Algorithmic Quantum State Collapse for Machine Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum state collapse, a fundamental process in quantum mechanics, offers a potential paradigm shift in machine learning. This paper explores the theoretical and potential practical applications of quantum state collapse in optimizing machine learning models, specifically focusing on the enhancement of training efficiency and model accuracy. We delve into the core mechanisms underlying this approach, outlining a novel algorithm that leverages quantum entanglement and superposition to dynamically adjust model parameters during the training phase. The paper presents a preliminary analysis of the benefits and challenges associated with this approach, emphasizing the need for further research and validation. We conclude by highlighting the potential impact of this technology on the future of machine learning.

DOI: 10.5281/zenodo.22281145

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