Entanglement improves coordination in distributed systems

• URL: http://arxiv.org/abs/2602.04588v1
• Date: Wed, 04 Feb 2026 14:14:41 GMT
• Title: Entanglement improves coordination in distributed systems
• Authors: Francisco Ferreira da Silva, Stephanie Wehner,
• Abstract summary: Coordination in distributed systems is often hampered by communication latency, which degrades performance.<n>We investigate the application of shared entanglement to a dual-work optimization problem in a distributed system comprising two servers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• Measurement-based quantum computation with variable-range interacting systems [0.0]
  We show that weighted graph states (WGS) generated via variable-range interacting Ising spin systems can successfully implement single- and two-qubit gates.<n>We identify a threshold fall-off rate of the interaction above which the fidelity of both universal single- and two-qubit gates consistently exceeds $90%$ accuracy.
  arXiv  Detail & Related papers  (2025-06-13T15:59:26Z)
• Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning [1.0542466736167886]
  Current quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors.<n>We introduce a simulation-based tool that supports distributed scheduling and concurrent execution of quantum jobs on networked QPUs connected via real-time classical channels.
  arXiv  Detail & Related papers  (2025-06-12T16:54:19Z)
• Network-Aware Scheduling for Remote Gate Execution in Quantum Data Centers [8.528068737844364]
  We evaluate two entanglement scheduling strategies-static and dynamic-and analyze their performance.<n>We show that dynamic scheduling consistently outperforms static scheduling in scenarios with high entanglement parallelism.
  arXiv  Detail & Related papers  (2025-04-28T18:22:22Z)
• Analysis of Asynchronous Protocols for Entanglement Distribution in Quantum Networks [9.971549076128268]
  We explore two minimal asynchronous protocols for entanglement in quantum networks. A parallel scheme generating entanglement independently at the link level, and a sequential scheme extending entanglement iteratively from one party to the other. Our findings suggest the sequential scheme's superiority due to comparable performance with the parallel scheme, coupled with simpler implementation.
  arXiv  Detail & Related papers  (2024-05-03T18:04:11Z)
• Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
  In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions. The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
  arXiv  Detail & Related papers  (2022-05-13T21:09:41Z)
• Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling [57.271494741212166]
  This paper formulates and solves the communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.
  arXiv  Detail & Related papers  (2021-07-24T11:39:17Z)
• Quantum communication complexity beyond Bell nonlocality [87.70068711362255]
  Efficient distributed computing offers a scalable strategy for solving resource-demanding tasks. Quantum resources are well-suited to this task, offering clear strategies that can outperform classical counterparts. We prove that a new class of communication complexity tasks can be associated to Bell-like inequalities.
  arXiv  Detail & Related papers  (2021-06-11T18:00:09Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System [54.588242387136376]
  We introduce KaiS, a learning-based scheduling framework for edge-cloud systems. First, we design a coordinated multi-agent actor-critic algorithm to cater to decentralized request dispatch. Second, for diverse system scales and structures, we use graph neural networks to embed system state information. Third, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration.
  arXiv  Detail & Related papers  (2021-01-17T03:45:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.