{
  "id": "1711.05238",
  "version": "v1",
  "published": "2017-11-14T18:24:41.000Z",
  "updated": "2017-11-14T18:24:41.000Z",
  "title": "Quantum parameter estimation with a neural network",
  "authors": [
    "Eliska Greplova",
    "Christian Kraglund Andersen",
    "Klaus Mølmer"
  ],
  "comment": "8 pages, 11 figures",
  "categories": [
    "quant-ph"
  ],
  "abstract": "We propose to use neural networks to estimate the rates of coherent and incoherent processes in quantum systems from continuous measurement records. In particular, we adapt an image recognition algorithm to recognize the patterns in experimental signals and link them to physical quantities. We demonstrate that the parameter estimation works unabatedly in the presence of detector imperfections which complicate or rule out Bayesian filter analyses.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-11-14T18:24:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum parameter estimation",
      "neural network",
      "image recognition algorithm",
      "parameter estimation works",
      "bayesian filter analyses"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}