{
  "id": "1807.04259",
  "version": "v1",
  "published": "2018-07-11T17:43:39.000Z",
  "updated": "2018-07-11T17:43:39.000Z",
  "title": "The fundamentals of quantum machine learning",
  "authors": [
    "Bing Huang",
    "Nadine O. Symonds",
    "O. Anatole von Lilienfeld"
  ],
  "comment": "Published in Handbook of Materials Modeling (2018), https://doi.org/10.1007/978-3-319-42913-7_67-1",
  "journal": "Huang B., Symonds N.O., Lilienfeld O.A.. (2018) Quantum Machine Learning in Chemistry and Materials. In: Andreoni W., Yip S. (eds) Handbook of Materials Modeling. Springer, Cham",
  "doi": "10.1007/978-3-319-42913-7_67-1",
  "categories": [
    "physics.chem-ph",
    "physics.comp-ph"
  ],
  "abstract": "Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrodinger equation. The increasing availability of large quantum mechanics reference data sets have enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-07-11T17:43:39.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum machine learning",
      "quantum mechanics reference data sets",
      "large quantum mechanics reference data",
      "fundamentals",
      "qml models"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "Springer"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}