Learning topological defects formation with neural networks in a quantum phase transition

Han-Qing Shi, Hai-Qing Zhang

Published 2022-04-14Version 1

The strong representing power of neural networks make it become a powerful tool for solving quantum many-body systems. Except for static solutions, nonequilibrium processes are more challenging for neural networks. We study time evolutions and the universal statistics of topological defects beyond Kibble-Zurek mechanism after a quantum phase transition in a transverse Ising model by virtue of the state-of-the-art neural networks and machine learning methods. The first three cumulants of the topological defects numbers and the energy spectrum for this transverse Ising model are computed after a linear quench. The resulting outcomes match the analytical predictions very well.