{
  "id": "2012.07677",
  "version": "v2",
  "published": "2020-12-14T16:26:05.000Z",
  "updated": "2021-08-12T07:52:41.000Z",
  "title": "Neural-network-based parameter estimation for quantum detection",
  "authors": [
    "Yue Ban",
    "Javier Echanobe",
    "Yongcheng Ding",
    "Ricardo Puebla",
    "Jorge Casanova"
  ],
  "comment": "18 pages, 7 figures",
  "journal": "Yue Ban et al 2021 Quantum Sci. Technol. 6 045012",
  "doi": "10.1088/2058-9565/ac16ed",
  "categories": [
    "quant-ph"
  ],
  "abstract": "Artificial neural networks bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, neural networks find a natural playground. In particular, in the presence of a target, a quantum sensor delivers a response, i.e., the input data, which can be subsequently processed by a neural network that outputs the target features. We demonstrate that adequately trained neural networks enable to characterize a target with minimal knowledge of the underlying physical model, in regimes where the quantum sensor presents complex responses, and under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for $^{171}$Yb$^{+}$ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2021-08-12T07:52:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "quantum detection",
      "neural-network-based parameter estimation",
      "trained neural networks enable",
      "neural networks bridge input data",
      "artificial neural networks bridge input"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "IOP"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 18,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}