{
  "id": "1902.09216",
  "version": "v1",
  "published": "2019-02-25T12:01:55.000Z",
  "updated": "2019-02-25T12:01:55.000Z",
  "title": "Revealing quantum chaos with machine learning",
  "authors": [
    "Y. A. Kharkov",
    "V. E. Sotskov",
    "A. A. Karazeev",
    "E. O. Kiktenko",
    "A. K. Fedorov"
  ],
  "comment": "6+4 pages, 6+5 figures",
  "categories": [
    "quant-ph",
    "cond-mat.quant-gas",
    "cs.LG"
  ],
  "abstract": "Understanding the properties of quantum matter is an outstanding challenge in science. In this work, we demonstrate how machine learning methods can be successfully applied for the classification of various regimes in single-particle and many-body systems. We realize neural network algorithms that perform a classification between regular and chaotic behavior in quantum billiard models with remarkably high accuracy. By taking this method further, we show that machine learning techniques allow to pin down the transition from integrability to many-body quantum chaos in Heisenberg XXZ spin chains. Our results pave the way for exploring the power of machine learning tools for revealing exotic phenomena in complex quantum many-body systems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-02-25T12:01:55.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning",
      "revealing quantum chaos",
      "complex quantum many-body systems",
      "heisenberg xxz spin chains",
      "many-body quantum chaos"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}