One-shot trajectory learning of open quantum systems dynamics

TL;DR

This paper introduces a one-shot trajectory learning method for open quantum systems that enables ultra-fast prediction of quantum dynamics, significantly reducing computational time and memory compared to traditional methods.

Contribution

The authors develop a novel one-shot learning approach for quantum trajectories that drastically accelerates predictions and decreases training resource requirements.

Findings

Predicts 10ps quantum trajectories in 70 milliseconds

Reduces training time and memory consumption

Effective on large quantum systems like FMO complex

Abstract

Nonadiabatic quantum dynamics are important for understanding light-harvesting processes, but their propagation with traditional methods can be rather expensive. Here we present a one-shot trajectory learning approach that allows to directly make ultra-fast prediction of the entire trajectory of the reduced density matrix for a new set of such simulation parameters as temperature and reorganization energy. The whole 10ps long propagation takes 70 milliseconds as we demonstrate on the comparatively large quantum system, the Fenna-Matthews-Olsen (FMO) complex. Our approach also significantly reduces time and memory requirements for training.