A Deep-Learning Approach to the Dynamics of Landau–Zenner Transitions,Advanced Theory and Simulations

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A Deep-Learning Approach to the Dynamics of Landau–Zenner Transitions
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2021-05-07 , DOI: 10.1002/adts.202100083
Linliang Gao _{1,

2} , Kewei Sun ₁ , Huiru Zheng ₃ , Yang Zhao ₂

Affiliation

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

D_{2}

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

D_{2}

Ansatz with a multiplicity of ten, the error rate falls below 0.6%.

中文翻译：

朗道-齐纳跃迁动力学的深度学习方法

传统的高精度开放量子系统动力学方法通常是资源密集型的。如何提高目标系统的计算精度和效率是一个极其困难的挑战。在这项工作中，结合无监督和监督学习算法，引入了一种深度学习方法来模拟和预测 Landau-Zenner 动力学。从多个 Davydov 获得的数据