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Efficient and Flexible Multi-Qubit Entanglement Transmission in Quantum Networks

  • Yanan Gao
  • , Song Yang*
  • , Fan Li
  • , Youqi Li
  • , Liehuang Zhu
  • , Stojan Trajanovski
  • , Xiaoming Fu
  • *Corresponding author for this work
  • Beijing Institute of Technology
  • Microsoft USA
  • University of Göttingen

Research output: Contribution to journalArticlepeer-review

Abstract

The unprecedented advancements in quantum technology have opened new prospects for the widespread adoption of quantum applications, placing new demands on the information transmission capabilities of large-scale quantum networks. Long-distance and stable entanglements are deemed as the lifeline in quantum network communication. However, some weaknesses, e.g., quantum decoherence, scarce quantum memory, and uneven-quality entanglement, of the quantum entanglement hinder the development. In this paper, we propose Sophon, an online transmission framework for quantum networks, which utilizes high-dimensional entanglements to concurrently transmit multi-qubit data to satisfy the transmission requirements of the real-time request set. We first model the quantum network with multi-qubit entanglement represented by W quantum state and then formulate the Entanglement Routing and Qubit Provisioning (ERQP) problem as a global-local optimization process. To solve the ERQP problem, we distributedly regard each network node as an RL agent for resource provisioning and extend the step-updating of the Markov Decision Process by introducing a centralized controller for entanglement route selection to optimize local and global objectives, respectively. Extensive simulations demonstrate, on the self-made simulation platform, Sophon achieves a 21.89%-66.52% decrease in the communication cost, and is more robust on different scales of the network topology and the request set than the baselines.

Original languageEnglish
Pages (from-to)1761-1776
Number of pages16
JournalIEEE/ACM Transactions on Networking
Volume34
DOIs
Publication statusPublished - 2026
Externally publishedYes

Keywords

  • Quantum networks
  • branch-and-bound
  • multi-agent reinforcement learning
  • multi-qubit entanglement

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