Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments

Georg Kruse, Theodora-Augustina Drăgan, Robert Wille, Jeanette Miriam Lorenz

2024

Abstract

Quantum Reinforcement Learning (QRL) emerged as a branch of reinforcement learning (RL) that uses quantum submodules in the architecture of the algorithm. One branch of QRL focuses on the replacement of neural networks (NN) by variational quantum circuits (VQC) as function approximators. Initial works have shown promising results on classical environments with discrete action spaces, but many of the proposed architectural design choices of the VQC lack a detailed investigation. Hence, in this work we investigate the impact of VQC design choices such as angle embedding, encoding block architecture and postprocessesing on the training capabilities of QRL agents. We show that VQC design greatly influences training performance and heuristically derive enhancements for the analyzed components. Additionally, we show how to design a QRL agent in order to solve classical environments with continuous action spaces and benchmark our agents against classical feed-forward NNs.

Paper Citation

in Harvard Style

Kruse G., Drăgan T., Wille R. and Lorenz J. (2024). Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 393-400. DOI: 10.5220/0012353100003636

in Bibtex Style

@conference{icaart24,
author={Georg Kruse and Theodora-Augustina Drăgan and Robert Wille and Jeanette Miriam Lorenz},
title={Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={393-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012353100003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
SN - 978-989-758-680-4
AU - Kruse G.
AU - Drăgan T.
AU - Wille R.
AU - Lorenz J.
PY - 2024
SP - 393
EP - 400
DO - 10.5220/0012353100003636
PB - SciTePress