Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement offers fundamentally stronger correlations than classically achievable without communication. Crucially, these correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-work optimization problem in a distributed system comprising two servers. The system must process both a continuously available, preemptible baseline task and incoming customer requests arriving in pairs. System performance is characterized by the trade-off between baseline task throughput and customer waiting time. We present a rigorous analytical model demonstrating that when the baseline task throughput function is strictly convex, rewarding longer uninterrupted processing periods, entanglement-assisted routing strategies achieve Pareto-superior performance compared to optimal communication-free classical strategies. We prove this advantage through queueing-theoretic analysis, non-local game formulation, and computational certification of classical bounds. Our results identify distributed scheduling and coordination as a novel application domain for near-term entanglement-based quantum networks.

翻译：分布式系统中的协调常受通信延迟阻碍，导致性能下降。量子纠缠可提供经典系统无法通过通信实现的本征强关联性。关键在于，这些关联在测量时瞬时显现，与系统间的物理距离无关。本研究探讨了在包含两台服务器的分布式系统中，共享纠缠对双工作优化问题的应用。该系统需同时处理持续可用、可抢占的基线任务和成对到达的客户请求。系统性能通过基线任务吞吐量与客户等待时间之间的权衡关系表征。我们建立了严格的分析模型，证明当基线任务吞吐量函数为严格凸函数（即奖励更长的连续处理时段）时，采用纠缠辅助路由策略相比无通信最优经典策略可实现帕累托更优性能。通过排队论分析、非局域博弈建模及经典界限的计算验证，我们证明了该优势。本研究将分布式调度与协调确立为近期纠缠量子网络的新型应用领域。