  In recent years, quantum computing (QC) has been getting a lot of attention
from industry and academia. Especially, among various QC research topics,
variational quantum circuit (VQC) enables quantum deep reinforcement learning
(QRL). Many studies of QRL have shown that the QRL is superior to the classical
reinforcement learning (RL) methods under the constraints of the number of
training parameters. This paper extends and demonstrates the QRL to quantum
multi-agent RL (QMARL). However, the extension of QRL to QMARL is not
straightforward due to the challenge of the noise intermediate-scale quantum
(NISQ) and the non-stationary properties in classical multi-agent RL (MARL).
Therefore, this paper proposes the centralized training and decentralized
execution (CTDE) QMARL framework by designing novel VQCs for the framework to
cope with these issues. To corroborate the QMARL framework, this paper conducts
the QMARL demonstration in a single-hop environment where edge agents offload
packets to clouds. The extensive demonstration shows that the proposed QMARL
framework enhances 57.7% of total reward than classical frameworks.
