Title: Quantum Entanglement-Based Adaptive Algorithm Optimizer

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the potential of quantum entanglement to revolutionize algorithm optimization. We propose a novel adaptive algorithm optimizer leveraging quantum entanglement to accelerate computations and improve optimization results. The core mechanism hinges on exploiting the unique properties of entangled states to expedite key computational steps, offering a fundamentally different approach compared to traditional optimization methods. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of this approach in a specific, complex optimization problem. The paper concludes with a discussion of the challenges and future directions in this rapidly evolving field.

DOI: 10.5281/zenodo.22448158

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