Popular GitHub Repositories for Quantum Classical

A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.

Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning

SchNetPack - Deep Neural Networks for Atomistic Systems

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

Machine learning algorithms for many-body quantum systems

甲言，专注于古代汉语(古汉语/古文/文言文/文言)处理的NLP工具包，支持文言词库构建、分词、词性标注、断句和标点。Jiayan, the 1st NLP toolkit designed for Classical Chinese, supports lexicon construction, tokenizing, POS tagging, sentence segmentation and punctuation.

A Benchmark of Text Classification in PyTorch

GuwenBERT: 古文预训练语言模型（古文BERT） A Pre-trained Language Model for Classical Chinese (Literary Chinese)

Accurate Neural Network Potential on PyTorch

EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.

A modern IDE for writing classical Chinese poetry 格律诗编辑程序

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

Tensor network based quantum software framework for the NISQ era

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019

An extensive library of AI resources including books, courses, papers, guides, articles, tutorials, notebooks, AI field advancements and more.