A deep-learning approach to the dynamics of Landau-Zener transitions
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 d...
Saved in:
Main Authors: | Gao, Linliang, Sun, Kewei, Zheng, Huiru, Zhao, Yang |
---|---|
Other Authors: | School of Materials Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/150303 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Applications of neural networks to dynamics simulation of Landau-Zener transitions
by: Yang, Bianjiang, et al.
Published: (2022) -
Decoupled neural network training with re-computation and weight prediction
by: Peng, Jiawei, et al.
Published: (2023) -
A neural network approach to determining optimal inspection sampling size for CMM
by: Zhang, Y.F., et al.
Published: (2014) -
A multi-agent approach to fixture design
by: Subramaniam, V., et al.
Published: (2014) -
RGBD salient object detection via deep fusion
by: QU, Liangqiong, et al.
Published: (2017)