Quantum Multi-Agent Reinforcement Learning for Aerial Ad-hoc Networks

TL;DR

This paper explores quantum multi-agent reinforcement learning applied to aerial ad-hoc networks, demonstrating a hybrid quantum-classical algorithm that slightly outperforms classical methods and shows scalability for complex industrial applications.

Contribution

It introduces a hybrid quantum-classical multi-agent reinforcement learning algorithm for aerial networks, utilizing a variational quantum circuit within a centralized critic, and demonstrates its potential advantages.

Findings

Quantum-enhanced solution slightly outperforms classical algorithms.

Earlier convergence achieved with quantum approach.

Scalability demonstrated with larger ansatz and more trainable parameters.

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.