quantrs2_ml/
lib.rs

1#![recursion_limit = "8192"]
2#![allow(warnings)]
3#![allow(clippy::all)]
4#![allow(clippy::pedantic)]
5#![allow(clippy::nursery)]
6#![allow(clippy::restriction)]
7
8//! # Quantum Machine Learning
9//!
10//! This crate provides quantum machine learning capabilities for the QuantRS2 framework.
11//! It includes quantum neural networks, variational algorithms, and specialized tools for
12//! high-energy physics data analysis, plus cutting-edge quantum ML algorithms.
13//!
14//! ## Core Features
15//!
16//! - Quantum Neural Networks
17//! - Variational Quantum Algorithms
18//! - High-Energy Physics Data Analysis
19//! - Quantum Reinforcement Learning
20//! - Quantum Generative Models
21//! - Quantum Kernels for Classification
22//! - Quantum-Enhanced Cryptographic Protocols
23//! - Quantum Blockchain and Distributed Ledger Technology
24//! - Quantum-Enhanced Natural Language Processing
25//! - Quantum Anomaly Detection and Outlier Analysis
26//!
27//! ## Cutting-Edge Quantum ML Algorithms
28//!
29//! - **Quantum Neural ODEs**: Continuous-depth quantum neural networks using quantum circuits to parameterize derivative functions
30//! - **Quantum Physics-Informed Neural Networks (QPINNs)**: Quantum neural networks that enforce physical laws and solve PDEs
31//! - **Quantum Reservoir Computing**: Leverages quantum dynamics for temporal data processing with quantum advantages
32//! - **Quantum Graph Attention Networks**: Combines graph neural networks with quantum attention mechanisms for complex graph analysis
33//!
34//! ## Recent Updates (v0.2.0)
35//!
36//! - Refined SciRS2 v0.5.0 Stable Release integration with unified patterns
37//! - Automatic differentiation leveraging SciRS2's linear algebra operations
38//! - Parallel training with `scirs2_core::parallel_ops`
39//! - SIMD-accelerated quantum kernel computations
40
41use fastrand;
42use std::error::Error;
43use thiserror::Error;
44
45pub mod barren_plateau;
46pub mod blockchain;
47pub mod classification;
48pub mod crypto;
49pub mod enhanced_gan;
50pub mod gan;
51pub mod hep;
52pub mod kernels;
53pub mod nlp;
54pub mod optimization;
55pub mod qcnn;
56pub mod qnn;
57pub mod qsvm;
58pub mod reinforcement;
59pub mod vae;
60pub mod variational;
61
62pub mod adversarial;
63pub mod anneal_integration;
64pub mod anomaly_detection;
65pub mod attention;
66pub mod autodiff;
67pub mod automl;
68pub mod benchmarking;
69pub mod boltzmann;
70pub mod circuit_integration;
71pub mod classical_ml_integration;
72pub mod clustering;
73pub mod computer_vision;
74pub mod continual_learning;
75pub mod continuous_rl;
76pub mod device_compilation;
77pub mod diffusion;
78pub mod dimensionality_reduction;
79pub mod domain_templates;
80pub mod error;
81pub mod error_mitigation;
82pub mod explainable_ai;
83pub mod federated;
84pub mod few_shot;
85pub mod gnn;
86#[cfg(feature = "gpu")]
87pub mod gpu_backend_impl;
88pub mod industry_examples;
89pub mod keras_api;
90pub mod lstm;
91pub mod meta_learning;
92pub mod model_zoo;
93pub mod onnx_export;
94pub mod performance_profiler;
95pub mod pytorch_api;
96pub mod quantum_advanced_diffusion;
97pub mod quantum_advantage_validator;
98pub mod quantum_continuous_flows;
99pub mod quantum_graph_attention;
100pub mod quantum_implicit_neural_representations;
101pub mod quantum_in_context_learning;
102pub mod quantum_llm;
103pub mod quantum_memory_networks;
104pub mod quantum_mixture_of_experts;
105pub mod quantum_nas;
106pub mod quantum_neural_odes;
107pub mod quantum_neural_radiance_fields;
108pub mod quantum_pinns;
109pub mod quantum_reservoir_computing;
110pub mod quantum_self_supervised_learning;
111pub mod quantum_transformer;
112pub mod recommender;
113pub mod scirs2_integration;
114pub mod simulator_backends;
115pub mod sklearn_compatibility;
116pub mod tensorflow_compatibility;
117pub mod time_series;
118pub mod torchquantum;
119pub mod transfer;
120pub mod tutorials;
121
122// Utilities module for calibration, metrics, preprocessing, etc.
123pub mod utils;
124
125// QAOA warm-start initialisation via spectral relaxation
126pub mod qaoa_warm_start;
127
128// VQE Natural Gradient entry point (wraps autodiff::QuantumAutoDiff)
129pub mod vqe_natural_gradient;
130
131// Advanced Quantum-Classical Hybrid AutoML Engine
132pub mod hybrid_automl_engine;
133
134/// Re-export error types for easier access
135pub use error::MLError;
136pub use error::Result;
137
138/// Prelude module for convenient imports
139pub mod prelude {
140    pub use crate::adversarial::{
141        create_comprehensive_defense, create_default_adversarial_config, AdversarialTrainingConfig,
142        QuantumAdversarialExample, QuantumAdversarialTrainer, QuantumAttackType,
143        QuantumDefenseStrategy, RobustnessMetrics,
144    };
145    pub use crate::anneal_integration::{
146        AnnealingClient, AnnealingParams, AnnealingResult,
147        AnnealingSchedule as MLAnnealingSchedule, CircuitOptimizationProblem,
148        FeatureSelectionProblem, HyperparameterProblem, IsingProblem, OptimizationResult,
149        PortfolioOptimizationProblem, QuantumMLAnnealer, QuantumMLOptimizationProblem,
150        QuantumMLQUBO,
151    };
152    pub use crate::anomaly_detection::{
153        AnomalyDetectionMethod, AnomalyMetrics, AnomalyResult, PerformanceConfig,
154        PreprocessingConfig as AnomalyPreprocessingConfig, QuantumAnomalyConfig,
155        QuantumAnomalyDetector, QuantumAnomalyMetrics, QuantumAutoencoder,
156        QuantumEnhancementConfig, QuantumIsolationForest, QuantumLOF, QuantumOneClassSVM,
157        RealtimeConfig, SpecializedDetectorConfig,
158    };
159    pub use crate::automl::{
160        create_comprehensive_automl_config, create_default_automl_config, AdvancedAutoMLFeatures,
161        AlgorithmSearchSpace, EnsembleSearchSpace, EvaluationConfig, HyperparameterSearchSpace,
162        MLTaskType, OptimizationObjective, QuantumAutoML, QuantumAutoMLConfig, QuantumConstraints,
163        QuantumEncodingMethod, SearchBudgetConfig, SearchSpaceConfig,
164    };
165    pub use crate::benchmarking::{
166        Benchmark, BenchmarkCategory, BenchmarkConfig, BenchmarkFramework, BenchmarkReport,
167        BenchmarkResults, BenchmarkRunResult, BenchmarkSummary, ScalingType,
168    };
169    pub use crate::blockchain::{ConsensusType, QuantumBlockchain, QuantumToken, SmartContract};
170    pub use crate::boltzmann::{
171        AnnealingSchedule, DeepBoltzmannMachine, QuantumBoltzmannMachine, QuantumRBM,
172    };
173    pub use crate::circuit_integration::{
174        BackendManager, DeviceTopology, ExpressionvityMetrics, HardwareAwareCompiler,
175        MLCircuitAnalyzer, MLCircuitOptimizer, OptimizationPass, ParameterizedLayer, QuantumLayer,
176        QuantumMLExecutor, QubitProperties, RotationAxis, TrainabilityMetrics,
177    };
178    pub use crate::classical_ml_integration::{
179        utils as pipeline_utils, AutoOptimizationConfig, ClassicalModel, DataPreprocessor,
180        DatasetInfo, EnsembleStrategy, HybridModel, HybridPipeline, HybridPipelineManager,
181        MinMaxScaler, ModelRegistry, ModelType, OptimizedPipeline, PerformanceProfile,
182        PipelineConfig, PipelineRecommendation, PipelineStage, PipelineTemplate,
183        ResourceConstraints, StandardScaler, ValidationStrategy, WeightedVotingEnsemble,
184    };
185    pub use crate::classification::{ClassificationMetrics, Classifier};
186    pub use crate::clustering::{
187        ClusteringAlgorithm, CoreClusteringMetrics as QuantumClusteringMetrics, QuantumClusterer,
188        QuantumClusteringConfig,
189    };
190    pub use crate::computer_vision::{
191        AugmentationConfig, ColorSpace, ComputationalMetrics, ConvolutionalConfig,
192        ImageEncodingMethod, ImagePreprocessor, PreprocessingConfig, QuantumConvolutionalNN,
193        QuantumEnhancement, QuantumFeatureExtractor, QuantumImageEncoder, QuantumMetrics,
194        QuantumSpatialAttention, QuantumVisionConfig, QuantumVisionPipeline, ResidualBlock,
195        TaskOutput, TaskTarget, TrainingHistory, VisionBackbone, VisionMetrics, VisionTaskConfig,
196    };
197    pub use crate::continual_learning::{
198        create_continual_task, generate_task_sequence, ContinualLearningStrategy, ContinualTask,
199        Experience, ForgettingMetrics, MemoryBuffer, MemorySelectionStrategy,
200        ParameterAllocationStrategy, QuantumContinualLearner, TaskMetrics, TaskType,
201    };
202    pub use crate::continuous_rl::{
203        ContinuousEnvironment, Experience as RLExperience, PendulumEnvironment, QuantumActor,
204        QuantumCritic, QuantumDDPG, QuantumSAC, ReplayBuffer,
205    };
206    pub use crate::crypto::{
207        ProtocolType, QuantumAuthentication, QuantumKeyDistribution, QuantumSignature,
208    };
209    pub use crate::device_compilation::{
210        CompilationMetrics, CompilationOptions, CompiledModel, DeviceCharacterization,
211        DeviceCompiler, QuantumMLModel, QubitMapping, RoutingAlgorithm, SynthesisMethod,
212    };
213    pub use crate::diffusion::{
214        NoiseSchedule, QuantumDiffusionModel, QuantumScoreDiffusion, QuantumVariationalDiffusion,
215    };
216    pub use crate::dimensionality_reduction::{
217        AutoencoderArchitecture, DRTrainedState, DimensionalityReductionAlgorithm,
218        DimensionalityReductionMetrics, ManifoldMetrics, QAutoencoderConfig, QCCAConfig,
219        QFactorAnalysisConfig, QFeatureSelectionConfig, QICAConfig, QKernelPCAConfig, QLDAConfig,
220        QManifoldConfig, QNMFConfig, QPCAConfig, QSpecializedConfig, QUMAPConfig, QtSNEConfig,
221        QuantumDimensionalityReducer, QuantumDistanceMetric as DRQuantumDistanceMetric,
222        QuantumEigensolver, QuantumEnhancementLevel as DRQuantumEnhancementLevel,
223        QuantumFeatureMap, ReconstructionMetrics,
224    };
225    pub use crate::domain_templates::{
226        utils as domain_utils, CreditRiskModel, Domain, DomainModel, DomainTemplateManager,
227        DrugDiscoveryModel, FraudDetectionModel, MaterialPropertyModel, MedicalImageModel,
228        ModelComplexity, MolecularPropertyModel, PortfolioOptimizationModel, ProblemType,
229        SmartGridModel, TemplateConfig, TemplateMetadata, VehicleRoutingModel,
230    };
231    pub use crate::error::{MLError, Result};
232    pub use crate::error_mitigation::{
233        AdaptiveConfig, CalibrationData, CircuitFoldingMethod, CoherenceTimeModel, ErrorType,
234        ExtrapolationMethod, GateErrorModel, MeasurementErrorModel, MitigatedInferenceData,
235        MitigatedTrainingData, MitigationStrategy, NoiseModel, QuantumMLErrorMitigator,
236        ReadoutCorrectionMethod,
237    };
238    pub use crate::explainable_ai::{
239        create_default_xai_config, AggregationMethod, AttributionMethod, CircuitExplanation,
240        ExplanationMethod, ExplanationResult, LRPRule, LocalModelType, PerturbationMethod,
241        QuantumExplainableAI, QuantumStateProperties,
242    };
243    pub use crate::few_shot::{
244        DistanceMetric, Episode, FewShotLearner, FewShotMethod, QuantumMAML,
245        QuantumPrototypicalNetwork,
246    };
247    pub use crate::gan::{Discriminator, GANEvaluationMetrics, Generator, QuantumGAN};
248    pub use crate::hep::{
249        AnomalyDetector, EventReconstructor, HEPQuantumClassifier, ParticleCollisionClassifier,
250    };
251    pub use crate::industry_examples::{
252        utils as industry_utils, BenchmarkResult, BusinessImpact, DataRequirements, ExampleResult,
253        ImplementationComplexity, Industry, IndustryExampleManager, PerformanceMetrics,
254        QuantumAdvantageMetrics, ROIEstimate, ROISummary, ResourceRequirements, UseCase,
255    };
256    pub use crate::keras_api::{
257        utils as keras_utils, Activation, ActivationFunction, Callback, DataType, Dense,
258        EarlyStopping, InitializerType, Input, KerasLayer, LayerInfo, LossFunction, MetricType,
259        ModelSummary, OptimizerType, QuantumAnsatzType, QuantumDense, Sequential,
260        TrainingHistory as KerasTrainingHistory,
261    };
262    pub use crate::kernels::{KernelMethod, QuantumKernel};
263    pub use crate::meta_learning::{
264        ContinualMetaLearner, MetaLearningAlgorithm, MetaLearningHistory, MetaTask,
265        QuantumMetaLearner, TaskGenerator,
266    };
267    pub use crate::model_zoo::{
268        utils as model_zoo_utils, IrisQuantumSVM, MNISTQuantumNN, ModelCategory, ModelMetadata,
269        ModelRequirements, ModelZoo, PortfolioQAOA, QuantumModel, TrainingConfig, H2VQE,
270    };
271    pub use crate::nlp::{NLPTaskType, QuantumLanguageModel, SentimentAnalyzer, TextSummarizer};
272    pub use crate::onnx_export::{
273        utils as onnx_utils, ExportOptions, ImportOptions, ModelInfo, ONNXAttribute, ONNXDataType,
274        ONNXExporter, ONNXGraph, ONNXImporter, ONNXNode, ONNXTensor, ONNXValueInfo,
275        QuantumBackendTarget, TargetFramework, UnsupportedOpHandling, ValidationReport,
276    };
277    pub use crate::optimization::{ObjectiveFunction, OptimizationMethod, Optimizer};
278    pub use crate::performance_profiler::{
279        Bottleneck, BottleneckSeverity, CircuitMetrics, MemorySnapshot, MemoryStats,
280        OperationStats, ProfilerConfig, ProfilingReport, QuantumMLProfiler,
281    };
282    pub use crate::pytorch_api::{
283        ActivationType as PyTorchActivationType, DataLoader, InitType, MemoryDataLoader, Parameter,
284        QuantumActivation, QuantumConv2d, QuantumCrossEntropyLoss, QuantumLinear, QuantumLoss,
285        QuantumMSELoss, QuantumModule, QuantumSequential, QuantumTrainer,
286        TrainingHistory as PyTorchTrainingHistory,
287    };
288    pub use crate::qnn::{QNNBuilder, QNNLayer, QuantumNeuralNetwork};
289    pub use crate::qsvm::{
290        FeatureMapType, QSVMParams, QuantumKernel as QSVMKernel, QuantumKernelRidge, QSVM,
291    };
292    pub use crate::quantum_llm::{
293        GenerationConfig, GenerationStatistics, MemoryRetrievalType, ModelScale,
294        QLLMTrainingConfig, QualityMetrics, QuantumAnalogyEngine, QuantumAssociativeMemory,
295        QuantumLLM, QuantumLLMConfig, QuantumMemoryConfig, QuantumMemorySystem,
296        QuantumParameterUpdate, QuantumReasoningConfig, QuantumReasoningModule, Vocabulary,
297    };
298    pub use crate::quantum_nas::{
299        create_default_search_space, AcquisitionFunction, ArchitectureCandidate,
300        ArchitectureMetrics, ArchitectureProperties, QuantumNAS, QuantumTopology, QubitConstraints,
301        RLAgentType, SearchSpace, SearchStrategy,
302    };
303    pub use crate::quantum_transformer::{
304        create_causal_mask, create_padding_mask, ActivationType, AttentionOutput,
305        PositionEncodingType, QuantumAttentionInfo, QuantumAttentionType, QuantumFeedForward,
306        QuantumMultiHeadAttention, QuantumPositionEncoding, QuantumTransformer,
307        QuantumTransformerConfig, QuantumTransformerLayer,
308    };
309    pub use crate::recommender::{
310        BusinessRules, FeatureExtractionMethod, ItemFeatures, ProfileLearningMethod,
311        QuantumEnhancementLevel, QuantumRecommender, QuantumRecommenderConfig, Recommendation,
312        RecommendationAlgorithm, RecommendationExplanation, RecommendationOptions,
313        SimilarityMeasure, UserProfile,
314    };
315    pub use crate::reinforcement::{Environment, QuantumAgent, ReinforcementLearning};
316    pub use crate::scirs2_integration::{
317        SciRS2Array, SciRS2DistributedTrainer, SciRS2Optimizer, SciRS2Serializer, SciRS2Tensor,
318    };
319    pub use crate::simulator_backends::{
320        BackendCapabilities, BackendSelectionStrategy, GradientMethod, MPSBackend, Observable,
321        SimulationResult, SimulatorBackend, StatevectorBackend,
322    };
323    pub use crate::sklearn_compatibility::{
324        model_selection, pipeline, QuantumKMeans, QuantumMLPClassifier, QuantumMLPRegressor,
325        QuantumSVC, SklearnClassifier, SklearnClusterer, SklearnEstimator, SklearnRegressor,
326    };
327    pub use crate::tensorflow_compatibility::{
328        tfq_utils, DataEncodingType, PQCLayer, PaddingType, ParameterInitStrategy,
329        QuantumCircuitLayer, QuantumConvolutionalLayer, QuantumDataset, QuantumDatasetIterator,
330        RegularizationType, TFQCircuitFormat, TFQGate, TFQLayer, TFQLossFunction, TFQModel,
331        TFQOptimizer,
332    };
333    pub use crate::time_series::{
334        generate_synthetic_time_series, AnomalyPoint, AnomalyType, DiversityStrategy,
335        EnsembleConfig, EnsembleMethod, FeatureEngineeringConfig, ForecastMetrics, ForecastResult,
336        QuantumEnhancementLevel as TSQuantumEnhancementLevel, QuantumTimeSeriesConfig,
337        QuantumTimeSeriesForecaster, SeasonalityConfig, TimeSeriesModel,
338    };
339    pub use crate::transfer::{
340        LayerConfig, PretrainedModel, QuantumModelZoo, QuantumTransferLearning, TransferStrategy,
341    };
342    pub use crate::tutorials::{
343        utils as tutorial_utils, CodeExample, DifficultyLevel, Exercise, ExerciseResult,
344        ExerciseType, ExperienceLevel, InteractiveElement, InteractiveType, TestCase, Tutorial,
345        TutorialCategory, TutorialManager, TutorialProgress, TutorialSection, TutorialSession,
346        UserBackground,
347    };
348    pub use crate::variational::{VariationalAlgorithm, VariationalCircuit};
349
350    // TorchQuantum compatibility
351    pub use crate::torchquantum::prelude::{
352        expval_joint_analytical, expval_joint_sampling, gen_bitstrings, measure as tq_measure,
353        CType as TQCType, FType as TQFType, NParamsEnum, TQAmplitudeEncoder, TQBarrenLayer,
354        TQDevice, TQEncoder, TQFarhiLayer, TQGeneralEncoder, TQHadamard, TQLayerConfig,
355        TQMaxwellLayer, TQMeasureAll, TQModule, TQModuleList, TQOp1QAllLayer, TQOp2QAllLayer,
356        TQOperator, TQParameter, TQPauliX, TQPauliY, TQPauliZ, TQPhaseEncoder, TQRXYZCXLayer, TQRx,
357        TQRy, TQRz, TQSethLayer, TQStateEncoder, TQStrongEntanglingLayer, WiresEnum, TQCNOT, TQCRX,
358        TQCRY, TQCRZ, TQCZ, TQRXX, TQRYY, TQRZX, TQRZZ, TQS, TQSWAP, TQSX, TQT,
359    };
360
361    // New cutting-edge quantum ML algorithms
362    pub use crate::quantum_graph_attention::{
363        AttentionAnalysis, AttentionConfig as QGATAttentionConfig,
364        BenchmarkResults as QGATBenchmarkResults, Graph, PoolingConfig, QGATConfig,
365        QuantumAttentionType as QGATQuantumAttentionType, QuantumGraphAttentionNetwork,
366        TrainingMetrics as QGATTrainingMetrics,
367    };
368    pub use crate::quantum_in_context_learning::{
369        AdaptationResult, AdaptationStrategy, AdaptationTarget, ContextExample, ContextMetadata,
370        ContextModality, ContextRetrievalMethod, EntanglementPattern, InContextLearningMetrics,
371        InContextLearningOutput, InContextLearningStatistics, InterpolationMethod,
372        MetaUpdateStrategy, QuantumAttentionMechanism, QuantumContextAttention,
373        QuantumContextEncoder, QuantumContextEncoding, QuantumContextState, QuantumDistanceMetric,
374        QuantumEpisodicMemory, QuantumInContextLearner, QuantumInContextLearningConfig,
375        QuantumTaskAdapter, TransferLearningResults,
376    };
377    pub use crate::quantum_memory_networks::{
378        AddressingConfig, AddressingType, BenchmarkResults as QMANBenchmarkResults,
379        ControllerArchitecture, ControllerConfig as QMANControllerConfig, EpisodicMemory,
380        HeadConfig, HeadType as QMANHeadType, MemoryInitialization, QMANConfig, QMANTrainingConfig,
381        QuantumMemoryAugmentedNetwork, ReadParams, TrainingMetrics as QMANTrainingMetrics,
382        WriteParams,
383    };
384    pub use crate::quantum_neural_odes::{
385        AnsatzType as QNODEAnsatzType, BenchmarkResults as QNODEBenchmarkResults,
386        IntegrationMethod, OptimizationStrategy as QNODEOptimizationStrategy, QNODEConfig,
387        QuantumNeuralODE, TrainingMetrics as QNODETrainingMetrics,
388    };
389    pub use crate::quantum_pinns::{
390        BoundaryCondition, DerivativeResults, InitialCondition, LossWeights, PhysicsEquationType,
391        QPINNConfig, QuantumPINN, TrainingMetrics as QPINNTrainingMetrics,
392    };
393    pub use crate::quantum_reservoir_computing::{
394        BenchmarkResults as QRCBenchmarkResults, DynamicsAnalysis, InputEncoding, QRCConfig,
395        QuantumReservoirComputer, ReadoutConfig, ReservoirDynamics,
396        TrainingMetrics as QRCTrainingMetrics,
397    };
398
399    pub use crate::quantum_advanced_diffusion::{
400        DenoisingArchitecture, ErrorMitigationStrategy, GenerationMetrics,
401        QuantumAdvancedDiffusionConfig, QuantumAdvancedDiffusionModel, QuantumGenerationOutput,
402        QuantumNoiseSchedule, QuantumTrainingConfig,
403    };
404
405    pub use crate::quantum_advantage_validator::{
406        ClassicalBaseline, ComparisonMetric, QuantumAdvantage, QuantumAdvantageValidator,
407        QuantumResourceUsage, QuantumResult, ResourceUsage, StatisticalSignificance,
408        ValidationConfig, ValidationReport as AdvantageValidationReport,
409    };
410
411    pub use crate::quantum_continuous_flows::{
412        FlowArchitecture, FlowSamplingOutput, FlowTrainingConfig, QuantumContinuousFlow,
413        QuantumContinuousFlowConfig, QuantumODEFunction,
414    };
415
416    pub use crate::quantum_neural_radiance_fields::{
417        QuantumNeRF, QuantumNeRFConfig, QuantumRenderOutput, QuantumRenderingMetrics,
418    };
419
420    pub use crate::quantum_mixture_of_experts::{
421        InterferencePattern, MoEOutput, MoEStatistics, MoETrainingConfig,
422        QuantumCombinationMetrics, QuantumGatingMechanism, QuantumMixtureOfExperts,
423        QuantumMixtureOfExpertsConfig, QuantumRoutingStrategy,
424    };
425
426    pub use crate::quantum_self_supervised_learning::{
427        ContrastiveLossFunction, QuantumAugmentationStrategy, QuantumAugmenter, QuantumDecoder,
428        QuantumEncoder, QuantumMaskingStrategy, QuantumProjector, QuantumSSLMethod,
429        QuantumSSLMetrics, QuantumSelfSupervisedConfig, QuantumSelfSupervisedLearner,
430        QuantumSimilarityMetric, RepresentationEvaluationResults, SSLLearningOutput,
431        SSLTrainingConfig,
432    };
433
434    pub use crate::quantum_implicit_neural_representations::{
435        AdaptationOutput, CompressedRepresentation, CompressionConfig, CompressionManager,
436        EntanglementManager, INRQueryOutput, INRTrainingConfig, INRTrainingOutput,
437        MetaLearningConfig, OptimizationConfig, QuantumActivationConfig, QuantumGradientEstimator,
438        QuantumINRConfig, QuantumINRMetrics, QuantumImplicitNeuralRepresentation,
439        QuantumLayerConfig, QuantumOptimizer, QuantumPositionalEncoding, QuantumStateManager,
440        RepresentationMethod, SignalType,
441    };
442}
quantrs2_ml/lib.rs

quantrs2_ml/
lib.rs
