{
  "annotation_id": "2335a8e0-8a9b-460d-83ff-a030c5c8b021",
  "date_created": "2026-03-02T18:02:10.048000Z",
  "date_modified": "2026-03-02T18:02:10.048000Z",
  "file_hash": "c27e3b2879b1549035f2d4adb4e9366f05fbd1a720cdc9ab81b07854802031a9",
  "private": false,
  "record": {
    "abstract": "Signal processing techniques will lean on blind methods in the near future,\nwhere no redundant, resource allocating information will be transmitted through\nthe channel. To achieve a proper decision, however, it is essential to know at\nleast the probability density function (pdf), which to estimate is classically\na time consumption and/or less accurate hard task, that may make decisions to\nfail. This paper describes the design of a quantum assisted pdf estimation\nmethod also by an example, which promises to achieve the exact pdf by proper\nsetting of parameters in a very fast way.",
    "arxiv_id": "quant-ph/0409033",
    "authors": [
      "Ferenc Bal\u00e1zs",
      "S\u00e1ndor Imre"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Quantum Computation Based Probability Density Function Estimation",
    "url": "https://arxiv.org/abs/quant-ph/0409033"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "baa18ad8-a33b-4169-8e88-0fedc9058852",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}