Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136768
Title: Applications of neural networks to the simulation of dynamics of open quantum systems
Authors: Bandyopadhyay, Sayantan
Huang, Zhongkai
Sun, Kewei
Zhao, Yang
Keywords: Engineering::Materials
Issue Date: 2018
Source: Bandyopadhyay, S., Huang, Z., Sun, K., & Zhao, Y. (2018). Applications of neural networks to the simulation of dynamics of open quantum systems. Chemical Physics, 515, 272-278. doi:10.1016/j.chemphys.2018.05.019
Journal: Chemical Physics 
Abstract: Despite neural networks’ success, their applications to open-system dynamics are few. In this work, non-linear autoregressive neural networks are adopted to generalize time series of expectation values of observables of interest in open quantum systems. Using Dirac-Frenkel time-dependent variation with the multiple Davydov D2 Ansatz, we obtain first stages of dynamical states of both the spin-boson model and the dissipative Landau-Zener model. With calculated data, careful training of the non-linear neural networks is performed. It is shown that the training quality of the networks is sufficient to ensure a least mean square error of 1×10-11. Subsequently, the network is cross validated by testing with additional data. Successes of the network training demonstrate that initial data of simulated open-system dynamics contain sufficient knowledge regarding its future propagation. We use the first-stage information and the trained network to predict future values of target observables in the series, and succeed with considerable accuracy.
URI: https://hdl.handle.net/10356/136768
ISSN: 0301-0104
DOI: 10.1016/j.chemphys.2018.05.019
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Chemical Physics and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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