Complexity Theory Quantum Machine Learning Algorithm Design

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of complexity theory to the design of quantum machine learning algorithms. The core claim is to develop a quantum machine learning algorithm that leverages the principles of complexity theory to optimize model parameters and training processes, ultimately enhancing model generalization capabilities. Traditional machine learning often focuses on data features, but this research proposes a novel approach that utilizes complexity theory to dynamically restructure model parameters and training pathways. The paper details the theoretical underpinnings of complexity theory, its application to algorithm design, and the potential benefits of this methodology. We present a preliminary implementation demonstrating a potential improvement in performance compared to existing quantum machine learning approaches.

DOI: 10.5281/zenodo.22274219

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