  Quantum machine learning (QML) has received a lot of attention according to
its light training parameter numbers and speeds; and the advances of QML lead
to active research on quantum multi-agent reinforcement learning (QMARL).
Existing classical multi-agent reinforcement learning (MARL) features
non-stationarity and uncertain properties. Therefore, this paper presents a
simulation software framework for novel QMARL to control autonomous
multi-drones, i.e., quantum multi-drone reinforcement learning. Our proposed
framework accomplishes reasonable reward convergence and service quality
performance with fewer trainable parameters. Furthermore, it shows more stable
training results. Lastly, our proposed software allows us to analyze the
training process and results.
