Machine learning topological invariants of non-Hermitian systems

The study of topological properties by machine learning approaches has attracted considerable interest recently. Here we propose machine learning the topological invariants that are unique in non-Hermitian systems. Specifically, we train neural networks to predict the winding of eigenvalues of three different non-Hermitian Hamiltonians on the complex energy plane with nearly $100\%$ accuracy. Our demonstrations on the Hatano-Nelson model, the non-Hermitian Su-Schrieffer-Heeger model and generalized Aubry-Andre-Harper model show the capability of the neural networks in exploring topological invariants and the associated topological phase transitions and topological phase diagrams in non-Hermitian systems. Moreover, the neural networks trained by a small data set in the phase diagram can successfully predict topological invariants in untouched phase regions. Thus, our work pave a way to reveal non-Hermitian topology with the machine learning toolbox.

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