{
  "id": "1801.06316",
  "version": "v1",
  "published": "2018-01-19T06:50:57.000Z",
  "updated": "2018-01-19T06:50:57.000Z",
  "title": "Demonstration of Topological Data Analysis on a Quantum Processor",
  "authors": [
    "He-Liang Huang",
    "Xi-Lin Wang",
    "Peter P. Rohde",
    "Yi-Han Luo",
    "You-Wei Zhao",
    "Chang Liu",
    "Li Li",
    "Nai-Le Liu",
    "Chao-Yang Lu",
    "Jian-Wei Pan"
  ],
  "comment": "Accepted by Optica",
  "categories": [
    "quant-ph",
    "cs.AI"
  ],
  "abstract": "Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points -- topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-01-19T06:50:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "demonstration",
      "data points",
      "six-photon quantum processor",
      "topological data analysis offers",
      "topological features"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}