quantrs2-ml 0.1.3

#![recursion_limit = "8192"]
#![allow(warnings)]
#![allow(clippy::all)]
#![allow(clippy::pedantic)]
#![allow(clippy::nursery)]
#![allow(clippy::restriction)]

//! # Quantum Machine Learning
//!
//! This crate provides quantum machine learning capabilities for the QuantRS2 framework.
//! It includes quantum neural networks, variational algorithms, and specialized tools for
//! high-energy physics data analysis, plus cutting-edge quantum ML algorithms.
//!
//! ## Core Features
//!
//! - Quantum Neural Networks
//! - Variational Quantum Algorithms
//! - High-Energy Physics Data Analysis
//! - Quantum Reinforcement Learning
//! - Quantum Generative Models
//! - Quantum Kernels for Classification
//! - Quantum-Enhanced Cryptographic Protocols
//! - Quantum Blockchain and Distributed Ledger Technology
//! - Quantum-Enhanced Natural Language Processing
//! - Quantum Anomaly Detection and Outlier Analysis
//!
//! ## Cutting-Edge Quantum ML Algorithms
//!
//! - **Quantum Neural ODEs**: Continuous-depth quantum neural networks using quantum circuits to parameterize derivative functions
//! - **Quantum Physics-Informed Neural Networks (QPINNs)**: Quantum neural networks that enforce physical laws and solve PDEs
//! - **Quantum Reservoir Computing**: Leverages quantum dynamics for temporal data processing with quantum advantages
//! - **Quantum Graph Attention Networks**: Combines graph neural networks with quantum attention mechanisms for complex graph analysis
//!
//! ## Recent Updates (v0.1.3)
//!
//! - Refined SciRS2 v0.1.3 Stable Release integration with unified patterns
//! - Automatic differentiation leveraging SciRS2's linear algebra operations
//! - Parallel training with `scirs2_core::parallel_ops`
//! - SIMD-accelerated quantum kernel computations

use fastrand;
use std::error::Error;
use thiserror::Error;

pub mod barren_plateau;
pub mod blockchain;
pub mod classification;
pub mod crypto;
pub mod enhanced_gan;
pub mod gan;
pub mod hep;
pub mod kernels;
pub mod nlp;
pub mod optimization;
pub mod qcnn;
pub mod qnn;
pub mod qsvm;
pub mod reinforcement;
pub mod vae;
pub mod variational;

pub mod adversarial;
pub mod anneal_integration;
pub mod anomaly_detection;
pub mod attention;
pub mod autodiff;
pub mod automl;
pub mod benchmarking;
pub mod boltzmann;
pub mod circuit_integration;
pub mod classical_ml_integration;
pub mod clustering;
pub mod computer_vision;
pub mod continual_learning;
pub mod continuous_rl;
pub mod device_compilation;
pub mod diffusion;
pub mod dimensionality_reduction;
pub mod domain_templates;
pub mod error;
pub mod error_mitigation;
pub mod explainable_ai;
pub mod federated;
pub mod few_shot;
pub mod gnn;
#[cfg(feature = "gpu")]
pub mod gpu_backend_impl;
pub mod industry_examples;
pub mod keras_api;
pub mod lstm;
pub mod meta_learning;
pub mod model_zoo;
pub mod onnx_export;
pub mod performance_profiler;
pub mod pytorch_api;
pub mod quantum_advanced_diffusion;
pub mod quantum_advantage_validator;
pub mod quantum_continuous_flows;
pub mod quantum_graph_attention;
pub mod quantum_implicit_neural_representations;
pub mod quantum_in_context_learning;
pub mod quantum_llm;
pub mod quantum_memory_networks;
pub mod quantum_mixture_of_experts;
pub mod quantum_nas;
pub mod quantum_neural_odes;
pub mod quantum_neural_radiance_fields;
pub mod quantum_pinns;
pub mod quantum_reservoir_computing;
pub mod quantum_self_supervised_learning;
pub mod quantum_transformer;
pub mod recommender;
pub mod scirs2_integration;
pub mod simulator_backends;
pub mod sklearn_compatibility;
pub mod tensorflow_compatibility;
pub mod time_series;
pub mod torchquantum;
pub mod transfer;
pub mod tutorials;

// Utilities module for calibration, metrics, preprocessing, etc.
pub mod utils;

// Advanced Quantum-Classical Hybrid AutoML Engine
pub mod hybrid_automl_engine;

/// Re-export error types for easier access
pub use error::MLError;
pub use error::Result;

/// Prelude module for convenient imports
pub mod prelude {
    pub use crate::adversarial::{
        create_comprehensive_defense, create_default_adversarial_config, AdversarialTrainingConfig,
        QuantumAdversarialExample, QuantumAdversarialTrainer, QuantumAttackType,
        QuantumDefenseStrategy, RobustnessMetrics,
    };
    pub use crate::anneal_integration::{
        AnnealingClient, AnnealingParams, AnnealingResult,
        AnnealingSchedule as MLAnnealingSchedule, CircuitOptimizationProblem,
        FeatureSelectionProblem, HyperparameterProblem, IsingProblem, OptimizationResult,
        PortfolioOptimizationProblem, QuantumMLAnnealer, QuantumMLOptimizationProblem,
        QuantumMLQUBO,
    };
    pub use crate::anomaly_detection::{
        AnomalyDetectionMethod, AnomalyMetrics, AnomalyResult, PerformanceConfig,
        PreprocessingConfig as AnomalyPreprocessingConfig, QuantumAnomalyConfig,
        QuantumAnomalyDetector, QuantumAnomalyMetrics, QuantumAutoencoder,
        QuantumEnhancementConfig, QuantumIsolationForest, QuantumLOF, QuantumOneClassSVM,
        RealtimeConfig, SpecializedDetectorConfig,
    };
    pub use crate::automl::{
        create_comprehensive_automl_config, create_default_automl_config, AdvancedAutoMLFeatures,
        AlgorithmSearchSpace, EnsembleSearchSpace, EvaluationConfig, HyperparameterSearchSpace,
        MLTaskType, OptimizationObjective, QuantumAutoML, QuantumAutoMLConfig, QuantumConstraints,
        QuantumEncodingMethod, SearchBudgetConfig, SearchSpaceConfig,
    };
    pub use crate::benchmarking::{
        Benchmark, BenchmarkCategory, BenchmarkConfig, BenchmarkFramework, BenchmarkReport,
        BenchmarkResults, BenchmarkRunResult, BenchmarkSummary, ScalingType,
    };
    pub use crate::blockchain::{ConsensusType, QuantumBlockchain, QuantumToken, SmartContract};
    pub use crate::boltzmann::{
        AnnealingSchedule, DeepBoltzmannMachine, QuantumBoltzmannMachine, QuantumRBM,
    };
    pub use crate::circuit_integration::{
        BackendManager, DeviceTopology, ExpressionvityMetrics, HardwareAwareCompiler,
        MLCircuitAnalyzer, MLCircuitOptimizer, OptimizationPass, ParameterizedLayer, QuantumLayer,
        QuantumMLExecutor, QubitProperties, RotationAxis, TrainabilityMetrics,
    };
    pub use crate::classical_ml_integration::{
        utils as pipeline_utils, AutoOptimizationConfig, ClassicalModel, DataPreprocessor,
        DatasetInfo, EnsembleStrategy, HybridModel, HybridPipeline, HybridPipelineManager,
        MinMaxScaler, ModelRegistry, ModelType, OptimizedPipeline, PerformanceProfile,
        PipelineConfig, PipelineRecommendation, PipelineStage, PipelineTemplate,
        ResourceConstraints, StandardScaler, ValidationStrategy, WeightedVotingEnsemble,
    };
    pub use crate::classification::{ClassificationMetrics, Classifier};
    pub use crate::clustering::{
        ClusteringAlgorithm, CoreClusteringMetrics as QuantumClusteringMetrics, QuantumClusterer,
        QuantumClusteringConfig,
    };
    pub use crate::computer_vision::{
        AugmentationConfig, ColorSpace, ComputationalMetrics, ConvolutionalConfig,
        ImageEncodingMethod, ImagePreprocessor, PreprocessingConfig, QuantumConvolutionalNN,
        QuantumEnhancement, QuantumFeatureExtractor, QuantumImageEncoder, QuantumMetrics,
        QuantumSpatialAttention, QuantumVisionConfig, QuantumVisionPipeline, ResidualBlock,
        TaskOutput, TaskTarget, TrainingHistory, VisionBackbone, VisionMetrics, VisionTaskConfig,
    };
    pub use crate::continual_learning::{
        create_continual_task, generate_task_sequence, ContinualLearningStrategy, ContinualTask,
        Experience, ForgettingMetrics, MemoryBuffer, MemorySelectionStrategy,
        ParameterAllocationStrategy, QuantumContinualLearner, TaskMetrics, TaskType,
    };
    pub use crate::continuous_rl::{
        ContinuousEnvironment, Experience as RLExperience, PendulumEnvironment, QuantumActor,
        QuantumCritic, QuantumDDPG, QuantumSAC, ReplayBuffer,
    };
    pub use crate::crypto::{
        ProtocolType, QuantumAuthentication, QuantumKeyDistribution, QuantumSignature,
    };
    pub use crate::device_compilation::{
        CompilationMetrics, CompilationOptions, CompiledModel, DeviceCharacterization,
        DeviceCompiler, QuantumMLModel, QubitMapping, RoutingAlgorithm, SynthesisMethod,
    };
    pub use crate::diffusion::{
        NoiseSchedule, QuantumDiffusionModel, QuantumScoreDiffusion, QuantumVariationalDiffusion,
    };
    pub use crate::dimensionality_reduction::{
        AutoencoderArchitecture, DRTrainedState, DimensionalityReductionAlgorithm,
        DimensionalityReductionMetrics, ManifoldMetrics, QAutoencoderConfig, QCCAConfig,
        QFactorAnalysisConfig, QFeatureSelectionConfig, QICAConfig, QKernelPCAConfig, QLDAConfig,
        QManifoldConfig, QNMFConfig, QPCAConfig, QSpecializedConfig, QUMAPConfig, QtSNEConfig,
        QuantumDimensionalityReducer, QuantumDistanceMetric as DRQuantumDistanceMetric,
        QuantumEigensolver, QuantumEnhancementLevel as DRQuantumEnhancementLevel,
        QuantumFeatureMap, ReconstructionMetrics,
    };
    pub use crate::domain_templates::{
        utils as domain_utils, CreditRiskModel, Domain, DomainModel, DomainTemplateManager,
        DrugDiscoveryModel, FraudDetectionModel, MaterialPropertyModel, MedicalImageModel,
        ModelComplexity, MolecularPropertyModel, PortfolioOptimizationModel, ProblemType,
        SmartGridModel, TemplateConfig, TemplateMetadata, VehicleRoutingModel,
    };
    pub use crate::error::{MLError, Result};
    pub use crate::error_mitigation::{
        AdaptiveConfig, CalibrationData, CircuitFoldingMethod, CoherenceTimeModel, ErrorType,
        ExtrapolationMethod, GateErrorModel, MeasurementErrorModel, MitigatedInferenceData,
        MitigatedTrainingData, MitigationStrategy, NoiseModel, QuantumMLErrorMitigator,
        ReadoutCorrectionMethod,
    };
    pub use crate::explainable_ai::{
        create_default_xai_config, AggregationMethod, AttributionMethod, CircuitExplanation,
        ExplanationMethod, ExplanationResult, LRPRule, LocalModelType, PerturbationMethod,
        QuantumExplainableAI, QuantumStateProperties,
    };
    pub use crate::few_shot::{
        DistanceMetric, Episode, FewShotLearner, FewShotMethod, QuantumMAML,
        QuantumPrototypicalNetwork,
    };
    pub use crate::gan::{Discriminator, GANEvaluationMetrics, Generator, QuantumGAN};
    pub use crate::hep::{
        AnomalyDetector, EventReconstructor, HEPQuantumClassifier, ParticleCollisionClassifier,
    };
    pub use crate::industry_examples::{
        utils as industry_utils, BenchmarkResult, BusinessImpact, DataRequirements, ExampleResult,
        ImplementationComplexity, Industry, IndustryExampleManager, PerformanceMetrics,
        QuantumAdvantageMetrics, ROIEstimate, ROISummary, ResourceRequirements, UseCase,
    };
    pub use crate::keras_api::{
        utils as keras_utils, Activation, ActivationFunction, Callback, DataType, Dense,
        EarlyStopping, InitializerType, Input, KerasLayer, LayerInfo, LossFunction, MetricType,
        ModelSummary, OptimizerType, QuantumAnsatzType, QuantumDense, Sequential,
        TrainingHistory as KerasTrainingHistory,
    };
    pub use crate::kernels::{KernelMethod, QuantumKernel};
    pub use crate::meta_learning::{
        ContinualMetaLearner, MetaLearningAlgorithm, MetaLearningHistory, MetaTask,
        QuantumMetaLearner, TaskGenerator,
    };
    pub use crate::model_zoo::{
        utils as model_zoo_utils, IrisQuantumSVM, MNISTQuantumNN, ModelCategory, ModelMetadata,
        ModelRequirements, ModelZoo, PortfolioQAOA, QuantumModel, TrainingConfig, H2VQE,
    };
    pub use crate::nlp::{NLPTaskType, QuantumLanguageModel, SentimentAnalyzer, TextSummarizer};
    pub use crate::onnx_export::{
        utils as onnx_utils, ExportOptions, ImportOptions, ModelInfo, ONNXAttribute, ONNXDataType,
        ONNXExporter, ONNXGraph, ONNXImporter, ONNXNode, ONNXTensor, ONNXValueInfo,
        QuantumBackendTarget, TargetFramework, UnsupportedOpHandling, ValidationReport,
    };
    pub use crate::optimization::{ObjectiveFunction, OptimizationMethod, Optimizer};
    pub use crate::performance_profiler::{
        Bottleneck, BottleneckSeverity, CircuitMetrics, MemorySnapshot, MemoryStats,
        OperationStats, ProfilerConfig, ProfilingReport, QuantumMLProfiler,
    };
    pub use crate::pytorch_api::{
        ActivationType as PyTorchActivationType, DataLoader, InitType, MemoryDataLoader, Parameter,
        QuantumActivation, QuantumConv2d, QuantumCrossEntropyLoss, QuantumLinear, QuantumLoss,
        QuantumMSELoss, QuantumModule, QuantumSequential, QuantumTrainer,
        TrainingHistory as PyTorchTrainingHistory,
    };
    pub use crate::qnn::{QNNBuilder, QNNLayer, QuantumNeuralNetwork};
    pub use crate::qsvm::{
        FeatureMapType, QSVMParams, QuantumKernel as QSVMKernel, QuantumKernelRidge, QSVM,
    };
    pub use crate::quantum_llm::{
        GenerationConfig, GenerationStatistics, MemoryRetrievalType, ModelScale,
        QLLMTrainingConfig, QualityMetrics, QuantumAnalogyEngine, QuantumAssociativeMemory,
        QuantumLLM, QuantumLLMConfig, QuantumMemoryConfig, QuantumMemorySystem,
        QuantumParameterUpdate, QuantumReasoningConfig, QuantumReasoningModule, Vocabulary,
    };
    pub use crate::quantum_nas::{
        create_default_search_space, AcquisitionFunction, ArchitectureCandidate,
        ArchitectureMetrics, ArchitectureProperties, QuantumNAS, QuantumTopology, QubitConstraints,
        RLAgentType, SearchSpace, SearchStrategy,
    };
    pub use crate::quantum_transformer::{
        create_causal_mask, create_padding_mask, ActivationType, AttentionOutput,
        PositionEncodingType, QuantumAttentionInfo, QuantumAttentionType, QuantumFeedForward,
        QuantumMultiHeadAttention, QuantumPositionEncoding, QuantumTransformer,
        QuantumTransformerConfig, QuantumTransformerLayer,
    };
    pub use crate::recommender::{
        BusinessRules, FeatureExtractionMethod, ItemFeatures, ProfileLearningMethod,
        QuantumEnhancementLevel, QuantumRecommender, QuantumRecommenderConfig, Recommendation,
        RecommendationAlgorithm, RecommendationExplanation, RecommendationOptions,
        SimilarityMeasure, UserProfile,
    };
    pub use crate::reinforcement::{Environment, QuantumAgent, ReinforcementLearning};
    pub use crate::scirs2_integration::{
        SciRS2Array, SciRS2DistributedTrainer, SciRS2Optimizer, SciRS2Serializer, SciRS2Tensor,
    };
    pub use crate::simulator_backends::{
        BackendCapabilities, BackendSelectionStrategy, GradientMethod, MPSBackend, Observable,
        SimulationResult, SimulatorBackend, StatevectorBackend,
    };
    pub use crate::sklearn_compatibility::{
        model_selection, pipeline, QuantumKMeans, QuantumMLPClassifier, QuantumMLPRegressor,
        QuantumSVC, SklearnClassifier, SklearnClusterer, SklearnEstimator, SklearnRegressor,
    };
    pub use crate::tensorflow_compatibility::{
        tfq_utils, DataEncodingType, PQCLayer, PaddingType, ParameterInitStrategy,
        QuantumCircuitLayer, QuantumConvolutionalLayer, QuantumDataset, QuantumDatasetIterator,
        RegularizationType, TFQCircuitFormat, TFQGate, TFQLayer, TFQLossFunction, TFQModel,
        TFQOptimizer,
    };
    pub use crate::time_series::{
        generate_synthetic_time_series, AnomalyPoint, AnomalyType, DiversityStrategy,
        EnsembleConfig, EnsembleMethod, FeatureEngineeringConfig, ForecastMetrics, ForecastResult,
        QuantumEnhancementLevel as TSQuantumEnhancementLevel, QuantumTimeSeriesConfig,
        QuantumTimeSeriesForecaster, SeasonalityConfig, TimeSeriesModel,
    };
    pub use crate::transfer::{
        LayerConfig, PretrainedModel, QuantumModelZoo, QuantumTransferLearning, TransferStrategy,
    };
    pub use crate::tutorials::{
        utils as tutorial_utils, CodeExample, DifficultyLevel, Exercise, ExerciseResult,
        ExerciseType, ExperienceLevel, InteractiveElement, InteractiveType, TestCase, Tutorial,
        TutorialCategory, TutorialManager, TutorialProgress, TutorialSection, TutorialSession,
        UserBackground,
    };
    pub use crate::variational::{VariationalAlgorithm, VariationalCircuit};

    // TorchQuantum compatibility
    pub use crate::torchquantum::prelude::{
        expval_joint_analytical, expval_joint_sampling, gen_bitstrings, measure as tq_measure,
        CType as TQCType, FType as TQFType, NParamsEnum, TQAmplitudeEncoder, TQBarrenLayer,
        TQDevice, TQEncoder, TQFarhiLayer, TQGeneralEncoder, TQHadamard, TQLayerConfig,
        TQMaxwellLayer, TQMeasureAll, TQModule, TQModuleList, TQOp1QAllLayer, TQOp2QAllLayer,
        TQOperator, TQParameter, TQPauliX, TQPauliY, TQPauliZ, TQPhaseEncoder, TQRXYZCXLayer, TQRx,
        TQRy, TQRz, TQSethLayer, TQStateEncoder, TQStrongEntanglingLayer, WiresEnum, TQCNOT, TQCRX,
        TQCRY, TQCRZ, TQCZ, TQRXX, TQRYY, TQRZX, TQRZZ, TQS, TQSWAP, TQSX, TQT,
    };

    // New cutting-edge quantum ML algorithms
    pub use crate::quantum_graph_attention::{
        AttentionAnalysis, AttentionConfig as QGATAttentionConfig,
        BenchmarkResults as QGATBenchmarkResults, Graph, PoolingConfig, QGATConfig,
        QuantumAttentionType as QGATQuantumAttentionType, QuantumGraphAttentionNetwork,
        TrainingMetrics as QGATTrainingMetrics,
    };
    pub use crate::quantum_in_context_learning::{
        AdaptationResult, AdaptationStrategy, AdaptationTarget, ContextExample, ContextMetadata,
        ContextModality, ContextRetrievalMethod, EntanglementPattern, InContextLearningMetrics,
        InContextLearningOutput, InContextLearningStatistics, InterpolationMethod,
        MetaUpdateStrategy, QuantumAttentionMechanism, QuantumContextAttention,
        QuantumContextEncoder, QuantumContextEncoding, QuantumContextState, QuantumDistanceMetric,
        QuantumEpisodicMemory, QuantumInContextLearner, QuantumInContextLearningConfig,
        QuantumTaskAdapter, TransferLearningResults,
    };
    pub use crate::quantum_memory_networks::{
        AddressingConfig, AddressingType, BenchmarkResults as QMANBenchmarkResults,
        ControllerArchitecture, ControllerConfig as QMANControllerConfig, EpisodicMemory,
        HeadConfig, HeadType as QMANHeadType, MemoryInitialization, QMANConfig, QMANTrainingConfig,
        QuantumMemoryAugmentedNetwork, ReadParams, TrainingMetrics as QMANTrainingMetrics,
        WriteParams,
    };
    pub use crate::quantum_neural_odes::{
        AnsatzType as QNODEAnsatzType, BenchmarkResults as QNODEBenchmarkResults,
        IntegrationMethod, OptimizationStrategy as QNODEOptimizationStrategy, QNODEConfig,
        QuantumNeuralODE, TrainingMetrics as QNODETrainingMetrics,
    };
    pub use crate::quantum_pinns::{
        BoundaryCondition, DerivativeResults, InitialCondition, LossWeights, PhysicsEquationType,
        QPINNConfig, QuantumPINN, TrainingMetrics as QPINNTrainingMetrics,
    };
    pub use crate::quantum_reservoir_computing::{
        BenchmarkResults as QRCBenchmarkResults, DynamicsAnalysis, InputEncoding, QRCConfig,
        QuantumReservoirComputer, ReadoutConfig, ReservoirDynamics,
        TrainingMetrics as QRCTrainingMetrics,
    };

    pub use crate::quantum_advanced_diffusion::{
        DenoisingArchitecture, ErrorMitigationStrategy, GenerationMetrics,
        QuantumAdvancedDiffusionConfig, QuantumAdvancedDiffusionModel, QuantumGenerationOutput,
        QuantumNoiseSchedule, QuantumTrainingConfig,
    };

    pub use crate::quantum_advantage_validator::{
        ClassicalBaseline, ComparisonMetric, QuantumAdvantage, QuantumAdvantageValidator,
        QuantumResourceUsage, QuantumResult, ResourceUsage, StatisticalSignificance,
        ValidationConfig, ValidationReport as AdvantageValidationReport,
    };

    pub use crate::quantum_continuous_flows::{
        FlowArchitecture, FlowSamplingOutput, FlowTrainingConfig, QuantumContinuousFlow,
        QuantumContinuousFlowConfig, QuantumODEFunction,
    };

    pub use crate::quantum_neural_radiance_fields::{
        QuantumNeRF, QuantumNeRFConfig, QuantumRenderOutput, QuantumRenderingMetrics,
    };

    pub use crate::quantum_mixture_of_experts::{
        InterferencePattern, MoEOutput, MoEStatistics, MoETrainingConfig,
        QuantumCombinationMetrics, QuantumGatingMechanism, QuantumMixtureOfExperts,
        QuantumMixtureOfExpertsConfig, QuantumRoutingStrategy,
    };

    pub use crate::quantum_self_supervised_learning::{
        ContrastiveLossFunction, QuantumAugmentationStrategy, QuantumAugmenter, QuantumDecoder,
        QuantumEncoder, QuantumMaskingStrategy, QuantumProjector, QuantumSSLMethod,
        QuantumSSLMetrics, QuantumSelfSupervisedConfig, QuantumSelfSupervisedLearner,
        QuantumSimilarityMetric, RepresentationEvaluationResults, SSLLearningOutput,
        SSLTrainingConfig,
    };

    pub use crate::quantum_implicit_neural_representations::{
        AdaptationOutput, CompressedRepresentation, CompressionConfig, CompressionManager,
        EntanglementManager, INRQueryOutput, INRTrainingConfig, INRTrainingOutput,
        MetaLearningConfig, OptimizationConfig, QuantumActivationConfig, QuantumGradientEstimator,
        QuantumINRConfig, QuantumINRMetrics, QuantumImplicitNeuralRepresentation,
        QuantumLayerConfig, QuantumOptimizer, QuantumPositionalEncoding, QuantumStateManager,
        RepresentationMethod, SignalType,
    };
}