Machine learning topological energy braiding of non-Bloch bands
This work addresses a gap in machine learning applications for non-Hermitian topological phases, offering incremental advances in a specialized physics domain.
The authors tackled the problem of identifying non-Bloch energy braiding in non-Hermitian systems using machine learning, achieving near-perfect accuracy in predicting braiding types like Unlink and Hopf link with unsupervised and supervised methods.
Machine learning has been used to identify phase transitions in a variety of physical systems. However, there is still a lack of relevant research on non-Bloch energy braiding in non-Hermitian systems. In this work, we study non-Bloch energy braiding in one-dimensional non-Hermitian systems using unsupervised and supervised methods. In unsupervised learning, we use diffusion maps to successfully identify non-Bloch energy braiding without any prior knowledge and combine it with k-means to cluster different topological elements into clusters, such as Unlink and Hopf link. In supervised learning, we train a Convolutional Neural Network (CNN) based on Bloch energy data to predict not only Bloch energy braiding but also non-Bloch energy braiding with an accuracy approaching 100%. By analysing the CNN, we can ascertain that the network has successfully acquired the ability to recognise the braiding topology of the energy bands. The present study demonstrates the considerable potential of machine learning in the identification of non-Hermitian topological phases and energy braiding.