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走向大规模工作流集成，将量子设备整合至高性能计算系统已引起广泛关注。量子云服务商通过"先到先服务"队列共享设备，一个执行仅需3秒的量子电路可能需要等待数分钟乃至一整天。在保持执行保真度的同时最小化此开销，是量子云调度的核心挑战，而现有方法将两者视为独立问题。本文提出Qurator，一种架构无关的量子-经典任务调度器，可跨异构服务商联合优化队列等待时间与电路保真度。Qurator将混合工作流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分与合并决策——这些特性使得经典调度技术失效。保真度通过统一的对数成功分数估算，该分数将IBM、IonQ、IQM、Rigetti、AQT与QuEra等平台的不可比校准数据，规约至门错误率、读取保真度与退相干时间这组规范指标。我们基于慕尼黑量子工具包基准套件的电路，使用四个月的真实队列数据驱动的模拟器对Qurator进行评估。在5至35000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偏差不超过1%，高负载时可实现30%-75%的队列时间缩减，其保真度代价受用户指定目标约束。