{
  "annotation_id": "5eb512c6-9612-4b8d-977a-ccb460a4573e",
  "date_created": "2026-03-02T18:02:27.111000Z",
  "date_modified": "2026-03-02T18:02:27.111000Z",
  "file_hash": "d1b2a1b1a7dafcd2846968db89e44cfb810c7788be7aea29971a014039cf9d7e",
  "private": false,
  "record": {
    "abstract": "We show that the problem of designing a quantum information error correcting\nprocedure can be cast as a bi-convex optimization problem, iterating between\nencoding and recovery, each being a semidefinite program. For a given encoding\noperator the problem is convex in the recovery operator. For a given method of\nrecovery, the problem is convex in the encoding scheme. This allows us to\nderive new codes that are locally optimal. We present examples of such codes\nthat can handle errors which are too strong for codes derived by analogy to\nclassical error correction techniques.",
    "arxiv_id": "quant-ph/0606078",
    "authors": [
      "Robert L. Kosut",
      "Daniel A. Lidar"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1007/s11128-009-0120-2",
    "journal_ref": "Quant. Inf. Proc. 8, 443 (2009)",
    "title": "Quantum Error Correction via Convex Optimization",
    "url": "https://arxiv.org/abs/quant-ph/0606078"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8ad3c823-d314-40f9-a679-1255a430f253",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}