000874367 001__ 874367
000874367 005__ 20210130004647.0
000874367 0247_ $$2Handle$$a2128/24494
000874367 037__ $$aFZJ-2020-01390
000874367 1001_ $$0P:(DE-HGF)0$$aBauer, Carsten$$b0
000874367 1112_ $$aNIC Symposium 2020$$cJülich$$d2020-02-27 - 2020-02-28$$wGermany
000874367 245__ $$aMachine Learning Transport Properties in Quantum Many-Fermion Simulations
000874367 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2020
000874367 29510 $$aNIC Symposium 2020
000874367 300__ $$a85 - 92
000874367 3367_ $$2ORCID$$aCONFERENCE_PAPER
000874367 3367_ $$033$$2EndNote$$aConference Paper
000874367 3367_ $$2BibTeX$$aINPROCEEDINGS
000874367 3367_ $$2DRIVER$$aconferenceObject
000874367 3367_ $$2DataCite$$aOutput Types/Conference Paper
000874367 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1583419119_14363
000874367 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000874367 4900_ $$aPublication Series of the John von Neumann Institute for Computing (NIC) NIC Series$$v50
000874367 520__ $$aIn 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.
000874367 536__ $$0G:(DE-HGF)POF3-899$$a899 - ohne Topic (POF3-899)$$cPOF3-899$$fPOF III$$x0
000874367 7001_ $$0P:(DE-HGF)0$$aTrebst, Simon$$b1
000874367 7870_ $$0FZJ-2020-01353$$iIsPartOf
000874367 8564_ $$uhttps://juser.fz-juelich.de/record/874367/files/NIC_2020_Trebst.pdf$$yOpenAccess
000874367 8564_ $$uhttps://juser.fz-juelich.de/record/874367/files/NIC_2020_Trebst.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000874367 909CO $$ooai:juser.fz-juelich.de:874367$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000874367 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000874367 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000874367 9141_ $$y2020
000874367 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aUniversity of Cologne$$b0
000874367 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aUniversity of Cologne$$b1
000874367 9131_ $$0G:(DE-HGF)POF3-899$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vohne Topic$$x0
000874367 920__ $$lyes
000874367 9201_ $$0I:(DE-Juel1)NIC-20090406$$kNIC$$lJohn von Neumann - Institut für Computing$$x0
000874367 980__ $$acontrib
000874367 980__ $$aVDB
000874367 980__ $$aUNRESTRICTED
000874367 980__ $$acontb
000874367 980__ $$aI:(DE-Juel1)NIC-20090406
000874367 9801_ $$aFullTexts