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Learned-projector QAOA for hierarchical optimization — arXiv:2609.28888

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.28888
License (as archived): CC0-1.0
Archived: 2026-10-08T07:36:34.092Z
SHA-256 of the archived text: 51210f50da9242ad51bff4f41dbc78ecca7f64bd4a2611aa3a983c85fe58c830
Stable URL of this page: https://metraipolis.com/documento/dc_bd07d58ed271

How to cite

Learned-projector QAOA for hierarchical optimization — arXiv:2609.28888 — arXiv (en), https://arxiv.org/abs/2609.28888 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bd07d58ed271 (Metraipolis archive dc_bd07d58ed271, sha256 51210f50da9242ad51bff4f41dbc78ecca7f64bd4a2611aa3a983c85fe58c830)

BibTeX

@misc{dcbd07d58ed271,
  title = {Learned-projector QAOA for hierarchical optimization — arXiv:2609.28888},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.28888},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bd07d58ed271; sha256 51210f50da9242ad51bff4f41dbc78ecca7f64bd4a2611aa3a983c85fe58c830},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.28888. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Many optimization problems reveal inexpensive structural information before requiring costly final evaluation, whereas the conventional quantum approximate optimization algorithm (QAOA) applies a single aggregate objective throughout. We introduce the learned-projector quantum alternating operator ansatz (LP-QAOA), a multistage protocol that freezes optimized circuits and uses their output states to define later projector mixers. We prove a stability theorem for LP-QAOA that bounds the propagation of approximation errors through successive frozen stages. Our analysis also shows how learned-projector mixing avoids the high-order tunnelling suppression of local mixers. We conduct state-vector simulations on block-constrained spin tiling (BCST) instances, where LP-QAOA achieves higher probabilities of sampling the optimum, lower estimated logical-resource requirements, and better trainability than the tested baselines. A supporting stochastic block model experiment extends LP-QAOA to optimization problems with soft hierarchical structure. Our results highlight the potential of LP-QAOA to improve variational quantum optimization by exploiting hierarchical problem structure.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-25 in quant-ph).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.28888 — 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 2026-10-08T06:49:21.214Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).