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Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization — arXiv:2609.23180

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23180
License (as archived): CC0-1.0
Archived: 2026-10-09T22:40:07.358Z
SHA-256 of the archived text: 8fc29710a70522713c100bef7ce3517106e9d40100d56bd5076111e2070bf595
Stable URL of this page: https://metraipolis.com/documento/dc_60bfbbe3fb32

How to cite

Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization — arXiv:2609.23180 — arXiv (en), https://arxiv.org/abs/2609.23180 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_60bfbbe3fb32 (Metraipolis archive dc_60bfbbe3fb32, sha256 8fc29710a70522713c100bef7ce3517106e9d40100d56bd5076111e2070bf595)

BibTeX

@misc{dc60bfbbe3fb32,
  title = {Adaptive Differential Evolution and Multistart Search for Noisy QAOA Optimization — arXiv:2609.23180},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23180},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_60bfbbe3fb32; sha256 8fc29710a70522713c100bef7ce3517106e9d40100d56bd5076111e2070bf595},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23180. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We benchmark classical optimization of a fixed low-depth Quantum Approximate Optimization Algorithm (QAOA) ansatz across four cost-Hamiltonian families at $N=12$, $p=3$, and $D=6$. Ten optimizers are compared over 25 independent runs under common ceilings of 10\,000 and 30\,000 function evaluations (FEs), first with exact statevector objectives and then with two additive observation-noise levels. Exact objectives favor multistart BFGS and multistart CMA-ES. Under noisy feedback, adaptive population methods become more competitive, but the ranking depends on whether performance is measured by the best exact point visited or by the point selected from noisy observations. A targeted extension over all three pre-screened instances per family confirms this regime change while showing that named adaptive-DE winners are instance dependent: jSO-lite leads low-noise oracle search, iL-SHADE high-noise oracle search, and L-SRTDE high-noise selected solutions in the equal-instance summaries. Bootstrap analysis quantifies a non-negligible high-noise search--selection gap, and a retrospective fixed-budget verification proxy shows that reserving a small measurement budget for final re-evaluation improves selected quality across all ten methods at 30\,000 FEs. A supplementary structure-aware study further shows that QAOA cross-depth restriction and continuous basin refinement are more useful in these conditions than standalone Monte Carlo tree-search selection. Overall, optimizer choice depends jointly on landscape structure, observation noise, and final-point identification.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-22 in cs.NE).
Categorie arXiv: Quantum Physics (quant-ph) ; Neural and Evolutionary Computing (cs.NE).
Pagina del lavoro: https://arxiv.org/abs/2609.23180 — 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 2026-10-09T22:23:32.462Z dalla pagina https://arxiv.org/catchup/cs.NE/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).