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

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.

4+2 pages, 4+4 figures

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Cited by in corpus (5)

• Non-Hermitian Mott Skin Effect
• Entanglement Hamiltonian and effective temperature of non-Hermitian quantum spin ladders
• Topological phases of many-body non-Hermitian systems
• Non-Hermitian chiral anomalies in interacting systems
• Transfer learning of many-body electronic correlation entropy from local measurements