{
  "annotation_id": "bfd1782c-395d-4e9e-81ac-05dcbdadd61d",
  "date_created": "2026-02-17T05:53:15.044000Z",
  "date_modified": "2026-02-17T05:53:15.044000Z",
  "file_hash": "a2dc107472a8274d227c54634140ac39a5d0bf848569620a8f0197b970ef33cd",
  "private": false,
  "record": {
    "abstract": "Quantum error correction is one of the fundamental building blocks of digital quantum computation. The Quantum Lego formalism has introduced a systematic way of constructing new stabilizer codes out of basic lego-like building blocks, which in previous work we have used to generate improved error correcting codes via an automated reinforcement learning process. Here, we take this a step further and show the use of a hybrid classical-quantum algorithm. We combine classical reinforcement learning with calls to two commercial quantum devices to search for a stabilizer code to correct errors specific to the device, as well as an induced photon loss error.",
    "arxiv_id": "2601.08014",
    "authors": [
      "Yariv Yanay"
    ],
    "categories": [
      "quant-ph"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning",
    "url": "https://arxiv.org/abs/2601.08014",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d06b7277-e5a4-4405-9afe-eadbbf678f1b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}