TY - JOUR
T1 - A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty
AU - Li , Jingshi
AU - Chen , Song
AU - Wang , Lijin
AU - Cao , Yanzhao
JO - Communications in Computational Physics
VL - 5
SP - 1525
EP - 1545
PY - 2022
DA - 2022/05
SN - 31
DO - http://doi.org/10.4208/cicp.OA-2021-0219
UR - https://global-sci.org/intro/article_detail/cicp/20513.html
KW - Quantum (noise) control, neural network, symplectic methods, norm-preservation.
AB - <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>