{
  "annotation_id": "ed808641-862c-4804-826d-fa9016c9999c",
  "date_created": "2026-03-02T18:02:23.791000Z",
  "date_modified": "2026-03-02T18:02:23.791000Z",
  "file_hash": "a9413da9c30f06630d4233c278460727b884696d3fc1214cada6daefe49eb39b",
  "private": false,
  "record": {
    "abstract": "Discrete Cosine Transform (DCT) is very important in image compression.\nClassical 1-D DCT and 2-D DCT has time complexity O(NlogN) and O(N\u0026sup2;logN)\nrespectively. This paper presents a quantum DCT iteration, and constructs a\nquantum 1-D and 2-D DCT algorithm for image compression by using the iteration.\nThe presented 1-D and 2-D DCT has time complexity O(sqrt(N)) and O(N)\nrespectively. In addition, the method presented in this paper generalizes the\nfamous Grover\u0027s algorithm to solve complex unstructured search problem.",
    "arxiv_id": "quant-ph/0601043",
    "authors": [
      "Chao Yang Pang",
      "Zheng Wei Zhou",
      "Guang Can Guo"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Quantum Discrete Cosine Transform for Image Compression",
    "url": "https://arxiv.org/abs/quant-ph/0601043"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "ea5bb001-6711-4750-99f0-a98341b15663",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}