Expand description
§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 (v0.1.0-beta.2)
- Refined SciRS2 v0.1.0-beta.3 integration with unified patterns
- Automatic differentiation leveraging SciRS2’s linear algebra operations
- Parallel training with
scirs2_core::parallel_ops - SIMD-accelerated quantum kernel computations
Re-exports§
Modules§
- adversarial
- Quantum Adversarial Training
- anneal_
integration - Integration with annealing module for QUBO problems in quantum ML
- anomaly_
detection - Quantum Anomaly Detection Module
- attention
- Quantum attention mechanisms for transformer architectures.
- autodiff
- Automatic differentiation for quantum machine learning.
- automl
- Quantum Automated Machine Learning (AutoML) Framework
- barren_
plateau - Barren Plateau Detection for Quantum Machine Learning
- benchmarking
- Unified benchmarking framework for quantum machine learning
- blockchain
- boltzmann
- Quantum Boltzmann Machines
- circuit_
integration - Circuit integration for quantum machine learning
- classical_
ml_ integration - Classical ML pipeline integration for QuantRS2-ML
- classification
- clustering
- Quantum Clustering Module
- computer_
vision - Quantum Computer Vision Pipelines
- continual_
learning - Quantum Continual Learning
- continuous_
rl - Quantum Reinforcement Learning with Continuous Actions
- crypto
- device_
compilation - Device-specific model compilation for quantum machine learning
- diffusion
- Quantum Diffusion Models
- dimensionality_
reduction - Quantum Dimensionality Reduction Module
- domain_
templates - Domain-specific model templates for QuantRS2-ML
- enhanced_
gan - Enhanced Quantum Generative Adversarial Networks (QGAN)
- error
- error_
mitigation - Advanced Error Mitigation for Quantum Machine Learning
- explainable_
ai - Quantum Explainable AI (XAI)
- federated
- Quantum federated learning protocols for distributed quantum machine learning.
- few_
shot - Quantum Few-Shot Learning
- gan
- gnn
- Quantum Graph Neural Networks (GNNs) implementation.
- hep
- industry_
examples - Industry use case examples for QuantRS2-ML
- keras_
api - Keras-style model building API for QuantRS2-ML
- kernels
- lstm
- Quantum Long Short-Term Memory (QLSTM) and recurrent architectures.
- meta_
learning - Quantum Meta-Learning Algorithms
- model_
zoo - Pre-trained model zoo for QuantRS2-ML
- nlp
- onnx_
export - ONNX model export support for QuantRS2-ML
- optimization
- prelude
- Prelude module for convenient imports
- pytorch_
api - PyTorch-like API for quantum machine learning models
- qcnn
- Quantum Convolutional Neural Networks (QCNN)
- qnn
- qsvm
- Quantum Support Vector Machine (QSVM) implementation
- quantum_
advanced_ diffusion - Advanced Quantum Diffusion Models
- quantum_
continuous_ flows - Quantum Continuous Normalization Flows
- quantum_
graph_ attention - Quantum Graph Attention Networks (QGATs)
- quantum_
implicit_ neural_ representations - Quantum Implicit Neural Representations
- quantum_
in_ context_ learning - Quantum In-Context Learning
- quantum_
llm - Quantum Large Language Models (QLLMs)
- quantum_
memory_ networks - Quantum Memory Augmented Networks (QMANs)
- quantum_
mixture_ of_ experts - Quantum Mixture of Experts
- quantum_
nas - Quantum Neural Architecture Search (NAS)
- quantum_
neural_ odes - Quantum Neural Ordinary Differential Equations (QNODEs)
- quantum_
neural_ radiance_ fields - Quantum Neural Radiance Fields (QNeRF)
- quantum_
pinns - Quantum Physics-Informed Neural Networks (QPINNs)
- quantum_
reservoir_ computing - Quantum Reservoir Computing (QRC)
- quantum_
self_ supervised_ learning - Quantum Self-Supervised Learning Frameworks
- quantum_
transformer - Quantum Transformer Architectures
- recommender
- Quantum Recommender Systems
- reinforcement
- scirs2_
integration - SciRS2 integration layer for quantum machine learning
- simulator_
backends - Simulator backend integration for quantum machine learning
- sklearn_
compatibility - Scikit-learn compatibility layer for QuantRS2-ML
- tensorflow_
compatibility - TensorFlow Quantum compatibility layer for QuantRS2-ML
- time_
series - Quantum Time Series Forecasting - Modular Implementation
- transfer
- Quantum Transfer Learning
- tutorials
- Quantum Machine Learning Tutorials for QuantRS2-ML
- vae
- Quantum Variational Autoencoders (QVAE)
- variational