Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150303
Title: A deep-learning approach to the dynamics of Landau-Zener transitions
Authors: Gao, Linliang
Sun, Kewei
Zheng, Huiru
Zhao, Yang
Keywords: Engineering::Materials
Issue Date: 2021
Source: Gao, L., Sun, K., Zheng, H. & Zhao, Y. (2021). A deep-learning approach to the dynamics of Landau-Zener transitions. Advanced Theory and Simulations, 4(7), 2100083-. https://dx.doi.org/10.1002/adts.202100083
Project: 2018-T1-002-175
2020-T1-002- 075
Journal: Advanced Theory and Simulations
Abstract: Traditional approaches to the dynamics of the open quantum systems with high precision are often resource intensive. How to improve computation accuracy and efficiency for target systems is an extremely difficult challenge. In this work, combining unsupervised and supervised learning algorithms, a deep-learning approach is introduced to simulate and predict Landau–Zenner dynamics. Data obtained from multiple Davydov (Formula presented.) Ansatz with a low multiplicity of four are used for training, while the data from the trial state with a high multiplicity of ten are adopted as target data to assess the accuracy of prediction. After proper training, our method can successfully predict and simulate Landau–Zenner dynamics using only random noise and two adjustable model parameters. Compared to the high-precision dynamics data from multiple Davydov (Formula presented.) Ansatz with a multiplicity of ten, the error rate falls below 0.6%.
URI: https://hdl.handle.net/10356/150303
ISSN: 2513-0390
DOI: 10.1002/adts.202100083
Rights: This is the peer reviewed version of the following article: Gao, L., Sun, K., Zheng, H. & Zhao, Y. (2021). A deep-learning approach to the dynamics of Landau-Zener transitions. Advanced Theory and Simulations, 4(7), 2100083-, which has been published in final form at https://dx.doi.org/10.1002/adts.202100083. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Fulltext Permission: embargo_20220807
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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