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Quantum Knowledge Distillation: State-of-the-art, Applications and Open Problems — arXiv:2611.08461

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08461
License (as archived): CC0-1.0
Archived: 2026-11-06T14:55:07.235Z
SHA-256 of the archived text: 142e89eb90987a0dde94f076f461da04f3a28993b93c0998f4293272ec73241f
Stable URL of this page: https://metraipolis.com/documento/dc_e7bb89efedb4

How to cite

Quantum Knowledge Distillation: State-of-the-art, Applications and Open Problems — arXiv:2611.08461 — arXiv (en), https://arxiv.org/abs/2611.08461 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e7bb89efedb4 (Metraipolis archive dc_e7bb89efedb4, sha256 142e89eb90987a0dde94f076f461da04f3a28993b93c0998f4293272ec73241f)

BibTeX

@misc{dce7bb89efedb4,
  title = {Quantum Knowledge Distillation: State-of-the-art, Applications and Open Problems — arXiv:2611.08461},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08461},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e7bb89efedb4; sha256 142e89eb90987a0dde94f076f461da04f3a28993b93c0998f4293272ec73241f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08461. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Knowledge distillation transfers predictions, representations, or other supervisory information between learning models and is increasingly investigated in quantum machine learning. Its applications extend beyond compression to cross-architecture transfer, circuit approximation, distributed learning, and classical deployment of models trained using quantum-derived supervision. This survey reviews these approaches using two complementary classification axes: teacher-student configuration and the role of quantum resources in the distillation process. Quantum-inspired methods are discussed separately from methods that simulate quantum computations classically or execute them on quantum hardware. We explain how measurement probabilities, expectation values, classical readouts, state overlaps, and circuit-level objectives support different transfer mechanisms. We then critically assess the presented results against baseline design, execution conditions, uncertainty, and resource costs. The evidence shows that distillation can improve selected quantum students, but benefits are task- and architecture-dependent, and several studies find negligible gains or even performance degradation. Evaluations on physical quantum hardware remain limited, with some testing individual quantum components rather than complete learning pipelines. Heterogeneous protocols and incomplete resource reporting prevent general conclusions about practical quantum advantage. We identify priorities for controlled benchmarking, noise-aware learning, reproducible evaluation, and efficient transfer between quantum and classical models.

Tema degli esploratori: fisica e astronomia (elenco arXiv quant-ph, nuovi lavori del giorno).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2611.08461 — 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 2026-11-06T14:28:42.580Z dalla pagina https://arxiv.org/list/quant-ph/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).