{
  "annotation_id": "906def27-61c0-4612-8050-a717631f337d",
  "date_created": "2026-03-02T18:01:38.560000Z",
  "date_modified": "2026-03-02T18:01:38.560000Z",
  "file_hash": "8de8746c3e152e6bcb62edec12242bbe6f48d867e92924046ccc85a3cbb02822",
  "private": false,
  "record": {
    "abstract": "We present a quantum algorithm for combinatorial optimization using the cost\nstructure of the search states. Its behavior is illustrated for overconstrained\nsatisfiability and asymmetric traveling salesman problems. Simulations with\nrandomly generated problem instances show each step of the algorithm shifts\namplitude preferentially towards lower cost states, thereby concentrating\namplitudes into low-cost states, on average. These results are compared with\nconventional heuristics for these problems.",
    "arxiv_id": "quant-ph/0006090",
    "authors": [
      "Tad Hogg",
      "Dmitriy Portnov"
    ],
    "categories": [
      "quant-ph"
    ],
    "journal_ref": "Information Sciences 128, 181-197 (2000)",
    "title": "Quantum Optimization",
    "url": "https://arxiv.org/abs/quant-ph/0006090"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6583c101-8dda-4101-aca8-1a82be000065",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}