Service-the-Longest-Queue Among d Choices Policy for Quantum Entanglement Switching

2025-03-28

AI Summary

Quantum networks face resource-constrained scheduling at entanglement generation and swapping (EGS) hubs, where centralized queueing incurs high classical communication overhead and scalability bottlenecks. Method: This paper proposes a node-local service policy based on the Power-of-d-Choices paradigm, deploying request queues at end nodes rather than centrally; it integrates randomized sampling with longest-queue selection and introduces an adjustable backoff mechanism to reduce classical signaling. Contribution/Results: Leveraging mean-field modeling and queueing dynamics under Poisson arrivals and exponential service times, we prove that setting (d = 2) yields significant reduction in average processing latency, with diminishing marginal returns beyond this point. The mean-field approximation achieves high accuracy for moderately sized systems. This work establishes a new paradigm for low-overhead, scalable quantum network scheduling—shifting queue management to the edge while preserving performance guarantees.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

An Entanglement Generation Switch (EGS) is a quantum network hub that provides entangled states to a set of connected nodes by enabling them to share a limited number of hub resources. As entanglement requests arrive, they join dedicated queues corresponding to the nodes from which they originate. We propose a load-balancing policy wherein the EGS queries nodes for entanglement requests by randomly sampling d of all available request queues and choosing the longest of these to service. This policy is an instance of the well-known power-of-d-choices paradigm previously introduced for classical systems such as data-centers. In contrast to previous models, however, we place queues at nodes instead of directly at the EGS, which offers some practical advantages. Additionally, we incorporate a tunable back-off mechanism into our load-balancing scheme to reduce the classical communication load in the network. To study the policy, we consider a homogeneous star network topology that has the EGS at its center, and model it as a queueing system with requests that arrive according to a Poisson process and whose service times are exponentially distributed. We provide an asymptotic analysis of the system by deriving a set of differential equations that describe the dynamics of the mean-field limit and provide expressions for the corresponding unique equilibrium state. Consistent with analogous results from randomized load-balancing for classical systems, we observe a significant decrease in the average request processing time when the number of choices d increases from one to two during the sampling process, with diminishing returns for a higher number of choices. We also observe that our mean-field model provides a good approximation to study even moderately-sized systems.

Problem

• Load-balancing policy for quantum entanglement switch
• Reducing classical communication with tunable back-off
• Asymptotic analysis of queueing system dynamics

Innovation

• Load-balancing with power-of-d-choices policy
• Queues placed at nodes for practical advantages
• Tunable back-off mechanism reduces communication load