Analyzing Nonequilibrium Quantum States through Snapshots with Artificial Neural Networks

Current quantum simulation experiments are starting to explore nonequilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and timescales. Therefore, the question emerges as to which observables are best suited to study the dynamics in such quantum many-body systems. Using machine learning techniques, we investigate the dynamics and, in particular, the thermalization behavior of an interacting quantum system that undergoes a nonequilibrium phase transition from an ergodic to a many-body localized phase. We employ supervised and unsupervised training methods to distinguish nonequilibrium from equilibrium data, using the network performance as a probe for the thermalization behavior of the system. We test our methods with experimental snapshots of ultracold atoms taken with a quantum gas microscope. Our results provide a path to analyze highly entangled large-scale quantum states for system sizes where numerical calculations of conventional observables become challenging.

Automated summary

Current quantum simulation experiments are exploring nonequilibrium many-body dynamics with previously inaccessible regimes, using machine learning techniques to study thermalization behavior. By employing supervised and unsupervised training methods, researchers distinguish non-equilibrium from equilibrium data and use network performance as a probe for system thermalization behavior.