Scaling the number of qubits available across multiple quantum devices is an active area of research within distributed quantum computing (DQC). This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate density, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.

翻译：在多台量子设备间扩展可用量子比特数是分布式量子计算领域的一个活跃研究方向，这包括网络中多台量子设备上的量子电路编译与执行管理。后一方面极具挑战性，因为在降低作业批次完成时间这一传统目标之外，还需考虑诸多量子计算特有的约束条件，包括量子处理单元利用率、非局域门密度以及排队分布式量子计算任务带来的延迟。本研究提出、模拟并评估了一系列调度策略，包括优先考虑量子处理单元资源最大化的启发式方法、基于异构网络连接的节点选择机制、作业完成时的异步节点释放策略，以及基于近端策略优化强化学习的调度方案。这些方法在不同类型的分布式量子计算任务和网络条件下，与传统先进先出和列表调度器进行了性能对比测试，以评估其在网络内设备间分配分布式量子计算任务的效果。