Quantum Entanglement-Driven Adaptive Machine Learning Framework

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon in quantum mechanics, holds the potential for revolutionary advancements in computation and information processing. This paper explores the application of quantum entanglement to the development of a novel self-adaptive machine learning framework. Our approach leverages the unique properties of entangled quantum states to dynamically adjust the parameters of a machine learning model, enabling it to adapt seamlessly to changing input data. The core mechanism centers around utilizing entanglement to represent and manipulate the model's parameters in a way that promotes robustness and improved generalization. The proposed framework demonstrates a significant improvement in model performance compared to traditional methods, particularly in scenarios involving dynamic datasets. We present a detailed mathematical framework outlining the core principles of this innovative approach, including the quantum state representation, entanglement manipulation, and the resulting model adaptation process. This work represents a significant step toward utilizing quantum mechanics for enhanced machine learning capabilities.

DOI: 10.5281/zenodo.22193884

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