{
  "annotation_id": "78883212-26c1-466c-9e3a-4bdb323b796c",
  "date_created": "2026-03-02T18:02:27.172000Z",
  "date_modified": "2026-03-02T18:02:27.172000Z",
  "file_hash": "692411dd4c67b4f1edd1c6cd6d24b7be7c52017d0d69d89d279a878d430fb63b",
  "private": false,
  "record": {
    "abstract": "We demonstrate the possibility of realizing a neural network in a chain of\ntrapped ions with induced long range interactions. Such models permit one to\nstore information distributed over the whole system. The storage capacity of\nsuch network, which depends on the phonon spectrum of the system, can be\ncontrolled by changing the external trapping potential. We analyze the\nimplementation of error resistant universal quantum information processing in\nsuch systems.",
    "arxiv_id": "quant-ph/0607016",
    "authors": [
      "Marisa Pons",
      "Veronica Ahufinger",
      "Christof Wunderlich",
      "Anna Sanpera",
      "Sibylle Braungardt",
      "Aditi Sen De",
      "Ujjwal Sen",
      "Maciej Lewenstein"
    ],
    "categories": [
      "quant-ph",
      "cond-mat.dis-nn"
    ],
    "doi": "10.1103/PhysRevLett.98.023003",
    "journal_ref": "Phys. Rev. Lett. 98, 023003 (2007)",
    "title": "Trapped Ion Chain as a Neural Network: Error Resistant Quantum   Computation",
    "url": "https://arxiv.org/abs/quant-ph/0607016"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0819b044-7f8c-4d27-8cbb-dd69c147e6bc",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}