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FM: Fall Meeting

FM 10: Topology: Artificial Systems

FM 10.4: Talk

Montag, 23. September 2019, 15:00–15:15, 1199

Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data — Benno Rem^1,2, •Niklas Käming¹, Matthias Tarnowski^1,2, Luca Asteria¹, Nick Fläschner¹, Christoph Becker^1,3, Klaus Sengstock^1,2,3, and Christof Weitenberg^1,2 — ¹ILP Institut für Laserphysik, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany — ²The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany — ³ZOQ Zentrum für Optische Quantentechnologien, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany

Machine learning techniques such as artificial neural networks are currently revolutionizing many technological areas and have also proven successful in quantum physics applications. 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, which were not feasible with conventional methods. We map out the complete two-dimensional topological phase diagram of the Haldane model and provide an accurate characterization of the superfluid-to-Mott-insulator transition in an inhomogeneous Bose-Hubbard system. 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.