Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. 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 rate, 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：分布式量子计算作为一种通过多台互联量子设备扩展量子比特数量的方法，目前正得到积极研究。这包括量子电路编译以及网络内多台量子设备上的执行管理。后者极具挑战性，因为尽管减少任务批次的完成时间仍是相关目标，但必须考虑量子领域特有的约束条件，包括量子处理单元利用率、非局域门速率以及排队分布式量子计算任务带来的延迟。本研究提出、模拟并评估了多种调度策略，其中包括：优先最大化量子处理单元利用率的启发式算法、基于异构网络连通性的节点选择机制、任务完成时异步释放节点的策略，以及基于近端策略优化的强化学习调度方法。这些方法在分布式量子计算任务类型和网络条件变化的情况下，与传统的先入先出调度器和列表调度器进行了基准对比，用于分布式量子计算任务在网络内设备间的分配。