Machine learning non-Hermitian topological phases

Abstract

Non-Hermitian topological phases have gained widespread interest due to their unconventional properties, which have no Hermitian counterparts. In this work, we propose to use machine learning to identify and predict non-Hermitian topological phases, based on their winding number. We consider two examples -- non-Hermitian Su-Schrieffer-Heeger model and its generalized version in one dimension and non-Hermitian nodal line semimetal in three dimensions -- to demonstrate the use of neural networks to accurately characterize the topological phases. We show that for the one dimensional model, a fully connected neural network gives an accuracy greater than 99.9\%, and is robust to the introduction of disorder. For the three dimensional model, we find that a convolutional neural network accurately predicts the different topological phases.

arXiv commentPublished version

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• Transfer learning from Hermitian to non-Hermitian quantum many-body physics
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• Machine Learning Study on the Flat-Band States Constructed by Molecular-Orbital Representation with Randomness