Freiburg 2019 – scientific programme

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FM: Fall Meeting

FM 79: Entanglement: Neural Networks for Many-Body Dynamics

FM 79.2: Talk

Thursday, September 26, 2019, 14:15–14:30, 2004

Quenches near Ising quantum criticality as a challenge for artificial neural networks — Martin Gärttner, •Stefanie Czischek, and Thomas Gasenzer — Kirchhoff-Institut für Physik, Heidelberg

The near-critical unitary dynamics of quantum Ising spin chains in transversal and longitudinal magnetic fields is studied using an artificial neural network representation of the wave function. A focus is set on strong spatial correlations which build up in the system following a quench into the vicinity of the quantum critical point. We compare correlations obtained by optimizing the parameters of the network states with analytical solutions in integrable cases and time-dependent density matrix renormalization group (tDMRG) simulations. The neural-network representation is shown to yield precise results in a wide parameter regime. However, for quenches close to the quantum critical point the representation becomes inefficient. For nonintegrable models we show that in regimes where tDMRG is limited to short times due to extensive entanglement growth, also the neural-network parametrization converges only at short times.