{
  "id": "2005.08582",
  "version": "v1",
  "published": "2020-05-18T10:48:39.000Z",
  "updated": "2020-05-18T10:48:39.000Z",
  "title": "Quantum Machine Learning in High Energy Physics",
  "authors": [
    "Wen Guan",
    "Gabriel Perdue",
    "Arthur Pesah",
    "Maria Schuld",
    "Koji Terashi",
    "Sofia Vallecorsa",
    "Jean-Roch Vlimant"
  ],
  "comment": "25 pages, 9 figures, submitted to Machine Learning: Science and Technology, Focus on Machine Learning for Fundamental Physics collection",
  "categories": [
    "quant-ph",
    "hep-ph"
  ],
  "abstract": "Machine learning has been used in high energy physics since a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to combine quantum machine learning with High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-05-18T10:48:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "high energy physics",
      "quantum machine learning",
      "noisy intermediate-scale quantum computing devices",
      "first generation",
      "perform computations"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 25,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}