  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.
