{
  "annotation_id": "b8ee7c83-07c3-40c0-835c-a9b862e6cf66",
  "date_created": "2026-03-02T18:01:45.569000Z",
  "date_modified": "2026-03-02T18:01:45.569000Z",
  "file_hash": "8c558ab171ccb57d0e4f761c59852025f7e4dea1bd39a5bf0d4e8565ff6c4729",
  "private": false,
  "record": {
    "abstract": "I propose a \"quantum annealing\" heuristic for the problem of combinatorial\nsearch among a frustrated set of states characterized by a cost function to be\nminimized. The algorithm is probabilistic, with postselection of the\nmeasurement result. A unique parameter playing the role of an effective\ntemperature governs the computational load and the overall quality of the\noptimization. Any level of accuracy can be reached with a computational load\nindependent of the dimension {\\it N} of the search set by choosing the\neffective temperature correspondingly low. This is much better than classical\nsearch heuristics, which typically involve computation times growing as powers\nof log({\\it N})",
    "arxiv_id": "quant-ph/0107081",
    "authors": [
      "Carlo A. Trugenberger"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1088/1367-2630/4/1/326",
    "journal_ref": "New Journal of Physics 4 (2002) 1.1-1.7",
    "title": "Quantum Optimization for Combinatorial Searches",
    "url": "https://arxiv.org/abs/quant-ph/0107081"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "639a525b-b79d-4d18-8ec6-91d37d80ec9b",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}