基于量子纠缠的自适应机器学习模型

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon where two or more particles become linked in such a way that they share the same fate no matter how far apart they are, has captivated physicists and intrigued mathematicians alike. Its potential applications extend far beyond the realm of theoretical physics, offering intriguing possibilities for advanced computation and information processing. This paper explores the use of quantum entanglement to design a novel self-adaptive machine learning model, aiming to enhance the model's adaptability and robustness. We propose a system where the model parameters are dynamically adjusted through quantum entanglement, leveraging the unique properties of entangled states to facilitate a more nuanced and efficient optimization process. The core mechanism centers around utilizing entanglement to establish a dynamic feedback loop, allowing the model to explore a wider range of parameter spaces and converge towards optimal solutions with enhanced adaptability. The research investigates the theoretical implications of this approach, examining its potential advantages over traditional machine learning techniques and highlighting the challenges involved in practical implementation. The paper concludes by discussing the preliminary results and potential future directions for this research.

DOI: 10.5281/zenodo.22193964

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