  As one of the latest fields of interest in both academia and industry,
quantum computing has garnered significant attention. Among various topics in
quantum computing, variational quantum circuits (VQC) have been noticed for
their ability to carry out quantum deep reinforcement learning (QRL). This
paper verifies the potential of QRL, which will be further realized by
implementing quantum multi-agent reinforcement learning (QMARL) from QRL,
especially for Internet-connected autonomous multi-robot control and
coordination in smart factory applications. However, the extension is not
straightforward due to the non-stationarity of classical MARL. To cope with
this, the centralized training and decentralized execution (CTDE) QMARL
framework is proposed under the Internet connection. A smart factory
environment with the Internet of Things (IoT)-based multiple agents is used to
show the efficacy of the proposed algorithm. The simulation corroborates that
the proposed QMARL-based autonomous multi-robot control and coordination
performs better than the other frameworks.
