Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra

TL;DR

This paper introduces a neural network-based quantum propagator that efficiently simulates non-Markovian quantum dynamics and spectra of complex systems, surpassing traditional iterative methods.

Contribution

The authors develop a neural quantum propagator model that enables long-time evolution of quantum states without iterative procedures, improving simulation efficiency for open quantum systems.

Findings

Accurately simulates population dynamics of the Fenna-Matthews-Olson complex.

Reproduces linear and two-dimensional spectra effectively.

Avoids time-consuming iterative calculations in quantum dynamics simulations.

Abstract

The accurate solution of dissipative quantum dynamics plays an important role on the simulation of open quantum systems. Here we propose a machine-learning-based universal solver for the hierarchical equations of motion, one of the most widely used approaches which takes into account non-markovian effects and nonperturbative system-environment interactions in a numerically exact manner. We develop a neural quantum propagator model by utilizing the neural network architecture, which avoids time-consuming iterations and can be used to evolve any initial quantum state for arbitrarily long times. To demonstrate the efficacy of our model, we apply it to the simulation of population dynamics and linear and two-dimensional spectra of the Fenna-Matthews-Olson complex.