Title: Universal Quantum Machine Learning with Dynamic Kernel Selection

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel machine learning framework, Universal Quantum Machine Learning (UQML), that leverages dynamic kernel selection to enhance generalization performance. Traditional machine learning often employs fixed kernels, limiting the model's ability to effectively adapt to diverse data distributions. UQML addresses this limitation by dynamically adapting the kernel function during training, ensuring optimal fit across various data types. We propose a quantum-inspired approach that continuously optimizes the kernel shape to achieve superior predictive accuracy and robustness. This work explores the theoretical implications of dynamic kernel selection and demonstrates its effectiveness through a series of simulations and experiments. The core mechanism centers around a quantum-enhanced kernel adaptation process that optimizes the kernel's shape in real-time. The benefits of this approach include improved generalization, reduced sensitivity to data initialization, and the potential for more efficient training.

DOI: 10.5281/zenodo.22219104

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