Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods
Abstract
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.
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BibTeX
@misc{tung2026qmes,
title = {{Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods}},
author = {Dao Duy Tung and Quoc Chuong Nguyen and Vu Tuan Hai and Le Bin Ho and Lan Nguyen Tran},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04652},
eprint = {2609.04652},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04652},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods
AU - Dao Duy Tung
AU - Quoc Chuong Nguyen
AU - Vu Tuan Hai
AU - Le Bin Ho
AU - Lan Nguyen Tran
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.04652
UR - https://arxiv.org/abs/2609.04652
AB - 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.
ER -