Quantum Entanglement-Based Machine Learning Algorithm

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon in quantum mechanics, offers a potentially transformative approach to machine learning. This paper explores the design and implementation of a novel machine learning algorithm leveraging quantum entanglement to enhance model performance, particularly when dealing with large datasets. We propose a method that encodes information through entangled states, reducing computational complexity and improving efficiency. The core mechanism centers around exploiting the unique properties of entanglement to facilitate faster and more nuanced computations. This research investigates the feasibility and potential benefits of incorporating quantum entanglement into machine learning algorithms, aiming to push the boundaries of data analysis and modeling.

DOI: 10.5281/zenodo.22250980

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