A Deep‐Learning Approach to the Dynamics of Landau–Zenner Transitions

Linliang Gao, Kewei Sun, Huiru Zheng, Yang Zhao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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%.

Original languageEnglish
Article number2100083
Pages (from-to)1-14
Number of pages14
JournalAdvanced Theory and Simulations
Volume4
Issue number7
Early online date7 May 2021
DOIs
Publication statusPublished - 14 Jul 2021

Keywords

  • Landau–Zener transitions
  • back propagation neural networks
  • convolutional neural networks
  • generative adversarial networks

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