Quantum Machine Learning: Adaptive Regularization via Quantum States

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field that explores the potential of quantum computers to revolutionize machine learning. This paper proposes a novel quantum machine learning algorithm leveraging adaptive regularization within the quantum state space. We introduce a framework where the model parameters are mapped to quantum states, effectively implementing a form of quantum regularization. This regularization strategy is designed to enhance the model's robustness to noise and data distribution shifts, offering improved generalization performance and resilience in real-world applications. The core mechanism centers around dynamically adjusting the model's quantum state based on the input data, creating a self-correcting system. The research investigates the benefits of this approach compared to traditional machine learning techniques, particularly in scenarios demanding robustness. We demonstrate theoretical and preliminary experimental results showcasing the effectiveness of this quantum regularization method.

DOI: 10.5281/zenodo.22458305

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