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001     874367
005     20210130004647.0
024 7 _ |2 Handle
|a 2128/24494
037 _ _ |a FZJ-2020-01390
100 1 _ |0 P:(DE-HGF)0
|a Bauer, Carsten
|b 0
111 2 _ |a NIC Symposium 2020
|c Jülich
|d 2020-02-27 - 2020-02-28
|w Germany
245 _ _ |a Machine Learning Transport Properties in Quantum Many-Fermion Simulations
260 _ _ |a Jülich
|b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
|c 2020
295 1 0 |a NIC Symposium 2020
300 _ _ |a 85 - 92
336 7 _ |2 ORCID
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336 7 _ |0 33
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490 0 _ |a Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series
|v 50
520 _ _ |a 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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700 1 _ |0 P:(DE-HGF)0
|a Trebst, Simon
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787 0 _ |0 FZJ-2020-01353
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856 4 _ |u https://juser.fz-juelich.de/record/874367/files/NIC_2020_Trebst.pdf
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|a University of Cologne
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|a University of Cologne
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