Quantum-Inspired Regularization for Machine Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired regularization is a novel approach to machine learning that leverages the principles of quantum error correction to enhance model robustness and accuracy. This paper explores the integration of quantum-inspired error correction mechanisms into the optimization process, aiming to mitigate the sensitivity of models to noise and improve generalization performance. We present a theoretical framework outlining the key components of this method and demonstrate its effectiveness through a series of simulations, showcasing a significant reduction in error sensitivity compared to traditional regularization techniques.

DOI: 10.5281/zenodo.22193029

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