This paper proposes a novel quantum multi-agent actor-critic networks (QMACN) algorithm for autonomously constructing a robust mobile access system using multiple unmanned aerial vehicles (UAVs). For the cooperation of multiple UAVs for autonomous mobile access, multi-agent reinforcement learning (MARL) methods are considered. In addition, we also adopt the concept of quantum computing (QC) to improve the training and inference performances. By utilizing QC, scalability and physical issues can happen. However, our proposed QMACN algorithm builds quantum critic and multiple actor networks in order to handle such problems. Thus, our proposed QMACN algorithm verifies the advantage of quantum MARL with remarkable performance improvements in terms of training speed and wireless service quality in various data-intensive evaluations. Furthermore, we validate that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access. Our data-intensive simulation results verify that our proposed QMACN algorithm outperforms the other existing algorithms.

翻译：本文提出一种新颖的量子多智能体演员-评论家网络（QMACN）算法，用于利用多架无人驾驶飞行器（UAV）自主构建鲁棒移动接入系统。针对多架无人机自主移动接入的协同问题，我们考虑了多智能体强化学习（MARL）方法。同时，引入量子计算（QC）概念以提升训练与推理性能。尽管采用量子计算可能引发可扩展性与物理实现问题，但所提QMACN算法通过构建量子评论家网络与多个演员网络来应对这些挑战。因此，在多种数据密集型评估中，该算法在训练速度与无线服务质量方面展现出量子MARL的显著优势。此外，我们验证了噪声注入策略可用于处理环境不确定性，从而实现鲁棒的移动接入。大量仿真结果表明，所提QMACN算法性能优于现有其他算法。