Popular GitHub Repositories for Qiskit Aqua

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Sample-based Quantum Diagonalization: Classically postprocess noisy quantum samples to yield more accurate eigenvalue estimations.

GitHub repo for Qiskit Hackathon "Quantum Reinforcement Learning" project

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

Slurm Spank plugins for Quantum resources and jobs support

Qelm - Quantum Enhanced Language Model (Qubit)

Deep Q-Learning with Hybrid Quantum Neural Network on Solving Maze Problems

A quantum computing alien invaders game, developed with ChatGPT4.

This simple neural network uses a quantum computer to generate truly-random weights.

QRLIT: Quantum Reinforcement Learning for Database Index Tuning

My coding portfolio showcases a diverse range of projects utilizing various programming languages and frameworks such as Python, Java, HTML, CSS, JavaScript, SQL, Qiskit, Flask and TensorFlow.

This repo is going to contain different resources that helped me learn quantum topics. I will also include my own personal way of explaining topics. Feel free to add or edit the content.

? QIP-X Framework – Quantum-Integrated AI & Cryptography A cutting-edge framework integrating quantum-ascendant cryptography, timeline optimization, and hyper-chaotic neural networks for secure, adaptive, and multiversal intelligence systems.

A testing ground for training AI models

Dask on Aqua using PBS

A quantum-classical (or hybrid) neural network and the use of a adversarial attack mechanism. The core libraries employed are Quantinuum pytket and pytket-qiskit. torchattacks is used for the white-box, targetted, compounded adversarial attacks.

Quantum-enhanced workflow scheduling optimizes multi-processor resource allocation using DAGs and a hybrid quantum-classical approach, visualized via advanced tools. QUBO problem formulation ensures efficiency.