发表机构
University of Information Technology, Vietnam National University, Ho Chi Minh City; Monash University; CSIRO（胡志明市越南国家大学信息技术大学; 莫纳什大学; 澳大利亚联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出QRLQ框架，将参数化量子电路与决斗双深度Q网络结合，在量子云编排中实现成本与延迟权衡，相比启发式方法降低成本延迟，且参数更少。
AI 中文摘要
量子云计算通过量子即服务（QaaS）模型交付，提供对量子计算资源的访问。然而，在本质上异构的量子资源上应用统一的时间计费定价，显著复杂化了任务编排，尤其是在处理执行成本与系统性能之间的权衡时。虽然启发式方法依赖预定义的调度规则，经典深度强化学习（DRL）模型在此场景中可能需要更多可训练参数。受参数化量子电路（PQCs）作为紧凑函数逼近器潜力的启发，我们提出了QRLQ，一个成本延迟感知的量子云调度框架，将PQCs与决斗双深度Q网络（D3QN）集成，以动态考虑成本和延迟。我们的仿真结果表明，QRLQ实现了比启发式基线更低的平均成本和延迟，相对于基于可用性和基于轮转的启发式方法，平均成本降低了5-11%，相对于最强和最弱的启发式基线，平均延迟分别降低了17%和82%，同时将执行保真度保持在保真度贪婪策略的2%以内。与经典DRL基线相比，QRLQ在实现可比调度性能的同时，使用的可训练参数减少了72%。这项工作探索了在量子云环境中使用QRL进行任务编排的可行性，并展示了其在成本延迟感知量子资源管理方面的潜力。
英文摘要
Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff between execution costs and system performance. While heuristic methods rely on predefined scheduling rules, classical deep reinforcement learning (DRL) models may require more trainable parameters in this setting. Motivated by the potential of parameterised quantum circuits (PQCs) as compact function approximators, we propose QRLQ, a cost-delay-aware quantum cloud scheduling framework integrating PQCs with a dueling double deep Q-network (D3QN) to dynamically account for both cost and delay. Our simulation results show that QRLQ achieves lower mean cost and delay than the heuristic baselines, achieving a 5-11% lower mean cost relative to availability-based and rotation-based heuristics and reducing mean delay by 17% and 82% relative to the strongest and weakest heuristic baselines, respectively, while retaining execution fidelity within 2% of a fidelity-greedy policy. Compared with the classical DRL baseline, QRLQ achieves comparable scheduling performance while using 72% fewer trainable parameters. This work explores the feasibility of using QRL for task orchestration in quantum cloud environments and demonstrates its potential for cost-delay-aware quantum resource management.
