{
  "id": "2101.11099",
  "version": "v1",
  "published": "2021-01-26T22:04:29.000Z",
  "updated": "2021-01-26T22:04:29.000Z",
  "title": "Neural networks in quantum many-body physics: a hands-on tutorial",
  "authors": [
    "Juan Carrasquilla",
    "Giacomo Torlai"
  ],
  "comment": "21 pages, 7 figures",
  "categories": [
    "quant-ph",
    "cond-mat.dis-nn",
    "cond-mat.quant-gas",
    "cond-mat.str-el"
  ],
  "abstract": "Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to mitigate the exponential complexity often encountered in quantum many-body physics, the study of properties of quantum systems built out of a large number of interacting particles. In this Article, we overview some applications of machine learning in condensed matter physics and quantum information, with particular emphasis on hands-on tutorials serving as a quick-start for a newcomer to the field. We present supervised machine learning with convolutional neural networks to learn a phase transition, unsupervised learning with restricted Boltzmann machines to perform quantum tomography, and variational Monte Carlo with recurrent neural-networks for approximating the ground state of a many-body Hamiltonian. We briefly review the key ingredients of each algorithm and their corresponding neural-network implementation, and show numerical experiments for a system of interacting Rydberg atoms in two dimensions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-01-26T22:04:29.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum many-body physics",
      "hands-on tutorial",
      "machine learning",
      "variational monte carlo",
      "artificial neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 21,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}