PennyLane/PyTorch implementation of Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (Skolik et al., 2021)

A quantum reinforcement learning framework based on PyTorch and PennyLane.

Study of entanglement and Shannon entropies in Quantum Reinforcement Learning (and its classical counterpart) in a discrete environment.

Clean and easy to understand implementations of many Quantum Reinforcement Learning agents as well as their classical analouges. Greately inspired by the orgininal CleanRL

Comparative study: Quantum vs. classical models for Cart Pole. Examining entanglement layers and data re-uploading, highlighting quantum model superiority.

Implementation of proof of concept quantum enhanced reinforced learning algorithm, able to find the sequence of quantum gates needed to approximate a given function.

QRLIT: Quantum Reinforcement Learning for Database Index Tuning

Python library for hybrid quantum-classical reinforcement learning agents using PennyLane and Gymnasium.

GitHub repo for Qiskit Hackathon "Quantum Reinforcement Learning" project

This repository contains the source code and results for the experiments presented in Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits.

Reinforcement Learning with Variational Quantum Circuits

Study of entanglement and Shannon entropies in Quantum Reinforcement Learning (and its classical counterpart) in a discrete environment.

Using reinforcement learning agents to control and optimize quantum gate operations

3D container loading optimization using classical RL (PPO, A3C) and quantum-classical hybrid RL (Quantum PPO, Quantum A3C) with VQE actor and QAOA critic circuit

Accelerated Quantum Reinforcement Learning Benchmarking with JAX

🎮 Clearing Super Mario Bros with Quantum Reinforcement Learning

PennyLane/PyTorch implementation of Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (Skolik et al., 2021)