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Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods — arXiv:2609.04652

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.04652
License (as archived): CC0-1.0
Archived: 2026-10-17T00:01:23.411Z
SHA-256 of the archived text: 366af63be124dec2878c0d7a2b2a2a092b10f368574574e545a4a2cfd1d2640e
Stable URL of this page: https://metraipolis.com/documento/dc_81c9f754dd67

How to cite

Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods — arXiv:2609.04652 — arXiv (en), https://arxiv.org/abs/2609.04652 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_81c9f754dd67 (Metraipolis archive dc_81c9f754dd67, sha256 366af63be124dec2878c0d7a2b2a2a092b10f368574574e545a4a2cfd1d2640e)

BibTeX

@misc{dc81c9f754dd67,
  title = {Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods — arXiv:2609.04652},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.04652},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_81c9f754dd67; sha256 366af63be124dec2878c0d7a2b2a2a092b10f368574574e545a4a2cfd1d2640e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.04652. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Selecting an effective encoding quantum circuit is a key challenge in quantum kernel methods because different feature maps can lead to different performance. Conventional methods require constructing and evaluating every circuit for each new dataset, making it computationally expensive. We present Qmes, an open-source Python package that automatically recommends circuits through meta-learning. Qmes characterizes a dataset using classical complexity measures and queries a pre-trained model to recommend circuits without quantum evaluation at inference time. The package provides modular components for meta-feature extraction, quantum-kernel evaluation, recommender training, model selection, and user-defined circuit extension. We validate Qmes on 105 classification and 86 regression benchmark datasets. Qmes reduces the mean recommendation regret by 2.2x and 4.2x for classification and regression, respectively, compared to a non-adaptive baseline, with statistical significance confirmed via a paired Wilcoxon signed-rank test ($p < 10^{-4}$). Qmes thus enables efficient and practical encoding-circuit selection for quantum kernel methods.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-07 in quant-ph).
Categorie arXiv: Quantum Physics (quant-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.04652 — 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 2026-10-16T23:41:14.292Z dalla pagina https://arxiv.org/catchup/quant-ph/2026-09-07?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).