QSimPy：面向量子云资源管理的以学习为中心的仿真框架
QSimPy: A Learning-centric Simulation Framework for Quantum Cloud Resource Management
AI总结：
本文提出QSimPy，一个基于SimPy并集成Gymnasium的离散事件仿真框架，用于支持以深度强化学习为核心的量子云资源管理与任务放置优化研究。
AI中文摘要：
量子云计算是一种新兴的计算范式，允许以基于云的服务形式无缝访问量子硬件。然而，有效利用量子资源具有挑战性，需要稳健的仿真框架来进行有效的资源管理设计与评估。为满足这一需求，我们提出了QSimPy，这是一种新颖的离散事件仿真框架，其设计主要聚焦于促进以学习为中心的方法来解决云环境中的量子资源管理问题。在可扩展性、兼容性和可复用性原则的支撑下，QSimPy基于SimPy——一个广为人知的基于Python的仿真引擎——提供了一个轻量级仿真环境，用于对量子云资源和任务操作的动态进行建模。我们将Gymnasium环境集成到该框架中，以支持创建仿真环境，用于开发和评估基于强化学习的技术，从而优化量子云资源管理。QSimPy框架封装了量子云环境的操作复杂性，支持通过深度强化学习（DRL）方法开展动态任务分配与优化研究。我们还演示了QSimPy在开发用于量子任务放置问题的强化学习策略中的应用，展示了其作为未来量子云研究有用框架的潜力。
英文摘要：
Quantum cloud computing is an emerging computing paradigm that allows seamless access to quantum hardware as cloud-based services. However, effective use of quantum resources is challenging and necessitates robust simulation frameworks for effective resource management design and evaluation. To address this need, we proposed QSimPy, a novel discrete-event simulation framework designed with the main focus of facilitating learning-centric approaches for quantum resource management problems in cloud environments. Underpinned by extensibility, compatibility, and reusability principles, QSimPy provides a lightweight simulation environment based on SimPy, a well-known Python-based simulation engine for modeling dynamics of quantum cloud resources and task operations. We integrate the Gymnasium environment into our framework to support the creation of simulated environments for developing and evaluating reinforcement learning-based techniques for optimizing quantum cloud resource management. The QSimPy framework encapsulates the operational intricacies of quantum cloud environments, supporting research in dynamic task allocation and optimization through DRL approaches. We also demonstrate the use of QSimPy in developing reinforcement learning policies for quantum task placement problems, demonstrating its potential as a useful framework for future quantum cloud research.
