Please use this identifier to cite or link to this item:
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.
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

Files in This Item:
File Description SizeFormat 
Applications of neural networks to the simulation of dynamics of open quantum systems.pdf1.96 MBAdobe PDFView/Open

Citations 20

Updated on Jan 22, 2023

Web of ScienceTM
Citations 20

Updated on Jan 24, 2023

Page view(s)

Updated on Jan 28, 2023

Download(s) 50

Updated on Jan 28, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.