2020/09/02 by Markus Schmitt, Markus Heyl · 2 citations
Physics and Astronomy · #Quantum many-body systems #Quantum, superfluid, helium dynamics #Cold Atom Physics and Bose-Einstein Condensates
paper · pdf · doi:10.1103/physrevlett.125.100503
openalex publication_date 2020/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum matter constitutes a key challenge for current computational methods. This holds in particular in the regime of two spatial dimensions, whose experimental exploration is currently pursued with strong efforts in quantum simulators. In this work we present a versatile and efficient machine learning inspired approach based on a recently introduced artificial neural network encoding of quantum many-body wave functions. We identify and resolve key challenges for the simulation of time evolution, which previously imposed significant limitations on the accurate description of large systems and long-time dynamics. As a concrete example, we study the dynamics of the paradigmatic two-dimensional transverse-field Ising model, as recently also realized experimentally in systems of Rydberg atoms. Calculating the nonequilibrium real-time evolution across a broad range of parameters, we, for instance, observe collapse and revival oscillations of ferromagnetic order and demonstrate that the reached timescales are comparable to or exceed the capabilities of state-of-the-art tensor network methods.