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Current quantum simulation experiments are starting to explorenon-equilibrium many-body dynamics in previously inaccessible regimes in termsof system sizes and time scales. Therefore, the question emerges whichobservables are best suited to study the dynamics in such quantum many-bodysystems. Using machine learning techniques, we investigate the dynamics and inparticular the thermalization behavior of an interacting quantum system whichundergoes a dynamical phase transition from an ergodic to a many-body localizedphase. A neural network is trained to distinguish non-equilibrium from thermalequilibrium data, and the network performance serves as a probe for thethermalization behavior of the system. We test our methods with experimentalsnapshots of ultracold atoms taken with a quantum gas microscope. Our resultsprovide a path to analyze highly-entangled large-scale quantum states forsystem sizes where numerical calculations of conventional observables becomechallenging.

Analyzing non-equilibrium quantum states through snapshots with artificial neural networks