@Article{CiCP-31-5,
author = {Li, Jingshi and Song, Chen and Wang, Lijin and Yanzhao, Cao},
title = {A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty},
journal = {Communications in Computational Physics},
year = {2022},
volume = {31},
number = {5},
pages = {1525--1545},
abstract = {<p style="text-align: justify;">Robust quantum control with uncertainty plays a crucial role in practical
quantum technologies. This paper presents a method for solving a quantum control
problem by combining neural network and symplectic finite difference methods. The
neural network approach provides a framework that is easy to establish and train. At
the same time, the symplectic methods possess the norm-preserving property for the
quantum system to produce a realistic solution in physics. We construct a general
high dimensional quantum optimal control problem to evaluate the proposed method
and an approach that combines a neural network with forward Euler’s method. Our
analysis and numerical experiments confirm that the neural network-based symplectic
method achieves significantly better accuracy and robustness against noises.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2021-0219},
url = {https://global-sci.com/article/79555/a-symplectic-based-neural-network-algorithm-for-quantum-controls-under-uncertainty}
}