{
  "id": "1906.07682",
  "version": "v1",
  "published": "2019-06-18T16:52:40.000Z",
  "updated": "2019-06-18T16:52:40.000Z",
  "title": "Parameterized quantum circuits as machine learning models",
  "authors": [
    "Marcello Benedetti",
    "Erika Lloyd",
    "Stefan Sack"
  ],
  "comment": "19 pages, 8 figures;",
  "categories": [
    "quant-ph",
    "cs.LG"
  ],
  "abstract": "Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be thought of as machine learning models with remarkable expressive power. This Review presents components of these models and discusses their application to a variety of data-driven tasks such as supervised learning and generative modeling. With experimental demonstrations carried out on actual quantum hardware, and with software actively being developed, this rapidly growing field could become one of the first instances of quantum computing that addresses real world problems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-06-18T16:52:40.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "parameterized quantum circuits",
      "machine learning models",
      "addresses real world problems",
      "actual quantum hardware",
      "hybrid quantum-classical systems"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 19,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}