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

Sharareh Sayyad, Jose Lado

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
191 Downloads (Pure)

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.
Original languageEnglish
Article number185603
Pages (from-to)1-8
Number of pages8
JournalJournal of physics: Condensed matter
Volume36
Issue number18
DOIs
Publication statusPublished - 7 Feb 2024
MoE publication typeA1 Journal article-refereed

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