Classifying Snapshots of the Doped Hubbard Model with Machine Learning

Quantum gas microscopes for ultracold atoms can provide high-resolution real-space snapshots of complex many-body systems. We implement machine learning to analyze and classify such snapshots of ultracold atoms, which realize the Fermi-Hubbard model on a square lattice. At half-filling, we find that machine learning successfully identifies a crossover in the character of magnetic correlations with increasing temperature, in concurrence with the peak of the uniform spin susceptibility. We then extend the approach to assess two theoretical descriptions of doped antiferromagnets: a doped quantum spin liquid and a geometric string theory describing hidden spin order. Up to intermediate doping values, our algorithm tends to classify experimental snapshots as geometric-string-like, as compared to the doped spin liquid or to experimental images at high temperatures. Our results demonstrate the potential for machine learning in processing the wealth of data obtained through quantum gas microscopy for new physical insights.