# Predicting quantum many-body dynamics with transferable neural networks

## TL;DR

This paper introduces a recurrent neural network framework that efficiently predicts the dynamics of 1D quantum many-body systems, demonstrating transferability and accuracy without detailed Hamiltonian knowledge.

## Contribution

It presents a simple SRU-based transfer learning approach capable of predicting quantum dynamics from a single initial state, reducing computational costs and requiring minimal system information.

## Key findings

- Accurately predicts 1D Ising model dynamics
- Demonstrates transferability to larger systems
- Achieves predictions with constant computational complexity

## Abstract

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.