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 🙌
- Updated
Nov 2, 2025 - Jupyter Notebook
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 🙌
The Swiss Army Knife of Applied Quantum Technology (Experimental Tech)
🧬 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 Instances for benchmarking
🌊 D-Wave Neal Simulated Annealing Interface for JuMP
GCS-Q is a coalition structure generation algorithm for induced subgraph games
Optimal coalition formation among Low Earth Orbit (LEO) satellites via GCS-Q algorithm.
⚛️ 🚀 👽 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 project is inspired and to some extent based on the research paper titled "Quantum Permutation Synchronization" by Birdal et al from 2021
QVoice: Quantum Variational Optimization with In-Constraint Energy
This repository contains a data of the ongoing project work, which is a part my work as Junior researcher at Qkrishi, Gurgaon, India.
🚀 QuantumAI merges Quantum Computing with Artificial Intelligence to revolutionize machine learning, cryptography, and optimization. Leveraging quantum superposition, entanglement, and hybrid AI models, this project pushes the boundaries of computational intelligence. ⚡ Next-gen AI meets quantum power! 💡
An advanced exploration of Quantum Fourier Transform (QFT) using Quantum Machine Learning (QML). This project delves into the optimization of variational quantum circuits, leveraging machine learning techniques to evaluate and visualize the transformation capabilities of QFT in quantum computing.
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.
7 days journey of learning more about Qiskit Runtime, QML and Quantum Chemistry application of Quantum Computer through 4 exercises and a final challenge by IBM Quantum in Fall 2022.
Qiskit Hackathon Taiwan 2022 - Group 4 - Quantum Mapper
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