Quantum Machine Learning: Adaptive Regularization for Enhanced Robustness

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) presents a paradigm shift in computational paradigms, offering the potential to surpass classical algorithms in complex data analysis and modeling. This paper investigates the application of adaptive regularization, a key component of QML, to improve the robustness and generalization of machine learning models. We introduce a novel quantum algorithm designed to dynamically adjust model parameters and structure based on data characteristics, specifically focusing on mitigating noise and capturing non-linear relationships. The core mechanism centers around exploiting quantum properties to achieve a more nuanced and adaptable learning process. The algorithm's proposed adaptive regularization offers a significant advancement over existing techniques, promising to enhance model performance in real-world scenarios.

DOI: 10.5281/zenodo.22253995

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