quantum-optimization

The Classiq Library is the largest collection of quantum algorithms and applications. It is the best way to explore quantum computing software. We welcome community contributions to our Library 🙌

A comprehensive benchmark suite for quantum optimization methods, featuring ten challenging problem classes with practical applications.

The Swiss Army Knife of Applied Quantum Technology (Experimental Tech)

A Python library to automate generating, parallelizing, and executing quantum programs.

🧬 Tools for Quadratic Unconstrained Binary Optimization models in Julia

Quantum Annealing-based Unsupervised Segmentation Algorithm

JuMP wrapper for IBMQ Optimization Algorithms (ft QUBODrivers.jl)

OSCAR: configure and debug variational quantum algorithms efficiently

QUBO.jl Notebooks

QUBO Instances for benchmarking

Predator by Aether AI: AI security research, software repair, and hybrid quantum optimization. Public experiments and Hack The Box training demos.

Machine-verified Lean 4 / Mathlib formalizations in quantum optimization

LunaModel: Fast, symbolic modeling for optimization. Build, translate, and transform optimization models.

Deprecated compatibility shim for DWave.Neal in DWave.jl

Optimal coalition formation among Low Earth Orbit (LEO) satellites via GCS-Q algorithm.

GCS-Q is a coalition structure generation algorithm for induced subgraph games

⚛️ 🚀 👽 A self-paced, game-based Quantum Computing learning program for students, researchers and enthusiasts. This program offers a general understanding of Quantum Computing, as well as some of its applications, such as Quantum Machine Learning and Quantum Optimization, and how to program real quantum computers.

This repository contains a data of the ongoing project work, which is a part my work as Junior researcher at Qkrishi, Gurgaon, India.

This project is developed for the final project of my Quantum Computing course and is based on the research paper titled "Quantum Permutation Synchronization" by Birdal et al from 2021.

QAOA Portfolio Optimization Problem as part of the YQuantum Hackathon