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 (v0.1.0-rc.2)
35//!
36//! - Refined SciRS2 v0.1.1 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// Advanced Quantum-Classical Hybrid AutoML Engine
126pub mod hybrid_automl_engine;
127
128/// Re-export error types for easier access
129pub use error::MLError;
130pub use error::Result;
131
132/// Prelude module for convenient imports
133pub mod prelude {
134    pub use crate::adversarial::{
135        create_comprehensive_defense, create_default_adversarial_config, AdversarialTrainingConfig,
136        QuantumAdversarialExample, QuantumAdversarialTrainer, QuantumAttackType,
137        QuantumDefenseStrategy, RobustnessMetrics,
138    };
139    pub use crate::anneal_integration::{
140        AnnealingClient, AnnealingParams, AnnealingResult,
141        AnnealingSchedule as MLAnnealingSchedule, CircuitOptimizationProblem,
142        FeatureSelectionProblem, HyperparameterProblem, IsingProblem, OptimizationResult,
143        PortfolioOptimizationProblem, QuantumMLAnnealer, QuantumMLOptimizationProblem,
144        QuantumMLQUBO,
145    };
146    pub use crate::anomaly_detection::{
147        AnomalyDetectionMethod, AnomalyMetrics, AnomalyResult, PerformanceConfig,
148        PreprocessingConfig as AnomalyPreprocessingConfig, QuantumAnomalyConfig,
149        QuantumAnomalyDetector, QuantumAnomalyMetrics, QuantumAutoencoder,
150        QuantumEnhancementConfig, QuantumIsolationForest, QuantumLOF, QuantumOneClassSVM,
151        RealtimeConfig, SpecializedDetectorConfig,
152    };
153    pub use crate::automl::{
154        create_comprehensive_automl_config, create_default_automl_config, AdvancedAutoMLFeatures,
155        AlgorithmSearchSpace, EnsembleSearchSpace, EvaluationConfig, HyperparameterSearchSpace,
156        MLTaskType, OptimizationObjective, QuantumAutoML, QuantumAutoMLConfig, QuantumConstraints,
157        QuantumEncodingMethod, SearchBudgetConfig, SearchSpaceConfig,
158    };
159    pub use crate::benchmarking::{
160        Benchmark, BenchmarkCategory, BenchmarkConfig, BenchmarkFramework, BenchmarkReport,
161        BenchmarkResults, BenchmarkRunResult, BenchmarkSummary, ScalingType,
162    };
163    pub use crate::blockchain::{ConsensusType, QuantumBlockchain, QuantumToken, SmartContract};
164    pub use crate::boltzmann::{
165        AnnealingSchedule, DeepBoltzmannMachine, QuantumBoltzmannMachine, QuantumRBM,
166    };
167    pub use crate::circuit_integration::{
168        BackendManager, DeviceTopology, ExpressionvityMetrics, HardwareAwareCompiler,
169        MLCircuitAnalyzer, MLCircuitOptimizer, OptimizationPass, ParameterizedLayer, QuantumLayer,
170        QuantumMLExecutor, QubitProperties, RotationAxis, TrainabilityMetrics,
171    };
172    pub use crate::classical_ml_integration::{
173        utils as pipeline_utils, AutoOptimizationConfig, ClassicalModel, DataPreprocessor,
174        DatasetInfo, EnsembleStrategy, HybridModel, HybridPipeline, HybridPipelineManager,
175        MinMaxScaler, ModelRegistry, ModelType, OptimizedPipeline, PerformanceProfile,
176        PipelineConfig, PipelineRecommendation, PipelineStage, PipelineTemplate,
177        ResourceConstraints, StandardScaler, ValidationStrategy, WeightedVotingEnsemble,
178    };
179    pub use crate::classification::{ClassificationMetrics, Classifier};
180    pub use crate::clustering::{
181        ClusteringAlgorithm, CoreClusteringMetrics as QuantumClusteringMetrics, QuantumClusterer,
182        QuantumClusteringConfig,
183    };
184    pub use crate::computer_vision::{
185        AugmentationConfig, ColorSpace, ComputationalMetrics, ConvolutionalConfig,
186        ImageEncodingMethod, ImagePreprocessor, PreprocessingConfig, QuantumConvolutionalNN,
187        QuantumEnhancement, QuantumFeatureExtractor, QuantumImageEncoder, QuantumMetrics,
188        QuantumSpatialAttention, QuantumVisionConfig, QuantumVisionPipeline, ResidualBlock,
189        TaskOutput, TaskTarget, TrainingHistory, VisionBackbone, VisionMetrics, VisionTaskConfig,
190    };
191    pub use crate::continual_learning::{
192        create_continual_task, generate_task_sequence, ContinualLearningStrategy, ContinualTask,
193        Experience, ForgettingMetrics, MemoryBuffer, MemorySelectionStrategy,
194        ParameterAllocationStrategy, QuantumContinualLearner, TaskMetrics, TaskType,
195    };
196    pub use crate::continuous_rl::{
197        ContinuousEnvironment, Experience as RLExperience, PendulumEnvironment, QuantumActor,
198        QuantumCritic, QuantumDDPG, QuantumSAC, ReplayBuffer,
199    };
200    pub use crate::crypto::{
201        ProtocolType, QuantumAuthentication, QuantumKeyDistribution, QuantumSignature,
202    };
203    pub use crate::device_compilation::{
204        CompilationMetrics, CompilationOptions, CompiledModel, DeviceCharacterization,
205        DeviceCompiler, QuantumMLModel, QubitMapping, RoutingAlgorithm, SynthesisMethod,
206    };
207    pub use crate::diffusion::{
208        NoiseSchedule, QuantumDiffusionModel, QuantumScoreDiffusion, QuantumVariationalDiffusion,
209    };
210    pub use crate::dimensionality_reduction::{
211        AutoencoderArchitecture, DRTrainedState, DimensionalityReductionAlgorithm,
212        DimensionalityReductionMetrics, ManifoldMetrics, QAutoencoderConfig, QCCAConfig,
213        QFactorAnalysisConfig, QFeatureSelectionConfig, QICAConfig, QKernelPCAConfig, QLDAConfig,
214        QManifoldConfig, QNMFConfig, QPCAConfig, QSpecializedConfig, QUMAPConfig, QtSNEConfig,
215        QuantumDimensionalityReducer, QuantumDistanceMetric as DRQuantumDistanceMetric,
216        QuantumEigensolver, QuantumEnhancementLevel as DRQuantumEnhancementLevel,
217        QuantumFeatureMap, ReconstructionMetrics,
218    };
219    pub use crate::domain_templates::{
220        utils as domain_utils, CreditRiskModel, Domain, DomainModel, DomainTemplateManager,
221        DrugDiscoveryModel, FraudDetectionModel, MaterialPropertyModel, MedicalImageModel,
222        ModelComplexity, MolecularPropertyModel, PortfolioOptimizationModel, ProblemType,
223        SmartGridModel, TemplateConfig, TemplateMetadata, VehicleRoutingModel,
224    };
225    pub use crate::error::{MLError, Result};
226    pub use crate::error_mitigation::{
227        AdaptiveConfig, CalibrationData, CircuitFoldingMethod, CoherenceTimeModel, ErrorType,
228        ExtrapolationMethod, GateErrorModel, MeasurementErrorModel, MitigatedInferenceData,
229        MitigatedTrainingData, MitigationStrategy, NoiseModel, QuantumMLErrorMitigator,
230        ReadoutCorrectionMethod,
231    };
232    pub use crate::explainable_ai::{
233        create_default_xai_config, AggregationMethod, AttributionMethod, CircuitExplanation,
234        ExplanationMethod, ExplanationResult, LRPRule, LocalModelType, PerturbationMethod,
235        QuantumExplainableAI, QuantumStateProperties,
236    };
237    pub use crate::few_shot::{
238        DistanceMetric, Episode, FewShotLearner, FewShotMethod, QuantumMAML,
239        QuantumPrototypicalNetwork,
240    };
241    pub use crate::gan::{Discriminator, GANEvaluationMetrics, Generator, QuantumGAN};
242    pub use crate::hep::{
243        AnomalyDetector, EventReconstructor, HEPQuantumClassifier, ParticleCollisionClassifier,
244    };
245    pub use crate::industry_examples::{
246        utils as industry_utils, BenchmarkResult, BusinessImpact, DataRequirements, ExampleResult,
247        ImplementationComplexity, Industry, IndustryExampleManager, PerformanceMetrics,
248        QuantumAdvantageMetrics, ROIEstimate, ROISummary, ResourceRequirements, UseCase,
249    };
250    pub use crate::keras_api::{
251        utils as keras_utils, Activation, ActivationFunction, Callback, DataType, Dense,
252        EarlyStopping, InitializerType, Input, KerasLayer, LayerInfo, LossFunction, MetricType,
253        ModelSummary, OptimizerType, QuantumAnsatzType, QuantumDense, Sequential,
254        TrainingHistory as KerasTrainingHistory,
255    };
256    pub use crate::kernels::{KernelMethod, QuantumKernel};
257    pub use crate::meta_learning::{
258        ContinualMetaLearner, MetaLearningAlgorithm, MetaLearningHistory, MetaTask,
259        QuantumMetaLearner, TaskGenerator,
260    };
261    pub use crate::model_zoo::{
262        utils as model_zoo_utils, IrisQuantumSVM, MNISTQuantumNN, ModelCategory, ModelMetadata,
263        ModelRequirements, ModelZoo, PortfolioQAOA, QuantumModel, TrainingConfig, H2VQE,
264    };
265    pub use crate::nlp::{NLPTaskType, QuantumLanguageModel, SentimentAnalyzer, TextSummarizer};
266    pub use crate::onnx_export::{
267        utils as onnx_utils, ExportOptions, ImportOptions, ModelInfo, ONNXAttribute, ONNXDataType,
268        ONNXExporter, ONNXGraph, ONNXImporter, ONNXNode, ONNXTensor, ONNXValueInfo,
269        QuantumBackendTarget, TargetFramework, UnsupportedOpHandling, ValidationReport,
270    };
271    pub use crate::optimization::{ObjectiveFunction, OptimizationMethod, Optimizer};
272    pub use crate::performance_profiler::{
273        Bottleneck, BottleneckSeverity, CircuitMetrics, MemorySnapshot, MemoryStats,
274        OperationStats, ProfilerConfig, ProfilingReport, QuantumMLProfiler,
275    };
276    pub use crate::pytorch_api::{
277        ActivationType as PyTorchActivationType, DataLoader, InitType, MemoryDataLoader, Parameter,
278        QuantumActivation, QuantumConv2d, QuantumCrossEntropyLoss, QuantumLinear, QuantumLoss,
279        QuantumMSELoss, QuantumModule, QuantumSequential, QuantumTrainer,
280        TrainingHistory as PyTorchTrainingHistory,
281    };
282    pub use crate::qnn::{QNNBuilder, QNNLayer, QuantumNeuralNetwork};
283    pub use crate::qsvm::{
284        FeatureMapType, QSVMParams, QuantumKernel as QSVMKernel, QuantumKernelRidge, QSVM,
285    };
286    pub use crate::quantum_llm::{
287        GenerationConfig, GenerationStatistics, MemoryRetrievalType, ModelScale,
288        QLLMTrainingConfig, QualityMetrics, QuantumAnalogyEngine, QuantumAssociativeMemory,
289        QuantumLLM, QuantumLLMConfig, QuantumMemoryConfig, QuantumMemorySystem,
290        QuantumParameterUpdate, QuantumReasoningConfig, QuantumReasoningModule, Vocabulary,
291    };
292    pub use crate::quantum_nas::{
293        create_default_search_space, AcquisitionFunction, ArchitectureCandidate,
294        ArchitectureMetrics, ArchitectureProperties, QuantumNAS, QuantumTopology, QubitConstraints,
295        RLAgentType, SearchSpace, SearchStrategy,
296    };
297    pub use crate::quantum_transformer::{
298        create_causal_mask, create_padding_mask, ActivationType, AttentionOutput,
299        PositionEncodingType, QuantumAttentionInfo, QuantumAttentionType, QuantumFeedForward,
300        QuantumMultiHeadAttention, QuantumPositionEncoding, QuantumTransformer,
301        QuantumTransformerConfig, QuantumTransformerLayer,
302    };
303    pub use crate::recommender::{
304        BusinessRules, FeatureExtractionMethod, ItemFeatures, ProfileLearningMethod,
305        QuantumEnhancementLevel, QuantumRecommender, QuantumRecommenderConfig, Recommendation,
306        RecommendationAlgorithm, RecommendationExplanation, RecommendationOptions,
307        SimilarityMeasure, UserProfile,
308    };
309    pub use crate::reinforcement::{Environment, QuantumAgent, ReinforcementLearning};
310    pub use crate::scirs2_integration::{
311        SciRS2Array, SciRS2DistributedTrainer, SciRS2Optimizer, SciRS2Serializer, SciRS2Tensor,
312    };
313    pub use crate::simulator_backends::{
314        BackendCapabilities, BackendSelectionStrategy, GradientMethod, MPSBackend, Observable,
315        SimulationResult, SimulatorBackend, StatevectorBackend,
316    };
317    pub use crate::sklearn_compatibility::{
318        model_selection, pipeline, QuantumKMeans, QuantumMLPClassifier, QuantumMLPRegressor,
319        QuantumSVC, SklearnClassifier, SklearnClusterer, SklearnEstimator, SklearnRegressor,
320    };
321    pub use crate::tensorflow_compatibility::{
322        tfq_utils, DataEncodingType, PQCLayer, PaddingType, ParameterInitStrategy,
323        QuantumCircuitLayer, QuantumConvolutionalLayer, QuantumDataset, QuantumDatasetIterator,
324        RegularizationType, TFQCircuitFormat, TFQGate, TFQLayer, TFQLossFunction, TFQModel,
325        TFQOptimizer,
326    };
327    pub use crate::time_series::{
328        generate_synthetic_time_series, AnomalyPoint, AnomalyType, DiversityStrategy,
329        EnsembleConfig, EnsembleMethod, FeatureEngineeringConfig, ForecastMetrics, ForecastResult,
330        QuantumEnhancementLevel as TSQuantumEnhancementLevel, QuantumTimeSeriesConfig,
331        QuantumTimeSeriesForecaster, SeasonalityConfig, TimeSeriesModel,
332    };
333    pub use crate::transfer::{
334        LayerConfig, PretrainedModel, QuantumModelZoo, QuantumTransferLearning, TransferStrategy,
335    };
336    pub use crate::tutorials::{
337        utils as tutorial_utils, CodeExample, DifficultyLevel, Exercise, ExerciseResult,
338        ExerciseType, ExperienceLevel, InteractiveElement, InteractiveType, TestCase, Tutorial,
339        TutorialCategory, TutorialManager, TutorialProgress, TutorialSection, TutorialSession,
340        UserBackground,
341    };
342    pub use crate::variational::{VariationalAlgorithm, VariationalCircuit};
343
344    // TorchQuantum compatibility
345    pub use crate::torchquantum::prelude::{
346        expval_joint_analytical, expval_joint_sampling, gen_bitstrings, measure as tq_measure,
347        CType as TQCType, FType as TQFType, NParamsEnum, TQAmplitudeEncoder, TQBarrenLayer,
348        TQDevice, TQEncoder, TQFarhiLayer, TQGeneralEncoder, TQHadamard, TQLayerConfig,
349        TQMaxwellLayer, TQMeasureAll, TQModule, TQModuleList, TQOp1QAllLayer, TQOp2QAllLayer,
350        TQOperator, TQParameter, TQPauliX, TQPauliY, TQPauliZ, TQPhaseEncoder, TQRXYZCXLayer, TQRx,
351        TQRy, TQRz, TQSethLayer, TQStateEncoder, TQStrongEntanglingLayer, WiresEnum, TQCNOT, TQCRX,
352        TQCRY, TQCRZ, TQCZ, TQRXX, TQRYY, TQRZX, TQRZZ, TQS, TQSWAP, TQSX, TQT,
353    };
354
355    // New cutting-edge quantum ML algorithms
356    pub use crate::quantum_graph_attention::{
357        AttentionAnalysis, AttentionConfig as QGATAttentionConfig,
358        BenchmarkResults as QGATBenchmarkResults, Graph, PoolingConfig, QGATConfig,
359        QuantumAttentionType as QGATQuantumAttentionType, QuantumGraphAttentionNetwork,
360        TrainingMetrics as QGATTrainingMetrics,
361    };
362    pub use crate::quantum_in_context_learning::{
363        AdaptationResult, AdaptationStrategy, AdaptationTarget, ContextExample, ContextMetadata,
364        ContextModality, ContextRetrievalMethod, EntanglementPattern, InContextLearningMetrics,
365        InContextLearningOutput, InContextLearningStatistics, InterpolationMethod,
366        MetaUpdateStrategy, QuantumAttentionMechanism, QuantumContextAttention,
367        QuantumContextEncoder, QuantumContextEncoding, QuantumContextState, QuantumDistanceMetric,
368        QuantumEpisodicMemory, QuantumInContextLearner, QuantumInContextLearningConfig,
369        QuantumTaskAdapter, TransferLearningResults,
370    };
371    pub use crate::quantum_memory_networks::{
372        AddressingConfig, AddressingType, BenchmarkResults as QMANBenchmarkResults,
373        ControllerArchitecture, ControllerConfig as QMANControllerConfig, EpisodicMemory,
374        HeadConfig, HeadType as QMANHeadType, MemoryInitialization, QMANConfig, QMANTrainingConfig,
375        QuantumMemoryAugmentedNetwork, ReadParams, TrainingMetrics as QMANTrainingMetrics,
376        WriteParams,
377    };
378    pub use crate::quantum_neural_odes::{
379        AnsatzType as QNODEAnsatzType, BenchmarkResults as QNODEBenchmarkResults,
380        IntegrationMethod, OptimizationStrategy as QNODEOptimizationStrategy, QNODEConfig,
381        QuantumNeuralODE, TrainingMetrics as QNODETrainingMetrics,
382    };
383    pub use crate::quantum_pinns::{
384        BoundaryCondition, DerivativeResults, InitialCondition, LossWeights, PhysicsEquationType,
385        QPINNConfig, QuantumPINN, TrainingMetrics as QPINNTrainingMetrics,
386    };
387    pub use crate::quantum_reservoir_computing::{
388        BenchmarkResults as QRCBenchmarkResults, DynamicsAnalysis, InputEncoding, QRCConfig,
389        QuantumReservoirComputer, ReadoutConfig, ReservoirDynamics,
390        TrainingMetrics as QRCTrainingMetrics,
391    };
392
393    pub use crate::quantum_advanced_diffusion::{
394        DenoisingArchitecture, ErrorMitigationStrategy, GenerationMetrics,
395        QuantumAdvancedDiffusionConfig, QuantumAdvancedDiffusionModel, QuantumGenerationOutput,
396        QuantumNoiseSchedule, QuantumTrainingConfig,
397    };
398
399    pub use crate::quantum_advantage_validator::{
400        ClassicalBaseline, ComparisonMetric, QuantumAdvantage, QuantumAdvantageValidator,
401        QuantumResourceUsage, QuantumResult, ResourceUsage, StatisticalSignificance,
402        ValidationConfig, ValidationReport as AdvantageValidationReport,
403    };
404
405    pub use crate::quantum_continuous_flows::{
406        FlowArchitecture, FlowSamplingOutput, FlowTrainingConfig, QuantumContinuousFlow,
407        QuantumContinuousFlowConfig, QuantumODEFunction,
408    };
409
410    pub use crate::quantum_neural_radiance_fields::{
411        QuantumNeRF, QuantumNeRFConfig, QuantumRenderOutput, QuantumRenderingMetrics,
412    };
413
414    pub use crate::quantum_mixture_of_experts::{
415        InterferencePattern, MoEOutput, MoEStatistics, MoETrainingConfig,
416        QuantumCombinationMetrics, QuantumGatingMechanism, QuantumMixtureOfExperts,
417        QuantumMixtureOfExpertsConfig, QuantumRoutingStrategy,
418    };
419
420    pub use crate::quantum_self_supervised_learning::{
421        ContrastiveLossFunction, QuantumAugmentationStrategy, QuantumAugmenter, QuantumDecoder,
422        QuantumEncoder, QuantumMaskingStrategy, QuantumProjector, QuantumSSLMethod,
423        QuantumSSLMetrics, QuantumSelfSupervisedConfig, QuantumSelfSupervisedLearner,
424        QuantumSimilarityMetric, RepresentationEvaluationResults, SSLLearningOutput,
425        SSLTrainingConfig,
426    };
427
428    pub use crate::quantum_implicit_neural_representations::{
429        AdaptationOutput, CompressedRepresentation, CompressionConfig, CompressionManager,
430        EntanglementManager, INRQueryOutput, INRTrainingConfig, INRTrainingOutput,
431        MetaLearningConfig, OptimizationConfig, QuantumActivationConfig, QuantumGradientEstimator,
432        QuantumINRConfig, QuantumINRMetrics, QuantumImplicitNeuralRepresentation,
433        QuantumLayerConfig, QuantumOptimizer, QuantumPositionalEncoding, QuantumStateManager,
434        RepresentationMethod, SignalType,
435    };
436}
quantrs2_ml/lib.rs

quantrs2_ml/
lib.rs
