[ICLR 2023] "Equivariant Hypergraph Diffusion Neural Operators" by Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, Pan Li

[ICML'24] Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More

Official Repo for the NeurIPS2020 paper "Erdos Goes Neural: An Unsupervised Learning Framework for Combinatorial Optimization on Graphs"

A collection of quantum information science problems formulated as reinforcement learning environments

Official python implementation for ICML 2024: "Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem"

A Python toolkit for Machine Learning (ML) practices for Combinatorial Optimization (CO).

Official implementation of "COExpander: Adaptive Solution Expansion for Combinatorial Optimization".

Official Repo for the NeurIPS2024 spotlight paper "Are Graph Neural Networks Optimal Approximation Algorithms?"

🌺 Population-Based Reinforcement Learning for Combinatorial Optimization

[AAAI 2024] GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time

Source code for the paper UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks (IJCAI 2021).

Open-source implementation of AlphaEvolve

Learning-to-learn for QNNs: learning few-shot optimization of quantum neural networks with classical neural networks

This repository implements the architecture proposed by Verdon et al. in the paper Learning to learn with quantum neural networks via classical neural networks, using PennyLane and TensorFlow.

M.Sc. Dissertation – Noise-Adaptive Reinforcement Learning Strategies for Qubit Routing

A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)

Qubit Routing using Reinforcement Learning

Scalable toolkit for efficient model reinforcement

Awesome machine learning for combinatorial optimization papers.

the official repository of the paper unsupervised learning for combinatorial optimization needs meta learning

A Novel CUDA-Accelerated Simulation Framework for the QAOA

Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

Image retrieval model by leveraging image key points with Siamese Networks.

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

"Quantum attention functions are the keys to quantum machine learning." ― Amit Ray

A Toolkit for Reproducible Study, Application and Verification of QAOA

A collection of Jupyter notebooks showing how to use the Qiskit SDK