A comparative study of different machine learning methods for dissipative quantum dynamics
ORAL
Abstract
It has been recently shown that supervised machine learning (ML) algorithms can accurately and efficiently predict long-time population dynamics of dissipative quantum systems given only short-time population dynamics. In this work, we benchmarked 22 ML models on their ability to predict long-time dynamics of a two-level quantum system linearly coupled to harmonic bath. The models include uni- and bidirectional recurrent, convolutional, and fully-connected feedforward artificial neural networks (ANNs) and kernel ridge regression (KRR) with linear and most commonly used nonlinear kernels. Our results suggest that KRR with nonlinear kernels can serve as inexpensive yet accurate way to simulate long-time dynamics in cases where the constant length of input trajectories is appropriate. Convolutional gated recurrent unit model is found to be the most efficient ANN model.
*A A K acknowledges the Ralph E Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities. This work was also supported by the startup funds of the College of Arts and Sciences and the Department of Physics and Astronomy of the University of Delaware. P O D acknowledges funding by the National Natural Science Foundation of China (NSFC, No. 22003051), the Fundamental Research Funds for the Central Universities (No. 20720210092) and via the Lab project of the State Key Laboratory of Physical Chemistry of Solid Surfaces. K J R E acknowledges support by the Beyond Research Program between University of Delaware and Universidad Nacional de Colombia. Calculations were performed with high-performance computing resources provided by the University of Delaware and Xiamen University.
