Title: Quantum Machine Learning-Based Adaptive Data Enhancement

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel data augmentation technique based on quantum machine learning, termed Adaptive Data Enhancement (ADE). The core principle of ADE lies in its ability to dynamically adjust data augmentation strategies based on the inherent changes in the input data distribution. Traditional data augmentation methods rely on predefined parameters, which often struggle to effectively capture the complexities of real-world data. ADE leverages quantum machine learning algorithms to perform this adaptation in a computationally efficient manner, resulting in improved model generalization and enhanced machine learning performance. The proposed method offers a significant advancement over existing techniques by providing a mechanism for automated and responsive data enhancement, addressing a critical challenge in the field of machine learning.

DOI: 10.5281/zenodo.22296666

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