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Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes — arXiv:2610.41398

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41398
License (as archived): CC0-1.0
Archived: 2026-11-02T07:40:40.951Z
SHA-256 of the archived text: 834a7906d87db3fe76fad67097e7e445e52040158845ddd46e05648b128ad89c
Stable URL of this page: https://metraipolis.com/documento/dc_f6f85fbdd12e

How to cite

Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes — arXiv:2610.41398 — arXiv (en), https://arxiv.org/abs/2610.41398 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f6f85fbdd12e (Metraipolis archive dc_f6f85fbdd12e, sha256 834a7906d87db3fe76fad67097e7e445e52040158845ddd46e05648b128ad89c)

BibTeX

@misc{dcf6f85fbdd12e,
  title = {Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes — arXiv:2610.41398},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41398},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f6f85fbdd12e; sha256 834a7906d87db3fe76fad67097e7e445e52040158845ddd46e05648b128ad89c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41398. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates, and noise models. GraphMP uses local message passing to keep computation scalable as the decoding graph grows. Guided by its predictions, beam search progressively refines candidate error patterns, trading additional decoding time for lower logical error rates. We develop the decoder model factory as a paradigm for training successive models on experience generated by earlier models during decoding. Model-guided search supplies verified trajectories for knowledge distillation, folding computation spent on decoding into reusable training data. Repeated collection and distillation improve the decoder across generations and adapt its strategy as the noise changes. These results motivate a coupled quantum--classical architecture with two interacting cycles: rapid decoding protects quantum information, while slower learning renews the models from operating experience. The long-term vision is a scalable decoder model factory that produces high-performance, low-latency decoders and continually adapts them to evolving quantum hardware.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Quantum Physics (quant-ph) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41398 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:37:00.577Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).