Distributed quantum computing (DQC) enables scalable quantum computations by distributing large quantum circuits on multiple quantum processing units (QPUs) in the quantum cloud. In DQC, after partitioning quantum circuits, they must be scheduled and executed on heterogenous QPUs while balancing latency, overhead, QPU communication resource limits. However, since fully functioning quantum communication networks have not been realized yet, near-term quantum clouds will only rely on local operations and classical communication settings between QPUs, without entangled quantum links. Additionally, existing DQC scheduling frameworks do not account for user-defined execution deadlines and adopt inefficient wire cutting techniques. Accordingly, in this work, a deadline aware DQC scheduling framework with efficient wire cutting for near-term quantum cloud is proposed. The proposed framework schedules partitioned quantum subcircuits while accounting for circuit deadlines and QPU capacity limits. It also captures dependencies between partitioned subcircuits and distributes the execution of the sampling shots on different QPUs to have efficient wire cutting and faster execution. In this regard, a deadline-aware circuit scheduling optimization problem is formulated, and solved using simulated annealing. Simulation results show a marked improvement over existing shot-agnostic frameworks under urgent deadlines, reaching a 12.8% increase in requests served before their deadlines. Additionally, the proposed framework serves 8.16% more requests, on average, compared to state-of-the-art dependency-agnostic baseline frameworks, and by 9.60% versus the dependency-and-shot-agnostic baseline, all while achieving a smaller makespan of the DQC execution. Moreover, the proposed framework serves 23.7%, 24.5%, and 25.38% more requests compared to greedy, list scheduling, and random schedulers, respectively.

翻译：分布式量子计算（DQC）通过将大型量子电路分布到量子云中的多个量子处理单元（QPU）上，实现了可扩展的量子计算。在DQC中，量子电路被划分后，必须在异构的QPU上进行调度与执行，同时权衡延迟、开销以及QPU通信资源的限制。然而，由于功能完备的量子通信网络尚未实现，近远期量子云将仅依赖于QPU之间的本地操作与经典通信设置，而无需纠缠量子链路。此外，现有的DQC调度框架未考虑用户定义的执行截止时间，并采用了低效的线路切割技术。为此，本文提出了一种面向近远期量子云的、具备高效线路切割的截止时间感知DQC调度框架。该框架在调度划分后的量子子电路时，兼顾了电路截止时间与QPU容量限制。它同时捕捉划分后子电路之间的依赖关系，并将采样次数的执行分布到不同的QPU上，以实现高效的线路切割与更快的执行。在此背景下，本文构建了一个截止时间感知的电路调度优化问题，并采用模拟退火算法求解。仿真结果表明，在紧急截止时间下，该框架相较于现有的采样次数无关框架有显著提升，在截止时间前完成的请求数量增加了12.8%。此外，与最先进的依赖关系无关基准框架相比，所提框架平均多服务了8.16%的请求；与依赖关系和采样次数均无关的基准框架相比，多服务了9.60%的请求，同时实现了更小的DQC执行总完成时间。更进一步，与贪婪调度、列表调度及随机调度器相比，所提框架分别多服务了23.7%、24.5%和25.38%的请求。