Identifying quantum phase transitions using artificial neural networks on experimental data

Machine-learning techniques such as artificial neural networks are currently revolutionizing many technological areas and have also proven successful in quantum physics applications(1-4). Here, we employ an artificial neural network and deep-learning techniques to identify quantum phase transitions from single-shot experimental momentum-space density images of ultracold quantum gases and obtain results that were not feasible with conventional methods. We map out the complete two-dimensional topological phase diagram of the Haldane models(5-7) and provide an improved characterization of the superfluid-to-Mott-insulator transition in an inhomogeneous Bose-Hubbard systems(8-10). Our work points the way to unravel complex phase diagrams of general experimental systems, where the Hamiltonian and the order parameters might not be known.