Quantum Entanglement Adaptive Machine Learning Model

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon where two particles become linked and share the same fate regardless of distance, has captivated scientists and philosophers alike. This research explores the potential of utilizing quantum entanglement to enhance machine learning models, specifically focusing on the concept of adaptive learning and automated optimization. Traditional machine learning algorithms often struggle to adapt to evolving data distributions, leading to suboptimal performance. This paper proposes a novel approach leveraging the inherent correlations within entangled quantum states to dynamically adjust model parameters and optimize the learning process. The core mechanism centers around exploiting the non-classical correlations to introduce a form of self-correction and continuous refinement, potentially surpassing the limitations of classical machine learning techniques. This work investigates the theoretical implications, preliminary experimental setups, and potential applications of this quantum-inspired learning paradigm. The resulting model demonstrates enhanced performance across a range of machine learning tasks, suggesting a significant advancement in adaptive learning capabilities.

DOI: 10.5281/zenodo.22193405

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