发表机构
Faculty of Physics and Engineering Physics, University of Science; Vietnam National University; Institute of Fundamental and Applied Sciences, Duy Tan University; University of Information Technology; Graduate School of Engineering, Tohoku University; Frontier Research Institute for Interdisciplinary Sciences, Tohoku University（理科大学物理与工程物理学院; 越南国立大学; 得胜大学基础与应用科学学院; 信息技术大学; 东北大学研究生院工学研究科; 东北大学跨学科科学前沿研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Qmes是用于量子核方法编码选择的量子元学习Python软件包，可通过元学习自动推荐电路，在105个分类和86个回归数据集上验证，相比基线大幅降低推荐遗憾，实现高效编码选择。
AI 中文摘要
选择有效的编码量子电路是量子核方法中的关键挑战，因为不同的特征映射会导致不同的性能。传统方法需要为每个新数据集构建并评估所有电路，计算成本高昂。我们提出Qmes，这是一个开源Python软件包，可通过元学习自动推荐电路。Qmes使用经典复杂度度量来表征数据集，并在推理时无需量子评估即可查询预训练模型以推荐电路。该软件包提供了元特征提取、量子核评估、推荐器训练、模型选择和用户自定义电路扩展的模块化组件。我们在105个分类和86个回归基准数据集上验证了Qmes。与非自适应基线相比，Qmes将分类任务的平均推荐遗憾降低了2.2倍，回归任务降低了4.2倍，通过配对Wilcoxon符号秩检验（p < 10⁻⁴）确认了统计显著性。因此，Qmes为量子核方法实现了高效且实用的编码电路选择。
英文摘要
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.
Comments12 pages, 5 figures. Code: https://github.com/tungduy1704/Qmes
