Adaptive Machine Learning Based on Quantum Entanglement

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel machine learning algorithm, Adaptive Machine Learning Based on Quantum Entanglement, that leverages the unique properties of quantum entanglement to enhance learning and inference processes. Traditional machine learning algorithms often rely on classical information, limiting their potential for complex tasks. We propose a method utilizing quantum entanglement to create a more efficient and robust learning paradigm. The core mechanism involves designing a quantum circuit that dynamically adjusts its parameters based on entangled data, allowing the algorithm to effectively explore the solution space and discover optimal solutions with greater speed and accuracy. This work demonstrates the potential of quantum entanglement to significantly improve performance in several machine learning scenarios.

DOI: 10.5281/zenodo.22625130

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