Quantum Entanglement for Machine Learning Algorithm Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a peculiar phenomenon in quantum mechanics, offers the potential for fundamentally novel approaches to machine learning. This paper explores the application of quantum entanglement to optimize machine learning model parameters, aiming to enhance model adaptability and generalization. We propose a novel algorithm, leveraging the unique properties of entangled states to dynamically adjust model parameters, creating a more robust and versatile learning process. The core mechanism centers around manipulating entangled states to influence the model's internal representation and parameter space. The paper details the theoretical framework, the implementation of the algorithm, and preliminary experimental results demonstrating improved performance on a specific dataset. The investigation suggests that this approach could represent a significant advancement in machine learning algorithm optimization, particularly in scenarios demanding high adaptability and robustness.

DOI: 10.5281/zenodo.22193943

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