Emulating ultrafast dissipative quantum dynamics with deep neural networks

TL;DR

This paper presents a deep neural network approach to efficiently emulate driven dissipative quantum dynamics, significantly reducing computation time while maintaining accuracy across various driving fields.

Contribution

The authors develop a novel feature space and train neural networks to emulate quantum dynamics, enabling rapid predictions for different driving fields and environments.

Findings

Neural networks can accurately reproduce quantum system responses.

The approach is many orders of magnitude faster than traditional simulations.

The method generalizes well to unseen pulse shapes.

Abstract

The simulation of driven dissipative quantum dynamics is often prohibitively computation-intensive, especially when it is calculated for various shapes of the driving field. We engineer a new feature space for representing the field and demonstrate that a deep neural network can be trained to emulate these dynamics by mapping this representation directly to the target observables. We demonstrate that with this approach, the system response can be retrieved many orders of magnitude faster. We verify the validity of our approach using the example of finite transverse Ising model irradiated with few-cycle magnetic pulses interacting with a Markovian environment. We show that our approach is sufficiently generalizable and robust to reproduce responses to pulses outside the training set.