Conditional Seq2Seq model for the time-dependent two-level system

TL;DR

This paper introduces a deep learning approach using a conditional Seq2Seq model to solve the time-dependent Schrödinger equation for two-level quantum systems, achieving high accuracy and potential for higher-dimensional problems.

Contribution

The paper presents a novel neural network architecture tailored for quantum two-level systems, demonstrating improved efficiency and accuracy over traditional methods.

Findings

Achieved over 90% accuracy in long-term predictions for random electric fields.

The method outperforms traditional approaches in efficiency for solving time-dependent Schrödinger equations.

Potential to extend the approach to higher-dimensional quantum systems.

Abstract

We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the network structure and tuning parameters, above 90 percent accuracy in super long-term predictions can be achieved in the case of random electric fields, which indicates a promising new method to solve the time-dependent equation for two-level systems. By slightly modifying this network, we think that this method can solve the two- or three-dimensional time-dependent Schrodinger equation more efficiently than traditional approaches.