Quantum Entanglement Information Encoding Adaptive Optimization Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement offers a unique paradigm for information encoding, potentially enabling novel computational paradigms. This paper investigates the application of quantum entanglement to an adaptive optimization algorithm, aiming to surpass the limitations of traditional optimization methods in complex problem domains. We propose a novel approach leveraging the inherent properties of entangled states to dynamically adjust optimization parameters, thereby facilitating the discovery of global optima. The core mechanism centers around the manipulation of entangled states to guide the search process, leading to improved performance. The significance of this research lies in exploring a potential paradigm shift in optimization, driven by the fundamental principles of quantum mechanics.

DOI: 10.5281/zenodo.22301746

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