Quantum Multi-Agent Reinforcement Learning for Aerial Ad-Hoc Networks
Theodora-Augustina Drăgan, Akshat Tandon, Tom Haider, Carsten Strobel, Jasper Simon Krauser, Jeanette Lorenz
2025
Abstract
Quantum machine learning (QML) as combination of quantum computing with machine learning (ML) is a promising direction to explore, in particular due to the advances in realizing quantum computers and the hoped-for quantum advantage. A field within QML that is only little approached is quantum multi-agent reinforcement learning (QMARL), despite having shown to be potentially attractive for addressing industrial applications such as factory management, cellular access and mobility cooperation. This paper presents an aerial communication use case and introduces a hybrid quantum-classical (HQC) ML algorithm to solve it. This use case intends to increase the connectivity of flying ad-hoc networks and is solved by an HQC multi-agent proximal policy optimization algorithm in which the core of the centralized critic is replaced with a data reuploading variational quantum circuit. Results show a slight increase in performance for the quantum-enhanced solution with respect to a comparable classical algorithm, earlier reaching convergence, as well as the scalability of such a solution: an increase in the size of the ansatz, and thus also in the number of trainable parameters, leading to better outcomes. These promising results show the potential of QMARL to industrially-relevant complex use cases.
DownloadPaper Citation
in Harvard Style
Drăgan T., Tandon A., Haider T., Strobel C., Krauser J. and Lorenz J. (2025). Quantum Multi-Agent Reinforcement Learning for Aerial Ad-Hoc Networks. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO; ISBN 978-989-758-737-5, SciTePress, pages 731-741. DOI: 10.5220/0013375100003890
in Bibtex Style
@conference{qaio25,
author={Theodora-Augustina Drăgan and Akshat Tandon and Tom Haider and Carsten Strobel and Jasper Krauser and Jeanette Lorenz},
title={Quantum Multi-Agent Reinforcement Learning for Aerial Ad-Hoc Networks},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO},
year={2025},
pages={731-741},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013375100003890},
isbn={978-989-758-737-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO
TI - Quantum Multi-Agent Reinforcement Learning for Aerial Ad-Hoc Networks
SN - 978-989-758-737-5
AU - Drăgan T.
AU - Tandon A.
AU - Haider T.
AU - Strobel C.
AU - Krauser J.
AU - Lorenz J.
PY - 2025
SP - 731
EP - 741
DO - 10.5220/0013375100003890
PB - SciTePress