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

Journal Article · · Physical Review. B
 [1];  [2];  [2];  [2];  [1]
  1. Cornell University, Ithaca, NY (United States)
  2. University of Cologne (Germany)

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. Furthermore, 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.

Research Organization:
Cornell Univ., Ithaca, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division
Grant/Contract Number:
SC0018946
OSTI ID:
2322518
Alternate ID(s):
OSTI ID: 1509924; OSTI ID: 1613035
Journal Information:
Physical Review. B, Vol. 99, Issue 16; ISSN 2469-9950
Publisher:
American Physical Society (APS)Copyright Statement
Country of Publication:
United States
Language:
English

References (43)

Quantum critical phenomena journal August 1976
Quantum Entanglement in Neural Network States journal May 2017
Spin liquids in frustrated magnets journal March 2010
Sign-Problem-Free Quantum Monte Carlo of the Onset of Antiferromagnetism in Metals journal December 2012
Neural-network quantum state tomography journal February 2018
Quantum critical properties of a metallic spin-density-wave transition journal January 2017
Monte Carlo Studies of Quantum Critical Metals journal March 2019
What makes the Tc of monolayer FeSe on SrTiO3 so high: a sign-problem-free quantum Monte Carlo study journal June 2016
Sign problem in the numerical simulation of many-electron systems journal May 1990
Machine learning of quantum phase transitions journal March 2019
Helicity modulus in the two-dimensional Hubbard model journal March 1993
Machine learning quantum phases of matter beyond the fermion sign problem journal August 2017
Two-dimensional negative- U Hubbard model journal February 1991
Efficient representation of quantum many-body states with deep neural networks journal September 2017
Deep Learning the Quantum Phase Transitions in Random Electron Systems: Applications to Three Dimensions journal April 2017
Machine learning Z 2 quantum spin liquids with quasiparticle statistics journal December 2017
Machine learning vortices at the Kosterlitz-Thouless transition journal January 2018
Quantum Liquids: Bose condensation and Cooper pairing in condensed-matter systems book September 2006
Machine Learning Phases of Strongly Correlated Fermions journal August 2017
Supervised learning approach for recognizing magnetic skyrmion phases journal November 2018
Stable monte carlo algorithm for fermion lattice systems at low temperatures journal December 1988
Neural-Network Quantum States, String-Bond States, and Chiral Topological States journal January 2018
Probing many-body localization with neural networks journal June 2017
Supervised learning magnetic skyrmion phases text January 2018
Machine learning of frustrated classical spin models. I. Principal component analysis journal October 2017
Bose–Einstein Condensation in Dilute Gases book January 2008
Discovering phase transitions with unsupervised learning journal November 2016
Ordering, metastability and phase transitions in two-dimensional systems journal April 1973
Machine learning topological states journal November 2017
Deep Learning the Quantum Phase Transitions in Random Two-Dimensional Electron Systems journal December 2016
Competing Orders in a Nearly Antiferromagnetic Metal journal August 2016
Insulator, metal, or superconductor: The criteria journal April 1993
Quantum phase recognition via unsupervised machine learning preprint January 2017
Superfluid density and the Drude weight of the Hubbard model journal May 1992
Quantum Loop Topography for Machine Learning journal May 2017
Effect of a nonzero temperature on quantum critical points in itinerant fermion systems journal September 1993
Phase diagram of the two-dimensional negative- U Hubbard model journal March 1989
Learning phase transitions by confusion journal February 2017
Introduction to quantum Monte Carlo simulations for fermionic systems journal January 2003
Solving the quantum many-body problem with artificial neural networks journal February 2017
Machine learning phases of matter journal February 2017
Neural-network quantum state tomography journal February 2018
Superconductivity and non-Fermi liquid behavior near a nematic quantum critical point journal April 2017

Figures / Tables (5)