Startseite Machine learning studies for the effects of probes and cavity on quantum synchronization
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Machine learning studies for the effects of probes and cavity on quantum synchronization

  • Qing-Yu Meng , Yong Hu , Qing Yang , Qin-Sheng Zhu und Xiao-Yu Li
Veröffentlicht/Copyright: 26. Februar 2021

Abstract

As an important technology of the quantum detection, the quantum synchronization detection is always used in the detection or measurement of some quantum systems. A probing model is established to describe the probing of a qubit system in the cavity field and to reveal the effect of the environment (cavity) on the quantum synchronization occurrence, as well as the interactions among environment, a qubit system, and probing equipment. By adjusting the frequency of the probe, the in-phase, anti-phase, and out-of-phase synchronization can be achieved. Simultaneously, the effect of γ3 which describes the interaction strength between the probe and environments for quantum synchronization is discussed under different Ohmic dissipation index s. Finally, the machine learning method is applied to present an optimization for classification and regression of synchronization transition dependent on s and γ3.


Corresponding author: Qin-Sheng Zhu, School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China, E-mail:

  1. Author contributions: Qingyu Meng has made substantial contributions to the conception or design of the work or the acquisition, analysis, or interpretation of data for the work; and Qingyu Meng has drafted the work or revised it critically for important intellectual content and has approved the final version to be published; and Qingyu Meng has agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Yong Hu and Qing Yang calculated part of the data and prepared Figure 3. Qinsheng Zhu and Xiaoyu Li played an important role in the revision of the article. All authors have reviewed the manuscript and provided editing and writing assistance.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-10-21
Accepted: 2021-01-31
Published Online: 2021-02-26
Published in Print: 2021-05-26

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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