论文标题
基于学习的可持续多用户计算移动边缘量子计算的卸载
Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing
论文作者
论文摘要
在本文中，提出了一种新颖的移动边缘量词计算（MEQC）的范式，该范式将量子计算能力带到更接近移动用户的移动边缘网络（即边缘设备）。首先，我们提出了一个MEQC系统模型，其中移动用户可以通过带有低温组件和容忍故障方案的边缘服务器将计算任务卸载到可扩展的量子计算机。其次，我们表明，从经典和量子计算的最佳潜伏期和能源成本方面，获得MEQC中部分卸载问题的集中解决方案是NP-HARD。第三，我们建议使用近端策略优化的多代理混合连接深度强化学习，以学习长期可持续的卸载策略，而没有先验知识。最后，实验结果表明，与不同系统设置下的现有基线解决方案相比，所提出的算法至少可以减少至少30％的成本。
In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is proposed, which brings quantum computing capacities to mobile edge networks that are closer to mobile users (i.e., edge devices). First, we propose an MEQC system model where mobile users can offload computational tasks to scalable quantum computers via edge servers with cryogenic components and fault-tolerant schemes. Second, we show that it is NP-hard to obtain a centralized solution to the partial offloading problem in MEQC in terms of the optimal latency and energy cost of classical and quantum computing. Third, we propose a multi-agent hybrid discrete-continuous deep reinforcement learning using proximal policy optimization to learn the long-term sustainable offloading strategy without prior knowledge. Finally, experimental results demonstrate that the proposed algorithm can reduce at least 30% of the cost compared with the existing baseline solutions under different system settings.
