{
  "annotation_id": "551302b5-a9aa-4492-91ca-e95b98f7f036",
  "date_created": "2026-03-02T18:01:46.091000Z",
  "date_modified": "2026-03-02T18:01:46.091000Z",
  "file_hash": "60f7d48e87d46125819206da67184f324c0a5c3ec4ddaba447cf49dbb1e975e3",
  "private": false,
  "record": {
    "abstract": "Presented here is a matrix inversion method utilizing quantum searching\nalgorithm. In this method, huge Hilbert space as a whole spanned by myriad of\neigen states is searched and evaluated efficiently by sequential reduction in\ndimension one by one. Total iteration steps required for search are\nproportional to the number of unknown variables. Our method could solve very\nlarge linear equations with sufficiently high probability faster than any\nexisting classical algorithms, which roughly depends on the cube of unknown\nvariables.",
    "arxiv_id": "quant-ph/0109131",
    "authors": [
      "Atsushi Miyauchi"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Linear Order Matrix Inversion Method with Help from Quantum Searching   Algorithm",
    "url": "https://arxiv.org/abs/quant-ph/0109131"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5265221d-84eb-4d32-b1a6-fe36786bcedb",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}