Transfer learning from Hermitian to non-Hermitian quantum many-body physics

TL;DR

This paper demonstrates that machine learning models trained on Hermitian quantum systems can be effectively transferred to identify phase boundaries in non-Hermitian quantum many-body models, simplifying analysis of complex non-Hermitian systems.

Contribution

The study introduces a transfer learning approach that applies Hermitian-trained machine learning models to non-Hermitian systems without additional training, enabling efficient phase boundary detection.

Findings

Hermitian-trained models accurately identify non-Hermitian phase boundaries

Transfer learning reduces the need for extensive non-Hermitian data

Method simplifies analysis of non-Hermitian quantum systems

Abstract

Identifying phase boundaries of interacting systems is one of the key steps to understanding quantum many-body models. The development of various numerical and analytical methods has allowed exploring the phase diagrams of many Hermitian interacting systems. However, numerical challenges and scarcity of analytical solutions hinder obtaining phase boundaries in non-Hermitian many-body models. Recent machine learning methods have emerged as a potential strategy to learn phase boundaries from various observables without having access to the full many-body wavefunction. Here, we show that a machine learning methodology trained solely on Hermitian correlation functions allows identifying phase boundaries of non-Hermitian interacting models. These results demonstrate that Hermitian machine learning algorithms can be redeployed to non-Hermitian models without requiring further training to reveal non-Hermitian phase diagrams. Our findings establish transfer learning as a versatile strategy to leverage Hermitian physics to machine learning non-Hermitian phenomena.