Entanglement improves coordination in distributed systems

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.

💡 Research Summary

The paper investigates how shared quantum entanglement can be used to improve coordination in a distributed system consisting of two identical servers that must simultaneously handle a continuously available, preemptible baseline task and incoming customer requests that arrive in pairs. The baseline task yields output according to a function T(t) that is assumed to be differentiable, increasing, and strictly convex – meaning that longer uninterrupted processing periods generate disproportionately more work (e.g., due to setup costs or learning curves). Customer pairs arrive according to a Poisson process with rate λ; each request’s service time is an independent exponential random variable with mean 1/µ. Each server has an infinite‑capacity FIFO queue and follows an FCFS discipline. When a server’s queue is empty it works on the baseline task; otherwise it preempts the baseline task to serve customers.

Because the two routers that assign customers to servers are physically separated, real‑time communication is infeasible: the decision time scale (tens of microseconds) is far shorter than the round‑trip latency (sub‑millisecond to millisecond) over distances of order 10² km. Consequently, each router can only observe the service time of its own arriving request (X₁ for router A, X₂ for router B) and must decide locally whether to “split” the pair (send each request to a different server) or “bunch” them (send both to the same server). The long‑run fraction of split pairs is denoted p; the baseline throughput per server depends only on p, while the average customer waiting time W_q depends on which specific pairs are split.

The authors formulate the routing problem as an optimization over policies r(x₁,x₂)∈