Computing an Optimal Entanglement Path with Throughput and Fidelity Considerations

Aug 27, 2026· Guoliang Xue , Nageswara S. V. Rao , Don Towsley , Gayane Vardoyan , Zunzheng Zhang , Xuanli Lin , Muneer Alshowkan , Joseph M. Lukens , Nicholas A. Peters , Saikat Guha · 1 min read
Summary
This paper develops a quantum-network routing algorithm that maximizes entanglement throughput while meeting a fidelity threshold, accounting for node buffers and sequential swapping. It proves the general feasibility problem is NP-hard and uses entanglement probability distributions and path dominance to find optimal routes, with reported subsecond results on networks containing thousands of nodes.
Type
Publication
IEEE Transactions on Networking. IEEE
publication

Abstract

Entanglement distribution is a core function of quantum networks essential for operations including teleportation, distributed quantum sensing, and multisite computation. Entanglement throughput and fidelity are two critical performance measures that depend on the quantum transmission along the links and swapping operations at the repeaters along the path. We study the problem of computing a end-to-end entanglement path that satisfies both fidelity and throughput requirements, leveraging qubit buffers at the nodes and considering the sequential swapping order. We show that the general problem of simultaneously satisfying both metrics to be NP-hard, and develop an algorithm to maximize throughput subject to a given fidelity threshold. We introduce the concepts of entanglement probability distribution and path domination and exploit them in the design of our algorithm. Extensive numerical results show that our algorithm can find optimal solutions in networks with thousands of nodes in less than a second. We also describe practical and possible implementation aspects of this algorithm in terms of devices and architecture support.

Published online as an Early Access article on August 27, 2026. Final volume and page numbers have not yet been assigned.

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.
Nageswara S. V. Rao
Authors
Corporate Fellow
Nageswara S. V. Rao is a Corporate Fellow at Oak Ridge National Laboratory, which he joined in 1993. His research covers high-performance and quantum networking, information fusion, machine learning, and connected scientific instruments. He earned his PhD in computer science from Louisiana State University in 1988. He is an IEEE Fellow and a recipient of the IEEE Computer Society Technical Achievement Award and an R&D 100 Award.
Zunzheng Zhang
Authors
PhD Student in Computer Science
Zunzheng Zhang is a PhD student in computer science at Arizona State University. He studies network optimization and quantum networks. Before joining ASU, he earned his bachelor’s and master’s degrees in electronic engineering from Nanjing University in 2021 and 2024, respectively.
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.