2024/03/13 by Ashish Joshi, Robert Peters, Joshi, Ashish +3 · 2 citations
Computer Science · Physics and Astronomy · #Cold Atom Physics and Bose-Einstein Condensates #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Neural Networks and Applications #Statistical Mechanics and Entropy
paper · doi:10.48550/arxiv.2403.08184
openalex publication_date 2024/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.