Entanglement Distribution in LEO Satellite-based Dynamic Quantum Networks

Dec 8, 2024· Alena Chang , Yinxin Wan , Xuanli Lin , Guoliang Xue , Arunabha Sen · 1 min read
Summary
This paper models entanglement distribution through moving LEO satellites while accounting for Earth rotation, inter-satellite links, and multiple orbital shells. It transforms the dynamic network into a smaller static logical graph and develops two greedy routing algorithms, evaluated against an integer-programming benchmark.
Type
Publication
GLOBECOM 2024 - 2024 IEEE Global Communications Conference, 4485-4490. IEEE
publication

Abstract

Recent advances in space quantum communications envision Low Earth Orbit (LEO) satellites for global entanglement distribution. Entanglement distribution in such a network requires considerations such as satellite mobility, ground station mobility due to the Earth’s rotation, inter-satellite links, and multiple orbital shells, all of which have not been thoroughly studied in the networking literature. We ameliorate this deficit by defining a system model which accounts for all of the aforementioned factors. Using this system model, we formulate the dynamic optimal entanglement distribution (DOED) problem. We convert the DOED problem in a dynamic physical network to an instance of the problem in a static logical graph, the latter of which can be used to solve the former. We obtain a reduced logical graph from a logical graph, which can be used to reduce the complexity of solving the DOED problem. We propose two polynomial-time greedy algorithms for computing entanglement paths, as well as an integer linear programming (ILP)-based algorithm as a benchmark. We present evaluation results to demonstrate the advantages of our model and algorithms.

Yinxin Wan
Authors
Assistant Professor of Computer Science
Yinxin Wan is an assistant professor in the Department of Computer Science at the University of Massachusetts Boston. His research focuses on cybersecurity, secure and trustworthy artificial intelligence, network measurement, the Internet of Things, and quantum networking. He received his PhD in Computer Science from Arizona State University in 2023, advised by Guoliang Xue and Kuai Xu, and his bachelor’s degree in Information Security from the University of Science and Technology of China.
Xuanli Lin
Authors
PhD Student in Computer Science

Xuanli Lin is a fifth-year PhD student in the Computer Science department at Arizona State University, supervised by Dr. Guoliang Xue.

His research interests include network optimization, network security, artificial intelligence, and the Internet of Things.

Guoliang Xue
Authors
Professor of Computer Science and Engineering
Guoliang Xue is a professor in Arizona State University’s School of Computing and Augmented Intelligence and an IEEE Fellow. He investigates wireless and quantum networks, network security and privacy, and optimization. He earned his PhD in computer science from the University of Minnesota in 1991. His honors include the IEEE Communications Society’s 2019 William R. Bennett Prize, and he chaired the IEEE INFOCOM Steering Committee from 2020 through 2025.