As quantum computing moves from isolated experiments toward integration with large-scale workflows, the integration of quantum devices into HPC systems has gained much interest. Quantum cloud providers expose shared devices through first-come first-serve queues where a circuit that executes in 3 seconds can spend minutes to an entire day waiting. Minimizing this overhead while maintaining execution fidelity is the central challenge of quantum cloud scheduling, and existing approaches treat the two as separate concerns. We present Qurator, an architecture-agnostic quantum-classical task scheduler that jointly optimizes queue time and circuit fidelity across heterogeneous providers. Qurator models hybrid workloads as dynamic DAGs with explicit quantum semantics, including entanglement dependencies, synchronization barriers, no-cloning constraints, and circuit cutting and merging decisions, all of which render classical scheduling techniques ineffective. Fidelity is estimated through a unified logarithmic success score that reconciles incompatible calibration data from IBM, IonQ, IQM, Rigetti, AQT, and QuEra into a canonical set of gate error, readout fidelity, and decoherence terms. We evaluate Qurator on a simulator driven by four months of real queue data using circuits from the Munich Quantum Toolkit benchmark suite. Across load conditions from 5 to 35,000 quantum tasks, Qurator stays within 1% of the highest-fidelity baseline at low load while achieving 30-75% queue time reduction at high load, at a fidelity cost bounded by a user-specified target.

翻译：随着量子计算从孤立实验向大规模工作流集成迈进，量子设备与高性能计算系统的融合日益受到关注。量子云提供商通过先到先服务队列公开共享设备，一个仅需3秒执行的量子电路可能等待数分钟乃至一整天。如何在保持执行保真度的同时最小化此类开销，是量子云调度的核心挑战，而现有方法将两者割裂处理。本文提出Qurator——一种架构无关的量子-经典任务调度器，可跨异构提供商标联合优化排队时间与电路保真度。Qurator将混合工作负载建模为具有显式量子语义的动态有向无环图，包括纠缠依赖、同步屏障、不可克隆约束以及电路切割与合并决策——这些特性均使经典调度技术失效。保真度通过统一的保真度评估方法进行估算，该方法将来自IBM、IonQ、IQM、Rigetti、AQT和QuEra的不兼容校准数据整合为规范的逻辑成功分数体系，涵盖门误差、读取保真度和退相干项。我们利用来自慕尼黑量子工具包基准套件的电路，基于四个月真实队列数据驱动的模拟器对Qurator进行评估。在5至35,000个量子任务的负载条件下，Qurator在低负载时与最高保真度基线的偏差保持在1%以内，在高负载时实现30-75%的排队时间缩减，且保真度损失受用户指定目标约束。