Centralizing Task-based Approach to Quantum Network Control

TL;DR

This paper proposes a centralized, resource-centric control framework for quantum networks that improves scalability and performance over traditional layered architectures, demonstrated through simulation of various topologies.

Contribution

It introduces a task-based, centralized control approach for quantum networks, showing improved scalability and robustness in simulation across multiple topologies.

Findings

Grid and caveman topologies have higher low-delay request fractions.

CDFs of queue sizes shift linearly with reservation delay.

Star topology's priority queues saturate quickly at high request rates.

Abstract

For the last decade, layered stacks have dominated the way of reasoning about architectures for quantum networks. However, layered architectures impose stringent design and timing constraints on quantum networks, adding additional latency to the time required to serve an entanglement generation request. Moreover, increasing delays from the layered approach to network control causes degradation of state, effectively minimizing achievable fidelities. In this work we simulate a resource-centric, task-based approach to quantum network control by utilizing a centralized controller. Using the SeQUeNCe quantum network simulator, we implement the centralized controller which tracks quantum memory availability across all nodes, and schedules objectives in an offline fashion using a priority-based scheduler. We evaluate the performance of this controller on multiple topologies (bottleneck, grid, star, caveman) of significant scale, with varying reservation patterns; thereby we demonstrate the viability of the resource-centric task-based quantum network control framework for scaling. Our simulation results show that the caveman and grid topologies have a higher fraction of delivered requests with low delay compared to the star topology, but with a higher fraction of highly delayed requests as well. Furthermore, we find a linear shift of the CDFs in terms of queue size for all topologies depending on the reservation delay. More interestingly, we conclude that the CDFs of priority queues for the star topology converge fast into saturation for increasing request arrival rates, demonstrating together with the other results that the framework is robust for high load scenarios in quantum networks.