  We study the dynamics of quantum skyrmions under a magnetic field gradient
using neural network quantum states. First, we obtain a quantum skyrmion
lattice ground state using variational Monte Carlo with a restricted Boltzmann
machine as the variational ansatz for a quantum Heisenberg model with
Dzyaloshinskii-Moriya interaction. Then, using the time-dependent variational
principle, we study the real-time evolution of quantum skyrmions after a
Hamiltonian quench with an inhomogeneous external magnetic field. We show that
field gradients are an effective way of manipulating and moving quantum
skyrmions. Furthermore, we demonstrate that quantum skyrmions can decay when
interacting with each other. This work shows that neural network quantum states
offer a promising way of studying the real-time evolution of quantum magnetic
systems that are outside the realm of exact diagonalization.
