Predicting rate kernels via dynamic mode decomposition

TL;DR

This paper demonstrates that dynamic mode decomposition (DMD) can efficiently predict long-term behaviors of open quantum systems' rate kernels using limited data, reducing computational costs compared to traditional methods.

Contribution

The study introduces the application of DMD for evaluating quantum rate kernels, enabling accurate long-term predictions with fewer samples and lower computational costs.

Findings

DMD accurately predicts quantum rate kernels.

DMD reduces computational costs significantly.

DMD works with or without external fields.

Abstract

Simulating dynamics of open quantum systems is sometimes a significant challenge, despite the availability of various exact or approximate methods. Particularly when dealing with complex systems, the huge computational cost will largely limit the applicability of these methods. We investigate the usage of dynamic mode decomposition (DMD) to evaluate the rate kernels in quantum rate processes. DMD is a data-driven model reduction technique that characterizes the rate kernels using snapshots collected from a small time window, allowing us to predict the long-term behaviors with only a limited number of samples. Our investigations show that whether the external field is involved or not, the DMD can give accurate prediction of the result compared with the traditional propagations, and simultaneously reduce the required computational cost.