Entanglement improves coordination in distributed systems

2025-09-08

Proceedings of the 2nd Workshop on Quantum Networks and Distributed Quantum Computing

AI Summary

This work addresses the inefficiency in coordination caused by communication delays in distributed systems by proposing a communication-free task scheduling mechanism leveraging quantum entanglement to jointly optimize baseline task throughput and client request waiting time. For the first time, quantum entanglement is introduced into distributed scheduling, and through queueing-theoretic analysis, nonlocal game modeling, and derivation of upper bounds for classical strategies, it is rigorously shown that, under strictly convex baseline reward functions, entanglement-assisted strategies strictly Pareto-dominate all classical communication-free strategies. Both theoretical analysis and numerical experiments confirm the superiority of the proposed approach in this bi-objective optimization setting, offering a novel paradigm for near-term quantum-network-enhanced distributed systems.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Quantum Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Coordination in distributed systems is often hampered by communication latency, which degrades performance. Quantum entanglement enables correlations stronger than classically possible without communication. Such correlations manifest instantaneously upon measurement, irrespective of the physical distance separating the systems. We investigate the application of shared entanglement to a dual-objective optimization problem in a distributed system comprising two servers. The servers process both a continuously available, preemptible baseline task and incoming paired customer requests, to maximize the baseline task throughput subject to a Quality of Service (QoS) constraint on average customer waiting time. We present a rigorous analytical model demonstrating that an entanglement-assisted routing strategy allows the system to achieve higher baseline throughput compared to communication-free classical strategies, provided the baseline task's output exhibits sufficiently increasing returns with processing time. This advantage stems from entanglement enabling better coordination, which allows the system to satisfy the customer QoS constraint with a lower overall probability of splitting customer requests, leading to more favorable conditions for baseline task processing and thus higher throughput. We further show that the magnitude of this throughput gain is particularly pronounced for tasks exhibiting increasing returns, where output grows super-linearly with processing time. Our results identify optimization of scheduling in distributed systems as a novel application domain for near-term quantum networks.

Problem

• distributed coordination
• communication latency
• task scheduling
• quantum entanglement
• performance trade-off

Innovation

• quantum entanglement
• distributed coordination
• Pareto-superior performance
• non-local games
• queueing theory