{
  "id": "2204.06769",
  "version": "v1",
  "published": "2022-04-14T06:00:19.000Z",
  "updated": "2022-04-14T06:00:19.000Z",
  "title": "Learning topological defects formation with neural networks in a quantum phase transition",
  "authors": [
    "Han-Qing Shi",
    "Hai-Qing Zhang"
  ],
  "comment": "5 pages, 3 figures",
  "categories": [
    "cond-mat.dis-nn",
    "hep-th"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-04-14T06:00:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum phase transition",
      "learning topological defects formation",
      "transverse ising model",
      "study time evolutions",
      "state-of-the-art neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}