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
some 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 time scales are comparable to or exceed the
capabilities of state-of-the-art tensor network methods.

该研究讨论了利用人工神经网络编码量子多体波函数的方法，有效地模拟了二维空间中的量子物质的无平衡实时演化，并应用到横向场伊辛模型上，验证了该方法的准确性和可行性。