Contribution to a conference proceedings/Contribution to a book

FZJ-2020-01390

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine Learning Transport Properties in Quantum Many-Fermion Simulations

Bauer, C. ; Trebst, S.

2020
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich

Please use a persistent id in citations: http://hdl.handle.net/2128/24494

Abstract: In computational condensed matter physics, the influx of algorithms from machine learning and their combination with traditional numerical many-body approaches is one of the most enticing recent developments. At this confluence novel techniques have been developed that allow to characterise many-body wave functions and discriminate quantum phase of matter by adapting concepts from computer science and statistics, which have proved tremendously practical in completely different contexts. However, in order to actually turn into a productive and widely accepted tool for obtaining a deeper understanding of microscopic physics these novel approaches must allow for meaningful, comprehensible inference and go beyond the applicability of their traditional counterparts. In this contribution, we report on significant progress made in this direction by discussing a novel algorithmic scheme using machine learning techniques to numerically infer the transport properties of quantum many-fermion systems. This approach is based on a quantum loop topography (QLT), and capable of distinguishing conventional metallic and superconducting transport in quantum Monte Carlo simulations by learning current-current correlations from equal-time Green’s functions. We showcase this approach by studying the emergence of s- and d-wave superconducting fluctuations in the negative-U Hubbard model and a spin-fermion model for a metallic quantum critical point. The presented results, 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.

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Contributing Institute(s):

1. John von Neumann - Institut für Computing (NIC)

Research Program(s):

1. 899 - ohne Topic (POF3-899) (POF3-899)

Appears in the scientific report 2020

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Book/Proceedings Müller, M. (Editor)Extern* ; Binder, K. (Editor) ; Trautmann, A. (Editor)FZJ*
NIC Symposium 2020: proceedings
2020NIC Symposium, JülichJülich, Germany, 27 Feb 2020 - 28 Feb 20202020-02-272020-02-28 Jülich : Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag, NIC Series 50, v, 424 S. (2020)2020   Files  Fulltext by OpenAccess repository BibTeX | EndNote: XML, Text | RIS

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