# Investigating ultrafast quantum magnetism with machine learning

## TL;DR

This paper demonstrates that Restricted Boltzmann Machines can efficiently model static and dynamic properties of large two-dimensional quantum spin systems, surpassing previous computational limits and aligning well with exact methods.

## Contribution

It introduces the application of RBMs to study both static and dynamic quantum spin properties in large 2D systems, showing high accuracy and scalability.

## Key findings

- Close agreement with Quantum Monte Carlo for static properties.
- Excellent match with exact diagonalization for small systems.
- Consistent results with spin-wave theory for larger systems.

## Abstract

We investigate the efficiency of the recently proposed Restricted Boltzmann Machine (RBM) representation of quantum many-body states to study both the static properties and quantum spin dynamics in the two-dimensional Heisenberg model on a square lattice. For static properties we find close agreement with numerically exact Quantum Monte Carlo results in the thermodynamical limit. For dynamics and small systems, we find excellent agreement with exact diagonalization, while for systems up to N=256 spins close consistency with interacting spin-wave theory is obtained. In all cases the accuracy converges fast with the number of network parameters, giving access to much bigger systems than feasible before. This suggests great potential to investigate the quantum many-body dynamics of large scale spin systems relevant for the description of magnetic materials strongly out of equilibrium.