Adaptive Machine Learning Algorithm Based on Quantum Entanglement

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces an adaptive machine learning algorithm leveraging quantum entanglement to enhance learning and inference capabilities. Traditional machine learning methods often rely on classical information, but this novel approach utilizes quantum entanglement to achieve more efficient learning and reasoning, enabling complex tasks. The core mechanism centers around designing an algorithm that exploits the inherent fragility of quantum entanglement for improved model performance. We present a theoretical framework and preliminary experimental validation demonstrating the algorithm's effectiveness in a range of machine learning scenarios. This work explores the potential of quantum entanglement to revolutionize machine learning, offering a pathway towards more robust and efficient models.

DOI: 10.5281/zenodo.22624576

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