{
  "annotation_id": "a84fa0d7-3a32-4447-b9f1-24c3ec1f1bb0",
  "date_created": "2026-03-02T18:02:13.642000Z",
  "date_modified": "2026-03-02T18:02:13.642000Z",
  "file_hash": "473e0780977ac851e8947effd537da70748d5119faf3eb1d196e97f24ccbe76f",
  "private": false,
  "record": {
    "abstract": "We present a framework for efficiently solving Approximate Traveling Salesman\nProblem (Approximate TSP) for Quantum Computing Models. Existing\nrepresentations of TSP introduce extra states which do not correspond to any\npermutation. We present an efficient and intuitive encoding for TSP in quantum\ncomputing paradigm. Using this representation and assuming a Gaussian\ndistribution on tour-lengths, we give an algorithm to solve Approximate TSP\n(Euclidean) within BQP resource bounds. Generalizing this strategy for any\ndistribution, we present an oracle based Quantum Algorithm to solve Approximate\nTSP. We present a realization of the oracle in the quantum counterpart of PP.",
    "arxiv_id": "quant-ph/0411013",
    "authors": [
      "Debabrata Goswami",
      "Harish Karnick",
      "Prateek Jain",
      "Hemanta K. Maji"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Towards Efficiently Solving Quantum Traveling Salesman Problem",
    "url": "https://arxiv.org/abs/quant-ph/0411013"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c8f65a9d-5e91-4909-b795-208247d13977",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}