  Machine learning (ML) architectures such as convolutional neural networks
(CNNs) have garnered considerable recent attention in the study of quantum
many-body systems. However, advanced ML approaches such as transfer learning
have seldom been applied to such contexts. 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 one-dimensional (1D) Ising model with simultaneous transverse and parallel
magnetic fields, as quantitatively corroborated by relative entropy
measurements and magnetization between the predicted and exact state
distributions. At a cost of constant computational complexity, a larger
many-body state evolution was 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 only with knowledge from a smaller system.
