Authors:
Theodora-Augustina Drăgan
1
;
Akshat Tandon
2
;
Tom Haider
1
;
Carsten Strobel
2
;
Jasper Simon Krauser
2
and
Jeanette Lorenz
1
Affiliations:
1
Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany
;
2
Airbus Central Research & Technology, Ottobrunn, Germany
Keyword(s):
Quantum Multi-Agent Reinforcement Learning, Proximal Policy Optimization, Communication, Networks.
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 clas
sical 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.
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