Quantum Entanglement-Based Adaptive Algorithm Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum entanglement, a peculiar phenomenon in quantum mechanics, has captivated scientists and philosophers alike. Its potential for manipulating and measuring states beyond classical understanding has spurred significant research into its applications, particularly in optimization problems. This paper investigates the application of quantum entanglement to a novel adaptive algorithm designed to optimize complex machine learning models, specifically addressing the challenge of dynamic data adaptation and improved generalization. We propose a framework utilizing entangled parameters to dynamically adjust model parameters based on input data shifts, demonstrating a significant improvement over traditional optimization techniques in this context. The core mechanism centers around leveraging the inherent, self-organizing properties of entangled states to facilitate continuous parameter refinement and enhanced model robustness. The paper details the algorithm's architecture, the underlying quantum principles, and preliminary experimental results showcasing its efficacy in optimizing a specific, complex machine learning model. We conclude with a discussion of the potential implications and future research directions.

DOI: 10.5281/zenodo.22274111

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