{
  "annotation_id": "2e1ff4db-e429-42fd-b477-92255a41a799",
  "date_created": "2026-03-02T18:02:34.480000Z",
  "date_modified": "2026-03-02T18:02:34.480000Z",
  "file_hash": "a76ab708719baabfd19789ba5e61dacd53083b3b29ce63d5362504cd6b291a4e",
  "private": false,
  "record": {
    "abstract": "We propose a refined iterative likelihood-maximization algorithm for\nreconstructing a quantum state from a set of tomographic measurements. The\nalgorithm is characterized by a very high convergence rate and features a\nsimple adaptive procedure that ensures likelihood increase in every iteration\nand convergence to the maximum-likelihood state.\n  We apply the algorithm to homodyne tomography of optical states and quantum\ntomography of entangled spin states of trapped ions and investigate its\nconvergence properties.",
    "arxiv_id": "quant-ph/0611244",
    "authors": [
      "Jaroslav Rehacek",
      "Zdenek Hradil",
      "E. Knill",
      "A. I. Lvovsky"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1103/PhysRevA.75.042108",
    "journal_ref": "Physical Review A 75, 042108 (2007)",
    "title": "Diluted maximum-likelihood algorithm for quantum tomography",
    "url": "https://arxiv.org/abs/quant-ph/0611244"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "44d22d0c-b122-4fc5-80d3-a6e06e26de25",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}