luoxx97's Stars
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
jackfrued/Python-100-Days
Python - 100天从新手到大师
binary-husky/gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。
google-research/google-research
Google Research
GopeedLab/gopeed
A modern download manager that supports all platforms. Built with Golang and Flutter.
Miraclelucy/dive_into_deep_learning
✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。
tensorflow/quantum
An open-source Python framework for hybrid quantum-classical machine learning.
QuantumBFS/Yao.jl
Extensible, Efficient Quantum Algorithm Design for Humans.
benedekrozemberczki/SimGNN
A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).
qiskit-community/qiskit-machine-learning
Quantum Machine Learning
amazon-science/co-with-gnns-example
Qiskit/platypus
Qiskit Textbook (beta)
huckiyang/QuantumSpeech-QCNN
IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition
jundeli/quantum-gan
PyTorch and PennyLane implementation of Quantum GAN with Hybrid Generator.
yh08037/quantum-neural-network
Qiskit Hackathon Korea 2021 Community Choice Award Winner : Exploring Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
SashwatAnagolum/DoNew
Repository for storing quantum computing algorithms.
piyush2896/PSO-for-Neural-Nets
Particle Swarm Optimizer For Neural Network Training
yiminghwang/qWGAN
The project codes associated with quantum Wasserstein GAN
Abexope/PySwarmOptimization
基于Python3语言开发的群体智能优化框架
erfanMhi/A-quantum-inspired-genetic-algorithm-for-k-means-clustering
Implementation of a Quantum inspired genetic algorithm proposed by A quantum-inspired genetic algorithm for k-means clustering paper.
qiyaoliang/Quantum-Deep-Learning
Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
n-gao/pesnet
Reference implementation of "Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions" (ICLR, 2022) and "Sampling-free Inference ob Ab-Initio Potential Energy Surface Networks" (ICLR 2023)
givgramacho/CERN-Quantum-Computing-Course
Quantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.
Sinestro38/qosf-qgan
Exploring learnability and optimal hyperparameters of various quantum generative adversarial networks and quantum neural networks using Pennylane.
mostafaaminnaji/ECNN
Ensemble of CNN for multi-focus image fusion
yingmao/Quantum-Generative-Adversarial-Network
A Quantum State Fidelity based Generative Adversarial Network
dreamiiia/QGA
量子遗传算法Python实现
QTI-TH/style-qgan
A style-based Quantum Generative Adversarial Network
NohaNekamiche/Quantum-GAN-PennyLane
Aymuo-s/Extreme_Learning_Machine
Handwritten Numeral Recognition using ELM and optimization using QPSO.