Statistical and machine learning approaches for prediction of long-time excitation energy transfer dynamics

TL;DR

This paper compares statistical and machine learning models to predict long-time excitation energy transfer dynamics in quantum systems, aiming to reduce computational costs compared to traditional methods.

Contribution

It demonstrates that models like SARIMA can accurately predict long-time dynamics using only short-time data, offering a computationally efficient alternative to HEOM.

Findings

SARIMA accurately predicts long-time dynamics

Machine learning models reduce computational resources

Models outperform traditional methods in efficiency

Abstract

One of the approaches used to solve for the dynamics of open quantum systems is the hierarchical equations of motion (HEOM). Although it is numerically exact, this method requires immense computational resources to solve. The objective here is to demonstrate whether models such as SARIMA, CatBoost, Prophet, convolutional and recurrent neural networks are able to bypass this requirement. We are able to show this successfully by first solving the HEOM to generate a data set of time series that depict the dissipative dynamics of excitation energy transfer in photosynthetic systems then, we use this data to test the models ability to predict the long-time dynamics when only the initial short-time dynamics is given. Our results suggest that the SARIMA model can serve as a computationally inexpensive yet accurate way to predict long-time dynamics.