Quantum Entanglement-Based Adaptive Machine Learning Model

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon where two or more particles become linked and share the same fate, presents a potentially revolutionary approach to machine learning. This paper explores the application of quantum entanglement to design a novel adaptive machine learning model, aiming to improve accuracy through dynamic parameter adjustments guided by quantum entanglement. We outline the core mechanism, detailing how entanglement facilitates faster information transfer, ultimately reducing computational complexity and potentially enhancing model performance. The study begins with a comprehensive overview of the challenges and opportunities presented by quantum entanglement in machine learning, followed by a detailed explanation of the proposed model's architecture and the underlying principles of entanglement-based optimization. We conclude by discussing the potential implications and future research directions of this approach.

DOI: 10.5281/zenodo.22281171

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