论文标题
量子多代理增强学习通过变异量子电路设计
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
论文作者
论文摘要
近年来，量子计算（QC）引起了行业和学术界的广泛关注。特别是，在各种QC研究主题中，变分量子电路（VQC）可实现量子深钢筋学习（QRL）。 QRL的许多研究表明，在训练参数数量的约束下，QRL优于经典的加固学习（RL）方法。本文扩展并演示QRL到量子多代理RL（QMARL）。但是，由于噪声中间尺度量子（NISQ）的挑战和经典多代理RL（MARL）中的噪声中间尺度量子（NISQ）的挑战，因此QRL向QMARL的扩展并不直接。因此，本文提出了集中式培训和分散执行（CTDE）QMARL框架，通过设计新颖的VQC来应对这些问题。为了证实QMARL框架，本文在单跳环境中进行了QMARL演示，其中边缘代理将数据包卸载到云。广泛的演示表明，所提出的QMARL框架比经典框架提高了总奖励的57.7％。
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.
