Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning

Quantum Physics [Submitted on 21 Apr 2026] Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning Dao Duy Tung, Nguyen Quoc Chuong, Vu Tuan Hai, Le Bin Ho, Lan Nguyen Tran In recent years, quantum kernel methods have shown promising applications on near-term quantum devices. However, selecting an appropriate encoding circuit for a given dataset requires costly evaluation of multiple candidates, formulated as a meta-learning problem. In this paper, we propose an automated recommender that utilizes the intrinsic characteristics of datasets to predict the optimal circuit without any quantum evaluation. Nine candidates are assessed alongside 24 classical complexity metrics serving as features, evaluated through two training approaches with four configurations, along with 14 machine learning models. Both approaches achieve Top-3 accuracy of up to 85.7% in identifying the best-performing encoding circuit, and demonstrate that classical data complexity metrics provide sufficient predictive signal for circuit selection. Comments: 22 pages, 4 figures. This paper is submitted to Quantum Machine Intelligence Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.19076 [quant-ph]   (or arXiv:2604.19076v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.19076 Focus to learn more Submission history From: Hai Vu Tuan [view email] [v1] Tue, 21 Apr 2026 04:37:38 UTC (342 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)