Quantum Entangled Learning: A Probabilistic Structure Optimization Framework

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Entangled Learning (QEL) offers a novel approach to machine learning, leveraging quantum entanglement to enhance learning efficiency and generalization ability. This paper proposes a new probabilistic structure optimization framework for QEL, focusing on the dynamic adjustment of entanglement patterns to adapt to varying data types and task specifications. We demonstrate the framework's effectiveness through a series of simulations and experiments, showcasing improved learning speed and robustness across diverse datasets. The core mechanism involves integrating probability logic with a customizable entanglement model, enabling tailored optimization strategies. The resulting system exhibits superior performance compared to existing methods, representing a significant advancement in the application of quantum principles to machine learning.

DOI: 10.5281/zenodo.22611743

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