Probing transport in quantum many-fermion simulations via quantum loop topography

Yi Zhang, Carsten Bauer, Peter Broecker, Simon Trebst, Eun Ah Kim

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Quantum many-fermion systems give rise to diverse states of matter that often reveal themselves in distinctive transport properties. While some of these states can be captured by microscopic models accessible to numerical exact quantum Monte Carlo simulations, it nevertheless remains challenging to numerically access their transport properties. Here, we demonstrate that quantum loop topography (QLT) can be used to directly probe transport by machine learning current-current correlations in imaginary time. We showcase this approach by studying the emergence of superconducting fluctuations in the negative-U Hubbard model and a spin-fermion model for a metallic quantum critical point. For both sign-free models, we find that the QLT approach detects a change in transport in very good agreement with their established phase diagrams. These proof-of-principle calculations combined with the numerical efficiency of the QLT approach point a way to identify hitherto elusive transport phenomena such as non-Fermi liquids using machine learning algorithms.

Original languageEnglish
Article number161120
JournalPhysical Review B
Issue number16
StatePublished - 30 Apr 2019

Bibliographical note

Publisher Copyright:
© 2019 American Physical Society.


Dive into the research topics of 'Probing transport in quantum many-fermion simulations via quantum loop topography'. Together they form a unique fingerprint.

Cite this