Exploring an implementation of quantum learning pipeline for support vector machines

• URL: http://arxiv.org/abs/2509.04983v1
• Date: Fri, 05 Sep 2025 10:19:32 GMT
• Title: Exploring an implementation of quantum learning pipeline for support vector machines
• Authors: Mario Bifulco, Luca Roversi,
• Abstract summary: This work presents a fully quantum approach to support vector machine (SVM) learning by integrating gate-based quantum kernel methods with quantum annealing-based optimization.<n>We explore the construction of quantum kernels using various feature maps and qubit, evaluating their suitability through Kernel-Target Alignment.<n>Our experiments demonstrate that a high degree of alignment in the kernel and an appropriate regularization parameter lead to competitive performance, with the best model achieving an F1-score of 90%.
• Score: 0.10742675209112622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a fully quantum approach to support vector machine (SVM) learning by integrating gate-based quantum kernel methods with quantum annealing-based optimization. We explore the construction of quantum kernels using various feature maps and qubit configurations, evaluating their suitability through Kernel-Target Alignment (KTA). The SVM dual problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling its solution via quantum annealers. Our experiments demonstrate that a high degree of alignment in the kernel and an appropriate regularization parameter lead to competitive performance, with the best model achieving an F1-score of 90%. These results highlight the feasibility of an end-to-end quantum learning pipeline and the potential of hybrid quantum architectures in quantum high-performance computing (QHPC) contexts.

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