@Article{CiCP-30-1453,
author = {Li , JingshiChen , SongCao , Yanzhao and Sun , Zhao},
title = {A Neural Network Approach to Sampling Based Learning Control for Quantum System with Uncertainty},
journal = {Communications in Computational Physics},
year = {2021},
volume = {30},
number = {5},
pages = {1453--1473},
abstract = {<p style="text-align: justify;">Robust control design for quantum systems with uncertainty is a key task
for developing practical quantum technology. In this paper, we apply neural networks
to learn the control of a quantum system with uncertainty. By exploiting the auto differentiation function developed for neural network models, our method avoids the
manual computation of the gradient of the cost function as required in traditional
methods. We implement our method using two algorithms. One uses neural networks to learn both the states and the controls and one uses neural networks to learn
only the controls but solve the states by finite difference methods. Both algorithms incorporate the sampling-based learning process into the training of the networks. The
performance of the algorithms is evaluated on a practical numerical example, followed
by a detailed discussion about the advantage and trade-offs between our method and
the other numerical schemes.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2020-0182},
url = {http://global-sci.org/intro/article_detail/cicp/19936.html}
}