Predicting Quantum Many-Body Dynamics with Transferable Neural Networks**Supported by the National Natural Science Foundation of China under Grant Nos 11874431 and 11804181, the National Key R&D Program of China under Grant No 2018YFA0306800, and the Guangdong Science and Technology Innovation Youth Talent Program under Grant Nos 2016TQ03X688 and 2018YFA0306504, and the Research Fund Program of the State Key Laboratory of

Abstract

Advanced machine learning (ML) approaches such as transfer learning have seldom been applied to approximate quantum many-body systems. Here we demonstrate that a simple recurrent unit (SRU) based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional (1D) Ising model with simultaneous transverse and parallel magnetic fields, as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions. At a cost of constant computational complexity, a larger many-body state evolution is predicted in an autoregressive way from just one initial state, without any guidance or knowledge of any Hamiltonian. Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.