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：本文提出了利用多无人驾驶飞行器自主构建强力移动接入系统的新型量子多剂行为者――化学网络(QMACN)算法。为了多个无人驾驶航空器合作自主移动接入,还考虑了多剂强化学习方法。此外,我们还采用了量子计算概念来改进培训和推论性能。通过使用QC,可缩放性和物理问题可以发生。然而,我们提议的QMACN算法建立了量子评论家和多个行为者网络,以便处理这些问题。因此,我们提议的QMACN算法在各种数据密集评估中验证了量子MARL的优势,在培训速度和无线服务质量方面业绩的显著改进。此外,我们确认可以使用噪音注入计划处理环境不确定性,以实现强力移动接入。我们的数据密集模拟结果证实,我们提议的QMACN算法比其他现有算法更完美。