AI Assisted Discovery in Quantum Gas Microscope Images

Quantum gas microscopes for ultracold atoms in optical lattices have transformed quantum simulations of many-body Hamiltonians. Statistical analysis of atomic snapshots can produce expectation values for various charge and spin correlation functions and has led to new discoveries for the Fermi-Hubbard model in two dimensions. Here, we enlist the help of artificial intelligence to look for possible patterns in the snapshots not captured by conventional indicators. We try this unbiased approach on images taken in the non-Fermi liquid phase of the Hubbard model around optimal doping.