{
  "id": "1904.00031",
  "version": "v1",
  "published": "2019-03-29T18:07:28.000Z",
  "updated": "2019-03-29T18:07:28.000Z",
  "title": "NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems",
  "authors": [
    "Giuseppe Carleo",
    "Kenny Choo",
    "Damian Hofmann",
    "James E. T. Smith",
    "Tom Westerhout",
    "Fabien Alet",
    "Emily J. Davis",
    "Stavros Efthymiou",
    "Ivan Glasser",
    "Sheng-Hsuan Lin",
    "Marta Mauri",
    "Guglielmo Mazzola",
    "Christian B. Mendl",
    "Evert van Nieuwenburg",
    "Ossian O'Reilly",
    "Hugo Théveniaut",
    "Giacomo Torlai",
    "Alexander Wietek"
  ],
  "categories": [
    "quant-ph",
    "cond-mat.dis-nn",
    "cond-mat.str-el",
    "physics.comp-ph",
    "physics.data-an"
  ],
  "abstract": "We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wave functions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wave-function data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-03-29T18:07:28.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "many-body quantum systems",
      "machine learning toolkit",
      "quantum wave functions",
      "quantum many-body physics",
      "neural-network quantum states"
    ],
    "tags": [
      "research tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}