This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple unmanned aerial vehicles (UAVs), the application of multi-agent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of a quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of quantum computing, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this paper validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.

翻译：本文提出一种名为量子多智能体Actor-Critic网络（QMACN）的新算法，用于自主构建基于多无人机的稳健移动接入系统。在促进多无人机协同作业的背景下，多智能体强化学习（MARL）技术被视为一种有前景的方法。该方法使无人机能够通过集体学习优化其在共享环境中的行动，最终实现更高效的协作行为。此外，本研究引入量子计算（QC）原理以增强无人机的训练过程与推理能力。通过利用量子计算的独特计算优势，所提方法旨在提升无人机系统的整体效能。然而，量子计算的应用受限于近中等规模量子（NISQ）对量子比特使用的约束，带来了可扩展性挑战。该算法通过实现量子集中式评论家有效缓解了NISQ限制的影响。此外，通过多种数据密集型评估验证了QMACN在训练速度与无线服务质量方面性能提升的优势。最后，本文验证了噪声注入方案可用于应对环境不确定性，以实现稳健的移动接入。