{
  "id": "1804.06521",
  "version": "v1",
  "published": "2018-04-18T01:28:09.000Z",
  "updated": "2018-04-18T01:28:09.000Z",
  "title": "Method to solve quantum few-body problems with artificial neural networks",
  "authors": [
    "Hiroki Saito"
  ],
  "comment": "7 pages, 5 figures",
  "categories": [
    "cond-mat.dis-nn",
    "cond-mat.quant-gas",
    "physics.comp-ph"
  ],
  "abstract": "A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave function is output from the network. The method is applied to the Calogero-Sutherland model in one-dimensional space and Efimov bound states in three-dimensional space.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-18T01:28:09.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "artificial neural networks",
      "quantum few-body problems",
      "efimov bound states",
      "quantum few-body systems",
      "ground-state wave function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 7,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}