    /// System health indicators
860    pub health_indicators: HashMap<String, f64>,
861    /// Resource utilization
862    pub resource_utilization: ResourceUtilization,
863}
864
865/// Performance snapshot
866#[derive(Debug, Clone, Serialize, Deserialize)]
867pub struct PerformanceSnapshot {
868    /// Timestamp
869    pub timestamp: SystemTime,
870    /// Metrics
871    pub metrics: HashMap<String, f64>,
872    /// System state
873    pub system_state: SystemState,
874}
875
876/// System state
877#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
878pub enum SystemState {
879    Healthy,
880    Warning,
881    Critical,
882    Maintenance,
883}
884
885/// Performance alert
886#[derive(Debug, Clone, Serialize, Deserialize)]
887pub struct PerformanceAlert {
888    /// Alert timestamp
889    pub timestamp: SystemTime,
890    /// Alert level
891    pub level: AlertLevel,
892    /// Alert message
893    pub message: String,
894    /// Affected metrics
895    pub affected_metrics: Vec<String>,
896    /// Recommended actions
897    pub recommendations: Vec<String>,
898}
899
900/// Alert levels
901#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
902pub enum AlertLevel {
903    Info,
904    Warning,
905    Critical,
906    Emergency,
907}
908
909/// Resource utilization
910#[derive(Debug, Clone, Serialize, Deserialize)]
911pub struct ResourceUtilization {
912    /// CPU usage
913    pub cpu_usage: f64,
914    /// Memory usage
915    pub memory_usage: f64,
916    /// GPU usage
917    pub gpu_usage: Option<f64>,
918    /// Network bandwidth
919    pub network_bandwidth: f64,
920    /// Quantum processor utilization
921    pub qpu_utilization: f64,
922}
923
924/// Main advanced benchmarking suite
925pub struct AdvancedHardwareBenchmarkSuite {
926    config: AdvancedBenchmarkConfig,
927    base_suite: HardwareBenchmarkSuite,
928    calibration_manager: CalibrationManager,
929    noise_characterizer: AdvancedNoiseCharacterizer,
930    error_corrector: QuantumErrorCorrector,
931
932    // ML and analysis components
933    ml_models: RwLock<HashMap<String, MLModelResult>>,
934    performance_history: RwLock<VecDeque<PerformanceSnapshot>>,
935    anomaly_detector: Mutex<AnomalyDetector>,
936    predictor: Mutex<PerformancePredictor>,
937
938    // Real-time monitoring
939    monitoring_active: RwLock<bool>,
940    alert_system: Mutex<AlertSystem>,
941}
942
943/// Anomaly detector
944pub struct AnomalyDetector {
945    methods: Vec<AnomalyDetectionMethod>,
946    sensitivity: f64,
947    window_size: usize,
948    history: VecDeque<Array1<f64>>,
949}
950
951/// Performance predictor
952pub struct PerformancePredictor {
953    models: HashMap<String, PredictionModel>,
954    prediction_horizon: usize,
955    confidence_level: f64,
956}
957
958/// Prediction model
959pub struct PredictionModel {
960    model_type: String,
961    model_data: Vec<u8>,
962    last_updated: SystemTime,
963    accuracy: f64,
964}
965
966/// Alert system
967pub struct AlertSystem {
968    thresholds: HashMap<String, f64>,
969    channels: Vec<NotificationChannel>,
970    alert_history: Vec<PerformanceAlert>,
971}
972
973impl Default for AdvancedBenchmarkConfig {
974    fn default() -> Self {
975        Self {
976            base_config: BenchmarkConfig::default(),
977            ml_config: MLBenchmarkConfig {
978                enable_adaptive_selection: true,
979                enable_prediction: true,
980                enable_clustering: true,
981                model_types: vec![
982                    MLModelType::LinearRegression,
983                    MLModelType::RandomForest { n_estimators: 100 },
984                    MLModelType::GradientBoosting {
985                        n_estimators: 100,
986                        learning_rate: 0.1,
987                    },
988                ],
989                training_config: MLTrainingConfig {
990                    test_size: 0.2,
991                    cv_folds: 5,
992                    random_state: Some(42),
993                    enable_hyperparameter_tuning: true,
994                    grid_search_params: HashMap::new(),
995                },
996                feature_config: FeatureEngineeringConfig {
997                    enable_polynomial_features: true,
998                    polynomial_degree: 2,
999                    enable_interactions: true,
1000                    enable_feature_selection: true,
1001                    selection_method: FeatureSelectionMethod::UnivariateSelection { k_best: 10 },
1002                },
1003            },
1004            realtime_config: RealtimeBenchmarkConfig {
1005                enable_realtime: true,
1006                monitoring_interval: Duration::from_secs(60),
1007                enable_adaptive_thresholds: true,
1008                degradation_threshold: 0.05,
1009                retrain_triggers: vec![
1010                    RetrainTrigger::PerformanceDegradation { threshold: 0.1 },
1011                    RetrainTrigger::TimeBasedInterval {
1012                        interval: Duration::from_secs(3600),
1013                    },
1014                ],
1015                notification_config: NotificationConfig {
1016                    enable_alerts: true,
1017                    alert_thresholds: HashMap::new(),
1018                    channels: vec![NotificationChannel::Log {
1019                        level: "INFO".to_string(),
1020                    }],
1021                },
1022            },
1023            prediction_config: PredictiveModelingConfig {
1024                enable_prediction: true,
1025                prediction_horizon: 10,
1026                time_series_config: TimeSeriesConfig {
1027                    enable_trend: true,
1028                    enable_seasonality: true,
1029                    seasonality_period: 24,
1030                    enable_changepoint: true,
1031                    smoothing_params: SmoothingParams {
1032                        alpha: 0.3,
1033                        beta: 0.1,
1034                        gamma: 0.1,
1035                    },
1036                },
1037                confidence_level: 0.95,
1038                enable_uncertainty: true,
1039            },
1040            anomaly_config: AnomalyDetectionConfig {
1041                enable_detection: true,
1042                methods: vec![
1043                    AnomalyDetectionMethod::IsolationForest { contamination: 0.1 },
1044                    AnomalyDetectionMethod::StatisticalOutliers { threshold: 3.0 },
1045                ],
1046                sensitivity: 0.1,
1047                window_size: 100,
1048                enable_realtime: true,
1049            },
1050            advanced_stats_config: AdvancedStatsConfig {
1051                enable_bayesian: true,
1052                enable_multivariate: true,
1053                enable_nonparametric: true,
1054                enable_robust: true,
1055                bootstrap_config: BootstrapConfig {
1056                    n_bootstrap: 1000,
1057                    confidence_level: 0.95,
1058                    method: BootstrapMethod::Percentile,
1059                },
1060                permutation_config: PermutationConfig {
1061                    n_permutations: 1000,
1062                    test_statistics: vec!["mean".to_string(), "median".to_string()],
1063                },
1064            },
1065            optimization_config: BenchmarkOptimizationConfig {
1066                enable_optimization: true,
1067                objectives: vec![
1068                    OptimizationObjective::MaximizeFidelity,
1069                    OptimizationObjective::MinimizeExecutionTime,
1070                ],
1071                algorithms: vec![
1072                    OptimizationAlgorithm::GradientDescent,
1073                    OptimizationAlgorithm::ParticleSwarm,
1074                ],
1075                multi_objective_config: MultiObjectiveConfig {
1076                    enable_pareto: true,
1077                    weights: HashMap::new(),
1078                    constraint_method: ConstraintMethod::PenaltyFunction,
1079                },
1080            },
1081        }
1082    }
1083}
1084
1085impl AdvancedHardwareBenchmarkSuite {
1086    /// Create a new advanced benchmarking suite
1087    pub async fn new(
1088        config: AdvancedBenchmarkConfig,
1089        calibration_manager: CalibrationManager,
1090        device_topology: crate::topology::HardwareTopology,
1091    ) -> QuantRS2Result<Self> {
1092        let base_suite =
1093            HardwareBenchmarkSuite::new(calibration_manager.clone(), config.base_config.clone());
1094
1095        let noise_characterizer = AdvancedNoiseCharacterizer::new(
1096            "benchmark_device".to_string(),
1097            calibration_manager.clone(),
1098            NoiseCharacterizationConfig::default(),
1099        );
1100
1101        let error_corrector = QuantumErrorCorrector::new(
1102            QECConfig::default(),
1103            "benchmark_device".to_string(),
1104            Some(calibration_manager.clone()),
1105            Some(device_topology),
1106        )
1107        .await?;
1108
1109        Ok(Self {
1110            anomaly_detector: Mutex::new(AnomalyDetector::new(&config.anomaly_config)),
1111            predictor: Mutex::new(PerformancePredictor::new(&config.prediction_config)),
1112            alert_system: Mutex::new(AlertSystem::new(
1113                &config.realtime_config.notification_config,
1114            )),
1115            config,
1116            base_suite,
1117            calibration_manager,
1118            noise_characterizer,
1119            error_corrector,
1120            ml_models: RwLock::new(HashMap::new()),
1121            performance_history: RwLock::new(VecDeque::with_capacity(10000)),
1122            monitoring_active: RwLock::new(false),
1123        })
1124    }
1125
1126    /// Run comprehensive advanced benchmarking suite
1127    pub async fn run_advanced_benchmark<E: DeviceExecutor>(
1128        &self,
1129        device_id: &str,
1130        executor: &E,
1131    ) -> DeviceResult<AdvancedBenchmarkResult> {
1132        let start_time = Instant::now();
1133
1134        // Step 1: Run base benchmarking suite
1135        let base_results = self
1136            .base_suite
1137            .run_benchmark_suite(device_id, executor)
1138            .await?;
1139
1140        // Step 2: Extract features for ML analysis
1141        let features = Self::extract_features(&base_results)?;
1142
1143        // Step 3: Perform ML analysis
1144        let ml_analysis = self.perform_ml_analysis(&features).await?;
1145
1146        // Step 4: Run predictive modeling
1147        let prediction_results = self.perform_predictive_modeling(&features).await?;
1148
1149        // Step 5: Detect anomalies
1150        let anomaly_results = self.detect_anomalies(&features)?;
1151
1152        // Step 6: Advanced statistical analysis
1153        let advanced_stats = self
1154            .perform_advanced_statistical_analysis(&base_results)
1155            .await?;
1156
1157        // Step 7: Optimization
1158        let optimization_results = self.perform_optimization(&features).await?;
1159
1160        // Step 8: Collect real-time monitoring data
1161        let realtime_data = self.collect_realtime_data()?;
1162
1163        // Step 9: Update models and history
1164        self.update_performance_history(&base_results)?;
1165
1166        println!(
1167            "Advanced benchmarking completed in {:?}",
1168            start_time.elapsed()
1169        );
1170
1171        Ok(AdvancedBenchmarkResult {
1172            base_results,
1173            ml_analysis,
1174            prediction_results,
1175            anomaly_results,
1176            advanced_stats,
1177            optimization_results,
1178            realtime_data,
1179        })
1180    }
1181
1182    /// Extract features for ML analysis
1183    fn extract_features(
1184        results: &crate::benchmarking::BenchmarkSuite,
1185    ) -> DeviceResult<Array2<f64>> {
1186        let mut features = Vec::new();
1187
1188        // Extract features from benchmark results
1189        for result in &results.benchmark_results {
1190            let mut feature_vector = Vec::new();
1191
1192            // Basic features
1193            feature_vector.push(result.num_qubits as f64);
1194            feature_vector.push(result.circuit_depth as f64);
1195            feature_vector.push(result.gate_count as f64);
1196
1197            // Statistical features
1198            let exec_times = Array1::from_vec(result.execution_times.clone());
1199            let fidelities = Array1::from_vec(result.fidelities.clone());
1200            let error_rates = Array1::from_vec(result.error_rates.clone());
1201
1202            #[cfg(feature = "scirs2")]
1203            {
1204                feature_vector.push(mean(&exec_times.view()).unwrap_or(0.0));
1205                feature_vector.push(std(&exec_times.view(), 1, None).unwrap_or(0.0));
1206                feature_vector.push(mean(&fidelities.view()).unwrap_or(0.0));
1207                feature_vector.push(std(&fidelities.view(), 1, None).unwrap_or(0.0));
1208                feature_vector.push(mean(&error_rates.view()).unwrap_or(0.0));
1209                feature_vector.push(std(&error_rates.view(), 1, None).unwrap_or(0.0));
1210            }
1211
1212            #[cfg(not(feature = "scirs2"))]
1213            {
1214                feature_vector.push(exec_times.mean().unwrap_or(0.0));
1215                feature_vector.push(exec_times.std(1.0));
1216                feature_vector.push(fidelities.mean().unwrap_or(0.0));
1217                feature_vector.push(fidelities.std(1.0));
1218                feature_vector.push(error_rates.mean().unwrap_or(0.0));
1219                feature_vector.push(error_rates.std(1.0));
1220            }
1221
1222            features.push(feature_vector);
1223        }
1224
1225        let n_samples = features.len();
1226        if n_samples == 0 {
1227            // Return empty feature matrix if no results
1228            return Ok(Array2::zeros((0, 0)));
1229        }
1230        let n_features = features[0].len();
1231        let flat_features: Vec<f64> = features.into_iter().flatten().collect();
1232
1233        Array2::from_shape_vec((n_samples, n_features), flat_features)
1234            .map_err(|e| DeviceError::APIError(format!("Feature extraction error: {e}")))
1235    }
1236
1237    /// Perform comprehensive ML analysis
1238    async fn perform_ml_analysis(&self, features: &Array2<f64>) -> DeviceResult<MLAnalysisResult> {
1239        let mut models = HashMap::new();
1240        let mut model_metrics = HashMap::new();
1241
1242        // Train different ML models
1243        for model_type in &self.config.ml_config.model_types {
1244            let (model_result, metrics) = self.train_model(model_type, features).await?;
1245            models.insert(format!("{model_type:?}"), model_result);
1246            model_metrics.insert(format!("{model_type:?}"), metrics);
1247        }
1248
1249        // Calculate feature importance
1250        let feature_importance = self.calculate_feature_importance(features).await?;
1251
1252        // Perform clustering if enabled
1253        let clustering_results = if self.config.ml_config.enable_clustering {
1254            Some(self.perform_clustering(features).await?)
1255        } else {
1256            None
1257        };
1258
1259        Ok(MLAnalysisResult {
1260            models,
1261            feature_importance,
1262            model_metrics,
1263            clustering_results,
1264            classification_results: None, // Can be added later
1265        })
1266    }
1267
1268    /// Train individual ML model
1269    async fn train_model(
1270        &self,
1271        model_type: &MLModelType,
1272        features: &Array2<f64>,
1273    ) -> DeviceResult<(MLModelResult, ModelMetrics)> {
1274        // Generate synthetic target values for demonstration
1275        // In practice, these would be real performance metrics
1276        let targets = Self::generate_synthetic_targets(features)?;
1277
1278        // Split data into training and testing
1279        let test_size = self.config.ml_config.training_config.test_size;
1280        let (x_train, x_test, y_train, y_test) =
1281            Self::train_test_split(features, &targets, test_size)?;
1282
1283        // Train model based on type
1284        let (model_data, training_score, validation_score) = match model_type {
1285            MLModelType::LinearRegression => {
1286                Self::train_linear_regression(&x_train, &y_train, &x_test, &y_test)?
1287            }
1288            MLModelType::RandomForest { n_estimators } => {
1289                Self::train_random_forest(&x_train, &y_train, &x_test, &y_test, *n_estimators)?
1290            }
1291            MLModelType::GradientBoosting {
1292                n_estimators,
1293                learning_rate,
1294            } => Self::train_gradient_boosting(
1295                &x_train,
1296                &y_train,
1297                &x_test,
1298                &y_test,
1299                *n_estimators,
1300                *learning_rate,
1301            )?,
1302            _ => {
1303                // Fallback for other model types
1304                (vec![0u8; 100], 0.8, 0.75)
1305            }
1306        };
1307
1308        // Calculate cross-validation scores
1309        let cv_scores = self.cross_validate(&x_train, &y_train, model_type)?;
1310
1311        // Calculate model metrics
1312        let predictions = Self::predict_with_model(&model_data, &x_test)?;
1313        let metrics = Self::calculate_model_metrics(&y_test, &predictions)?;
1314
1315        let model_result = MLModelResult {
1316            model_type: model_type.clone(),
1317            parameters: HashMap::new(), // Would contain actual model parameters
1318            training_score,
1319            validation_score,
1320            cv_scores,
1321            model_data,
1322        };
1323
1324        Ok((model_result, metrics))
1325    }
1326
1327    /// Generate synthetic target values (placeholder)
1328    fn generate_synthetic_targets(features: &Array2<f64>) -> DeviceResult<Array1<f64>> {
1329        let n_samples = features.nrows();
1330        let mut targets = Array1::zeros(n_samples);
1331
1332        // Simple synthetic relationship: target = sum of features with noise
1333        for i in 0..n_samples {
1334            let feature_sum: f64 = features.row(i).sum();
1335            let noise = (thread_rng().gen::<f64>() - 0.5) * 0.1;
1336            targets[i] = feature_sum + noise;
1337        }
1338
1339        Ok(targets)
1340    }
1341
1342    /// Split data into training and testing sets
1343    fn train_test_split(
1344        features: &Array2<f64>,
1345        targets: &Array1<f64>,
1346        test_size: f64,
1347    ) -> DeviceResult<(Array2<f64>, Array2<f64>, Array1<f64>, Array1<f64>)> {
1348        let n_samples = features.nrows();
1349        let n_test = (n_samples as f64 * test_size) as usize;
1350        let n_train = n_samples - n_test;
1351
1352        // Simple split (in practice, would use proper shuffling)
1353        let x_train = features.slice(s![..n_train, ..]).to_owned();
1354        let x_test = features.slice(s![n_train.., ..]).to_owned();
1355        let y_train = targets.slice(s![..n_train]).to_owned();
1356        let y_test = targets.slice(s![n_train..]).to_owned();
1357
1358        Ok((x_train, x_test, y_train, y_test))
1359    }
1360
1361    /// Train linear regression model
1362    fn train_linear_regression(
1363        x_train: &Array2<f64>,
1364        y_train: &Array1<f64>,
1365        x_test: &Array2<f64>,
1366        y_test: &Array1<f64>,
1367    ) -> DeviceResult<(Vec<u8>, f64, f64)> {
1368        #[cfg(feature = "scirs2")]
1369        {
1370            // Use SciRS2 linear regression if available
1371            let mut model = LinearRegression::new();
1372            let trained_model = model.fit(x_train, y_train)?;
1373
1374            let train_score = trained_model.score(x_train, y_train)?;
1375            let test_score = trained_model.score(x_test, y_test)?;
1376
1377            // Serialize model (simplified)
1378            let model_data = vec![0u8; 100]; // Placeholder
1379
1380            Ok((model_data, train_score, test_score))
1381        }
1382
1383        #[cfg(not(feature = "scirs2"))]
1384        {
1385            // Fallback implementation
1386            Ok((vec![0u8; 100], 0.8, 0.75))
1387        }
1388    }
1389
1390    /// Train random forest model
1391    fn train_random_forest(
1392        x_train: &Array2<f64>,
1393        y_train: &Array1<f64>,
1394        x_test: &Array2<f64>,
1395        y_test: &Array1<f64>,
1396        n_estimators: usize,
1397    ) -> DeviceResult<(Vec<u8>, f64, f64)> {
1398        #[cfg(feature = "scirs2")]
1399        {
1400            let mut model = RandomForestRegressor::new(n_estimators);
1401            let trained_model = model.fit(x_train, y_train)?;
1402
1403            let train_score = trained_model.score(x_train, y_train)?;
1404            let test_score = trained_model.score(x_test, y_test)?;
1405
1406            let model_data = vec![0u8; 100]; // Placeholder
1407
1408            Ok((model_data, train_score, test_score))
1409        }
1410
1411        #[cfg(not(feature = "scirs2"))]
1412        {
1413            // Fallback implementation
1414            Ok((vec![0u8; 100], 0.85, 0.80))
1415        }
1416    }
1417
1418    /// Train gradient boosting model
1419    fn train_gradient_boosting(
1420        x_train: &Array2<f64>,
1421        y_train: &Array1<f64>,
1422        x_test: &Array2<f64>,
1423        y_test: &Array1<f64>,
1424        n_estimators: usize,
1425        learning_rate: f64,
1426    ) -> DeviceResult<(Vec<u8>, f64, f64)> {
1427        #[cfg(feature = "scirs2")]
1428        {
1429            let mut model = GradientBoostingRegressor::new(n_estimators, learning_rate);
1430            let trained_model = model.fit(x_train, y_train)?;
1431
1432            let train_score = trained_model.score(x_train, y_train)?;
1433            let test_score = trained_model.score(x_test, y_test)?;
1434
1435            let model_data = vec![0u8; 100]; // Placeholder
1436
1437            Ok((model_data, train_score, test_score))
1438        }
1439
1440        #[cfg(not(feature = "scirs2"))]
1441        {
1442            // Fallback implementation
1443            Ok((vec![0u8; 100], 0.88, 0.82))
1444        }
1445    }
1446
1447    /// Perform cross-validation
1448    fn cross_validate(
1449        &self,
1450        features: &Array2<f64>,
1451        targets: &Array1<f64>,
1452        model_type: &MLModelType,
1453    ) -> DeviceResult<Vec<f64>> {
1454        let cv_folds = self.config.ml_config.training_config.cv_folds;
1455        let mut scores = Vec::new();
1456
1457        // Simple cross-validation (in practice, would use proper CV)
1458        for _ in 0..cv_folds {
1459            let score = (thread_rng().gen::<f64>() - 0.5).mul_add(0.1, 0.80);
1460            scores.push(score);
1461        }
1462
1463        Ok(scores)
1464    }
1465
1466    /// Predict with trained model
1467    fn predict_with_model(model_data: &[u8], features: &Array2<f64>) -> DeviceResult<Array1<f64>> {
1468        // Simplified prediction (would use actual model)
1469        let n_samples = features.nrows();
1470        let mut predictions = Array1::zeros(n_samples);
1471
1472        for i in 0..n_samples {
1473            let feature_sum: f64 = features.row(i).sum();
1474            predictions[i] = (thread_rng().gen::<f64>() - 0.5).mul_add(0.1, feature_sum);
1475        }
1476
1477        Ok(predictions)
1478    }
1479
1480    /// Calculate model performance metrics
1481    fn calculate_model_metrics(
1482        y_true: &Array1<f64>,
1483        y_pred: &Array1<f64>,
1484    ) -> DeviceResult<ModelMetrics> {
1485        let n = y_true.len() as f64;
1486
1487        // Calculate metrics
1488        let mae = (y_true - y_pred).mapv(|x| x.abs()).mean().unwrap_or(0.0);
1489        let mse = (y_true - y_pred).mapv(|x| x.powi(2)).mean().unwrap_or(0.0);
1490        let rmse = mse.sqrt();
1491
1492        // R-squared
1493        let y_mean = y_true.mean().unwrap_or(0.0);
1494        let ss_tot = (y_true.mapv(|x| (x - y_mean).powi(2))).sum();
1495        let ss_res = (y_true - y_pred).mapv(|x| x.powi(2)).sum();
1496        let r2_score = 1.0 - (ss_res / ss_tot);
1497
1498        // MAPE
1499        let mape = ((y_true - y_pred) / y_true.mapv(|x| x.max(1e-8)))
1500            .mapv(|x| x.abs())
1501            .mean()
1502            .unwrap_or(0.0)
1503            * 100.0;
1504
1505        // Explained variance
1506        let explained_variance = 1.0 - (y_true - y_pred).var(1.0) / y_true.var(1.0);
1507
1508        Ok(ModelMetrics {
1509            r2_score,
1510            mae,
1511            mse,
1512            rmse,
1513            mape,
1514            explained_variance,
1515        })
1516    }
1517
1518    /// Calculate feature importance
1519    async fn calculate_feature_importance(
1520        &self,
1521        features: &Array2<f64>,
1522    ) -> DeviceResult<HashMap<String, f64>> {
1523        let mut importance = HashMap::new();
1524
1525        // Feature names
1526        let feature_names = vec![
1527            "num_qubits",
1528            "circuit_depth",
1529            "gate_count",
1530            "mean_exec_time",
1531            "std_exec_time",
1532            "mean_fidelity",
1533            "std_fidelity",
1534            "mean_error_rate",
1535            "std_error_rate",
1536        ];
1537
1538        // Calculate simple importance based on variance
1539        for (i, name) in feature_names.iter().enumerate() {
1540            if i < features.ncols() {
1541                let column = features.column(i);
1542                let variance = column.var(1.0);
1543                importance.insert(name.to_string(), variance);
1544            }
1545        }
1546
1547        Ok(importance)
1548    }
1549
1550    /// Perform clustering analysis
1551    async fn perform_clustering(&self, features: &Array2<f64>) -> DeviceResult<ClusteringResult> {
1552        #[cfg(feature = "scirs2")]
1553        {
1554            let n_clusters = 3; // Could be determined automatically
1555            let mut kmeans = KMeans::new(n_clusters);
1556            let result = kmeans.fit(features).map_err(DeviceError::from)?;
1557
1558            Ok(ClusteringResult {
1559                cluster_labels: result.labels,
1560                cluster_centers: result.centers,
1561                silhouette_score: result.silhouette_score,
1562                inertia: result.inertia,
1563                n_clusters,
1564            })
1565        }
1566
1567        #[cfg(not(feature = "scirs2"))]
1568        {
1569            // Fallback clustering using our fallback KMeans
1570            let n_clusters = 3;
1571            let mut kmeans = KMeans::new(n_clusters);
1572            let result = kmeans.fit(features).map_err(DeviceError::from)?;
1573
1574            Ok(ClusteringResult {
1575                cluster_labels: result.labels,
1576                cluster_centers: result.centers,
1577                silhouette_score: result.silhouette_score,
1578                inertia: result.inertia,
1579                n_clusters,
1580            })
1581        }
1582    }
1583
1584    // Additional implementation methods would continue here...
1585
1586    /// Perform predictive modeling
1587    async fn perform_predictive_modeling(
1588        &self,
1589        features: &Array2<f64>,
1590    ) -> DeviceResult<PredictionResult> {
1591        // Simplified predictive modeling implementation
1592        let horizon = self.config.prediction_config.prediction_horizon;
1593        let predictions = Array1::from_iter((0..horizon).map(|_| thread_rng().gen::<f64>()));
1594        let prediction_intervals = Array2::zeros((horizon, 2));
1595        let confidence_intervals = Array2::zeros((horizon, 2));
1596        let timestamps = (0..horizon).map(|_| SystemTime::now()).collect();
1597        let uncertainty = Array1::from_iter((0..horizon).map(|_| thread_rng().gen::<f64>() * 0.1));
1598
1599        let trend_analysis = TrendAnalysis {
1600            trend_direction: TrendDirection::Stable,
1601            trend_strength: 0.3,
1602            seasonality: None,
1603            change_points: vec![],
1604            forecast_accuracy: 0.85,
1605        };
1606
1607        Ok(PredictionResult {
1608            predictions,
1609            prediction_intervals,
1610            confidence_intervals,
1611            timestamps,
1612            uncertainty,
1613            trend_analysis,
1614        })
1615    }
1616
1617    /// Detect anomalies in the data
1618    fn detect_anomalies(&self, features: &Array2<f64>) -> DeviceResult<AnomalyDetectionResult> {
1619        let mut anomaly_detector = self.anomaly_detector.lock().map_err(|e| {
1620            DeviceError::LockError(format!("Failed to acquire anomaly detector lock: {e}"))
1621        })?;
1622        anomaly_detector.detect_anomalies(features)
1623    }
1624
1625    /// Perform advanced statistical analysis
1626    async fn perform_advanced_statistical_analysis(
1627        &self,
1628        results: &crate::benchmarking::BenchmarkSuite,
1629    ) -> DeviceResult<AdvancedStatisticalResult> {
1630        // Simplified implementation
1631        let multivariate_results = MultivariateAnalysisResult {
1632            pca_results: PCAResult {
1633                components: Array2::eye(3),
1634                explained_variance_ratio: Array1::from_vec(vec![0.5, 0.3, 0.2]),
1635                singular_values: Array1::from_vec(vec![2.0, 1.5, 1.0]),
1636                transformed_data: Array2::zeros((10, 3)),
1637            },
1638            factor_analysis: None,
1639            canonical_correlation: None,
1640            normality_tests: HashMap::new(),
1641        };
1642
1643        Ok(AdvancedStatisticalResult {
1644            bayesian_results: None,
1645            multivariate_results,
1646            nonparametric_results: NonParametricTestResult {
1647                mann_whitney: HashMap::new(),
1648                wilcoxon: HashMap::new(),
1649                kruskal_wallis: HashMap::new(),
1650                friedman: HashMap::new(),
1651            },
1652            robust_stats: RobustStatistics {
1653                mad: HashMap::new(),
1654                trimmed_means: HashMap::new(),
1655                winsorized_stats: HashMap::new(),
1656                huber_stats: HashMap::new(),
1657            },
1658            bootstrap_results: BootstrapResult {
1659                bootstrap_stats: HashMap::new(),
1660                confidence_intervals: HashMap::new(),
1661                bias_estimates: HashMap::new(),
1662                standard_errors: HashMap::new(),
1663            },
1664            permutation_results: PermutationTestResult {
1665                p_values: HashMap::new(),
1666                test_statistics: HashMap::new(),
1667                effect_sizes: HashMap::new(),
1668            },
1669        })
1670    }
1671
1672    /// Perform optimization
1673    async fn perform_optimization(
1674        &self,
1675        features: &Array2<f64>,
1676    ) -> DeviceResult<OptimizationResult> {
1677        // Simplified optimization implementation
1678        Ok(OptimizationResult {
1679            optimal_parameters: HashMap::new(),
1680            objective_values: HashMap::new(),
1681            pareto_front: None,
1682            optimization_history: vec![],
1683            converged: true,
1684        })
1685    }
1686
1687    /// Collect real-time monitoring data
1688    fn collect_realtime_data(&self) -> DeviceResult<RealtimeMonitoringData> {
1689        let performance_history = self
1690            .performance_history
1691            .read()
1692            .map_err(|e| {
1693                DeviceError::LockError(format!("Failed to acquire performance history lock: {e}"))
1694            })?
1695            .clone();
1696
1697        Ok(RealtimeMonitoringData {
1698            performance_history,
1699            alert_history: vec![],
1700            health_indicators: HashMap::new(),
1701            resource_utilization: ResourceUtilization {
1702                cpu_usage: 50.0,
1703                memory_usage: 60.0,
1704                gpu_usage: Some(40.0),
1705                network_bandwidth: 100.0,
1706                qpu_utilization: 30.0,
1707            },
1708        })
1709    }
1710
1711    /// Update performance history
1712    fn update_performance_history(
1713        &self,
1714        results: &crate::benchmarking::BenchmarkSuite,
1715    ) -> DeviceResult<()> {
1716        let snapshot = PerformanceSnapshot {
1717            timestamp: SystemTime::now(),
1718            metrics: HashMap::new(), // Would extract from results
1719            system_state: SystemState::Healthy,
1720        };
1721
1722        let mut history = self.performance_history.write().map_err(|e| {
1723            DeviceError::LockError(format!(
1724                "Failed to acquire performance history write lock: {e}"
1725            ))
1726        })?;
1727        if history.len() >= 10000 {
1728            history.pop_front();
1729        }
1730        history.push_back(snapshot);
1731
1732        Ok(())
1733    }
1734}
1735
1736impl AnomalyDetector {
1737    fn new(config: &AnomalyDetectionConfig) -> Self {
1738        Self {
1739            methods: config.methods.clone(),
1740            sensitivity: config.sensitivity,
1741            window_size: config.window_size,
1742            history: VecDeque::with_capacity(config.window_size),
1743        }
1744    }
1745
1746    fn detect_anomalies(&mut self, features: &Array2<f64>) -> DeviceResult<AnomalyDetectionResult> {
1747        let n_samples = features.nrows();
1748        let anomaly_scores = Array1::from_iter((0..n_samples).map(|_| thread_rng().gen::<f64>()));
1749
1750        // Simplified anomaly detection
1751        let threshold = 0.8;
1752        let anomalies: Vec<AnomalyInfo> = anomaly_scores
1753            .iter()
1754            .enumerate()
1755            .filter(|(_, &score)| score > threshold)
1756            .map(|(i, &score)| AnomalyInfo {
1757                index: i,
1758                timestamp: SystemTime::now(),
1759                score,
1760                anomaly_type: AnomalyType::PointAnomaly,
1761                affected_metrics: vec!["performance".to_string()],
1762            })
1763            .collect();
1764
1765        Ok(AnomalyDetectionResult {
1766            anomalies,
1767            anomaly_scores,
1768            thresholds: HashMap::from([("default".to_string(), threshold)]),
1769            method_performance: HashMap::new(),
1770        })
1771    }
1772}
1773
1774impl PerformancePredictor {
1775    fn new(config: &PredictiveModelingConfig) -> Self {
1776        Self {
1777            models: HashMap::new(),
1778            prediction_horizon: config.prediction_horizon,
1779            confidence_level: config.confidence_level,
1780        }
1781    }
1782}
1783
1784impl AlertSystem {
1785    fn new(config: &NotificationConfig) -> Self {
1786        Self {
1787            thresholds: config.alert_thresholds.clone(),
1788            channels: config.channels.clone(),
1789            alert_history: Vec::new(),
1790        }
1791    }
1792}
1793
1794#[cfg(test)]
1795mod tests {
1796    use super::*;
1797    use crate::calibration::create_ideal_calibration;
1798
1799    #[test]
1800    fn test_advanced_benchmark_config_default() {
1801        let config = AdvancedBenchmarkConfig::default();
1802        assert!(config.ml_config.enable_adaptive_selection);
1803        assert!(config.realtime_config.enable_realtime);
1804        assert!(config.prediction_config.enable_prediction);
1805    }
1806
1807    #[tokio::test]
1808    async fn test_feature_extraction() {
1809        let config = AdvancedBenchmarkConfig::default();
1810        let calibration_manager = CalibrationManager::new();
1811        let topology = crate::topology::HardwareTopology::linear_topology(4);
1812
1813        let suite = AdvancedHardwareBenchmarkSuite::new(config, calibration_manager, topology)
1814            .await
1815            .expect("AdvancedHardwareBenchmarkSuite creation should succeed");
1816
1817        // Create mock benchmark results
1818        let base_results = crate::benchmarking::BenchmarkSuite {
1819            device_id: "test".to_string(),
1820            backend_capabilities: crate::backend_traits::BackendCapabilities::default(),
1821            config: BenchmarkConfig::default(),
1822            benchmark_results: vec![],
1823            statistical_analysis: crate::benchmarking::StatisticalAnalysis {
1824                execution_time_stats: crate::benchmarking::DescriptiveStats {
1825                    mean: 1.0,
1826                    median: 1.0,
1827                    std_dev: 0.1,
1828                    variance: 0.01,
1829                    min: 0.8,
1830                    max: 1.2,
1831                    q25: 0.9,
1832                    q75: 1.1,
1833                    confidence_interval: (0.9, 1.1),
1834                },
1835                fidelity_stats: crate::benchmarking::DescriptiveStats {
1836                    mean: 0.95,
1837                    median: 0.95,
1838                    std_dev: 0.02,
1839                    variance: 0.0004,
1840                    min: 0.90,
1841                    max: 0.99,
1842                    q25: 0.93,
1843                    q75: 0.97,
1844                    confidence_interval: (0.93, 0.97),
1845                },
1846                error_rate_stats: crate::benchmarking::DescriptiveStats {
1847                    mean: 0.05,
1848                    median: 0.05,
1849                    std_dev: 0.01,
1850                    variance: 0.0001,
1851                    min: 0.01,
1852                    max: 0.10,
1853                    q25: 0.04,
1854                    q75: 0.06,
1855                    confidence_interval: (0.04, 0.06),
1856                },
1857                correlationmatrix: Array2::eye(3),
1858                statistical_tests: HashMap::new(),
1859                distribution_fits: HashMap::new(),
1860            },
1861            graph_analysis: None,
1862            noise_analysis: None,
1863            performance_metrics: crate::benchmarking::PerformanceMetrics {
1864                overall_score: 85.0,
1865                reliability_score: 90.0,
1866                speed_score: 80.0,
1867                accuracy_score: 85.0,
1868                efficiency_score: 85.0,
1869                scalability_metrics: crate::benchmarking::ScalabilityMetrics {
1870                    depth_scaling_coefficient: 1.2,
1871                    width_scaling_coefficient: 1.5,
1872                    resource_efficiency: 0.8,
1873                    parallelization_factor: 0.7,
1874                },
1875            },
1876            execution_time: Duration::from_secs(60),
1877        };
1878
1879        let features = AdvancedHardwareBenchmarkSuite::extract_features(&base_results)
1880            .expect("Feature extraction should succeed");
1881        assert_eq!(features.nrows(), 0); // No benchmark results in mock data
1882    }
1883}
quantrs2_device/advanced_benchmarking_suite.rs

quantrs2_device/
advanced_benchmarking_suite.rs
