TY - JOUR
T1 - Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence
AU - Khatami, Ehsan
AU - Guardado-Sanchez, Elmer
AU - Spar, Benjamin M.
AU - Carrasquilla, Juan Felipe
AU - Bakr, Waseem S.
AU - Scalettar, Richard T.
N1 - Funding Information:
We thank Christie Chiu, Annabelle Bohrdt, and Neil Switz for useful discussions. E.K. acknowledges support from the National Science Foundation (NSF) under Grant No. DMR-1609560. Computations were performed in part on the Spartan high-performance computer at San José State University, which is supported by the NSF under Grant No. OAC-1626645. The work of R.T.S. was supported by Grant No. DE-SC0014671 funded by the U.S. Department of Energy, Office of Science. The work of E.G.-S., B.M.S., and W.S.B. was supported by the NSF under Grant No. DMR-1607277, the David and Lucile Packard Foundation under Grant No. 2016-65128, and the AFOSR Young Investigator Research Program under Grant No. FA9550-16-1-0269. J.C. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Shared Hierarchical Academic Research Computing Network (SHARCNET), Compute Canada, Google Quantum Research Award, and the Canada CIFAR AI chair program.
Publisher Copyright:
© 2020 American Physical Society.
PY - 2020/9
Y1 - 2020/9
N2 - Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- A nd short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.
AB - Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- A nd short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.
UR - http://www.scopus.com/inward/record.url?scp=85092597711&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092597711&partnerID=8YFLogxK
U2 - 10.1103/PhysRevA.102.033326
DO - 10.1103/PhysRevA.102.033326
M3 - Article
AN - SCOPUS:85092597711
SN - 2469-9926
VL - 102
JO - Physical Review A
JF - Physical Review A
IS - 3
M1 - 033326
ER -