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Qmes addresses a critical bottleneck in quantum kernel methods by using classical meta-learning to recommend optimal encoding circuits without costly quantum evaluations at inference time. This approach significantly reduces recommendation regret across diverse benchmarks, offering a practical pathway to streamline feature map selection for near-term quantum algorithms. The open-source package provides essential tools for researchers aiming to optimize hybrid classical-quantum workflows efficiently.
Qmes: Quantum Meta-Learning for Encoding Selection in Quantum Kernel Methods
arXiv:2609.04652v1 Announce Type: new 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.
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.
12 pages, 5 figures. Code: this https URL
arXiv:2609.04652 [quant-ph]
(or arXiv:2609.04652v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.04652 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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