Optical lattice experiments at unobserved conditions and scales through generative adversarial deep learning

Understand

Machine learning provides a novel avenue for the study of experimental realizations of many-body systems, and has recently been proven successful in analyzing properties of experimental data of ultracold quantum gases.

• We here show that deep learning succeeds in the more challenging task of modelling such an experimental data distribution.
• Our generative model (RUGAN) is able to produce snapshots of a doped two-dimensional Fermi-Hubbard model that are indistinguishable from previously reported experimental realizations.
• Importantly, it is capable of accurately generating snapshots at conditions for which it did not observe any experimental data, such as at higher doping values.