Journal
NATURE PHYSICS
Volume 15, Issue 9, Pages 921-924Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41567-019-0565-x
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Funding
- Harvard-MIT CUA
- NSF [DMR-1308435, PHY-1506203, PHY-1734011]
- AFOSR-MURI Quantum Phases of Matter [FA9550-14-1-0035]
- AFOSR [FA9550-16-10323]
- DoD NDSEG
- Gordon and Betty Moore Foundation EPIQS programme
- NSF GRFP
- ONR [N00014-18-1-2863]
- SNSF
- Studienstiftung des deutschen Volkes
- Technical University of Munich - Institute for Advanced Study - German Excellence Initiative
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC-2111-390814868]
- DFG [KN1254/1-1, TRR80]
- European Union [291763]
- Gordon and Betty Moore Foundation [6791]
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Quantum gas microscopes for ultracold atoms can provide high-resolution real-space snapshots of complex many-body systems. We implement machine learning to analyse and classify such snapshots of ultracold atoms. Specifically, we compare the data from an experimental realization of the two-dimensional Fermi-Hubbard model to two theoretical approaches: a doped quantum spin liquid state of resonating valence bond type(1,2), and the geometric string theory(3,4), describing a state with hidden spin order. This technique considers all available information without a potential bias towards one particular theory by the choice of an observable and can therefore select the theory that is more predictive in general. Up to intermediate doping values, our algorithm tends to classify experimental snapshots as geometric-string-like, as compared to the doped spin liquid. Our results demonstrate the potential for machine learning in processing the wealth of data obtained through quantum gas microscopy for new physical insights.
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