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

Machine-verified Lean 4 / Mathlib formalizations in quantum optimization

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

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

Deprecated compatibility shim for DWave.Neal in DWave.jl

⚛️ 🚀 👽 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.

QVoice: Quantum Variational Optimization with In-Constraint Energy

This project involves simulating a quantum classifier using a variational quantum circuit for binary classification problems. It is divided into three main parts, each contributing to the total project credits.