Learning-based Optimal Quantum Switch Scheduling-Reference-Cited by-同舟云学术

Learning-based Optimal Quantum Switch Scheduling

Published:2023-09-28 Issue:2 Volume:51 Page:75-77
ISSN:0163-5999
Container-title:ACM SIGMETRICS Performance Evaluation Review
language:en
Short-container-title:SIGMETRICS Perform. Eval. Rev.

Author:

Huang Jiatai¹,Huang Longbo¹

Affiliation:

1. Tsinghua University

Abstract

In this paper, we consider the problem of optimal scheduling for quantum switches with dynamic demand and random entanglement successes. Different from prior results that often focus on (known) fixed entanglement success probabilities, we assume zero prior knowledge about the entanglement success probabilities and allow them to vary from time to time in an adversarial manner. We propose a learning-based algorithm QSSoftMW based on the framework developed in [1], which combines adversarial learning and Lyapunov queue analysis. We show that QSSoftMW is able to automatically adapt to the changing system statistics and ensure quantum switch stability.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3626570.3626597

Reference6 articles.

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2. Resource Allocation and Cross-Layer Control in Wireless Networks

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4. V. V. P. Promponas and L. Tassiulas . Full exploitation of limited memory in quantum entanglement switching. arXiv preprint arXiv:2304.10602 , 2023 . V. V. P. Promponas and L. Tassiulas. Full exploitation of limited memory in quantum entanglement switching. arXiv preprint arXiv:2304.10602, 2023.

5. T. V. D. Towsley . Stability analysis of a quantum network with max-weight scheduling. In arXiv:2106.00831 , 2021 . T. V. D. Towsley. Stability analysis of a quantum network with max-weight scheduling. In arXiv:2106.00831, 2021.