Journal
PHYSICAL REVIEW A
Volume 102, Issue 3, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.102.033326
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Funding
- National Science Foundation (NSF) [DMR-1609560]
- NSF [OAC-1626645, DMR1607277]
- U.S. Department of Energy, Office of Science [DE-SC0014671]
- David and Lucile Packard Foundation [2016-65128]
- AFOSR Young Investigator Research Program [FA9550-16-1-0269]
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Shared Hierarchical Academic Research Computing Network (SHARCNET)
- Compute Canada
- Google Quantum Research Award
- Canada CIFAR AI chair program
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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- and 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.
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