QCOEM：基于进化多目标优化的量子云编排
QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization
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中文总结 AI 辅助
研究量子云平台跨异构后端动态编排工作负载的问题，提出QCOEM框架，利用进化算法进行多目标优化调度，比较NSGA-II和NSGA-III，应用AASF规则，性能评估显示该框架能提供稳定高保真执行及轻量级资源管理。
中文摘要 AI 辅助
量子云平台需要跨异构量子计算后端动态编排工作负载，而异构量子计算后端的噪声分布情况、量子比特拓扑结构和队列会随时间变化。现有编排器使用与噪声无关的启发式算法，忽略特定于后端的错误，导致执行保真度降低、负载不平衡和频繁重新调度。为应对这些挑战，我们提出了QCOEM——一个利用进化算法进行量子任务调度多目标优化的量子云编排框架。我们比较了NSGA-II和NSGA-III在联合最小化平均完成时间、执行错误率和负载不平衡方面的表现。为从非凸帕累托前沿中选择调度，我们应用增强成就标量化函数（AASF）作为基于偏好的决策规则，将帕累托集映射到与用户优先级一致的单个可调度调度。我们在异构量子云环境中的广泛性能评估表明，与与噪声无关的启发式算法相比，任务重新调度为零，平均保真度提高约30%，同时保持有限的调度开销。实验结果表明，我们的QCOEM框架可以为量子云计算提供稳定、高保真的执行和轻量级资源管理。
英文摘要
Quantum cloud platforms need to dynamically orchestrate workloads across heterogeneous quantum computation backends whose noise profiles, qubit topologies, and queues vary over time. Existing orchestrators use noise-agnostic heuristics that ignore backend-specific errors, causing reduced execution fidelity, load imbalance, and frequent rescheduling. To address these challenges, we propose QCOEM - a Quantum Cloud Orchestration framework that leverages Evolutionary algorithms for Multi-objective optimization of quantum task scheduling. We compare NSGA-II and NSGA-III for jointly minimizing mean completion time, execution error rate, and load imbalance. To select schedules from a non-convex Pareto front, we apply an Augmented Achievement Scalarization Function (AASF) as a preference-based decision rule that maps the Pareto set to a single dispatchable schedule aligned with user priorities. Our extensive performance evaluation in a heterogeneous quantum cloud environment shows zero task rescheduling and about 30% higher mean fidelity than noise-agnostic heuristics, while maintaining bounded scheduling overhead. The experiment results indicate that our QCOEM framework can deliver stable, high-fidelity execution and lightweight resource management for quantum cloud computing.
