{
  "id": "2303.09491",
  "version": "v1",
  "published": "2023-03-16T17:10:39.000Z",
  "updated": "2023-03-16T17:10:39.000Z",
  "title": "Challenges and Opportunities in Quantum Machine Learning",
  "authors": [
    "M. Cerezo",
    "Guillaume Verdon",
    "Hsin-Yuan Huang",
    "Lukasz Cincio",
    "Patrick J. Coles"
  ],
  "comment": "14 pages, 5 figures",
  "journal": "Nature Computational Science 2, 567-576 (2022)",
  "doi": "10.1038/s43588-022-00311-3",
  "categories": [
    "quant-ph",
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-03-16T17:10:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum machine learning",
      "opportunities",
      "review current methods",
      "quantum neural networks",
      "quantum data"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "Nature"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 14,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}