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Quantum Machine Learning Quantum Simulation

Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning

arXiv

Authors: Chanyoung Park, Jae Pyoung Kim, Won Joon Yun, Soohyun Park, Soyi Jung, Joongheon Kim

Year

2022

Paper ID

6540

Status

Preprint

Abstract Read

~2 min

Abstract Words

101

Citations

N/A

Abstract

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.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2022 reference point for readers tracking recent quantum research.
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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2211.15375 [2] arXiv https://arxiv.org/abs/2211.15375 [3] Publisher https://arxiv.org/abs/2211.15375

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