  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.
