The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)-based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81% to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.

翻译：量子云计算范式因其计算资源的动态性与异构性，在任务放置方面提出了独特的挑战。传统的启发式方法难以适应量子计算领域的快速演进。本文提出DRLQ，一种基于深度强化学习（DRL）的新型量子云计算环境任务放置技术，旨在优化任务完成时间与量子任务调度效率。该方法采用深度Q网络（DQN）架构，并结合Rainbow DQN技术进行增强，以构建动态任务放置策略。此方法是量子云资源管理领域的开创性工作之一，能够实现量子云环境的自适应学习与决策，并根据变化的条件和资源可用性有效优化任务放置。我们使用QSimPy仿真工具包进行了大量实验以评估本方法的性能，结果表明其在任务执行效率上取得显著提升，并减少了量子任务重新调度的需求。实验结果显示，与其他启发式方法相比，采用DRLQ方法进行任务放置可将量子任务总完成时间降低37.81%至72.93%，并有效避免任务重新调度的尝试。