{
  "annotation_id": "56c61423-e240-4d69-8c67-e7db1c2c24cd",
  "date_created": "2026-03-02T18:01:44.799000Z",
  "date_modified": "2026-03-02T18:01:44.799000Z",
  "file_hash": "60cf97364d8e68f873c26bf2eda9038b6923fa53e7d03cee77dc458e92709fc7",
  "private": false,
  "record": {
    "abstract": "Quantum computation holds promise for the solution of many intractable\nproblems. However, since many quantum algorithms are stochastic in nature they\ncan only find the solution of hard problems probabilistically. Thus the\nefficiency of the algorithms has to be characterized both by the expected time\nto completion {\\it and} the associated variance. In order to minimize both the\nrunning time and its uncertainty, we show that portfolios of quantum algorithms\nanalogous to those of finance can outperform single algorithms when applied to\nthe NP-complete problems such as 3-SAT.",
    "arxiv_id": "quant-ph/0105071",
    "authors": [
      "Sebastian Maurer",
      "Tad Hogg",
      "Bernardo Huberman"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1103/PhysRevLett.87.257901",
    "journal_ref": "Physical Review Letters 87, 257901 (2001)",
    "title": "Quantum Portfolios",
    "url": "https://arxiv.org/abs/quant-ph/0105071"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1f1b5d3d-e486-4b85-9504-01d3a0b5879f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}