Dimension Space Quantum Entanglement Adaptive Optimization Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel quantum entanglement-based adaptive optimization algorithm designed to address complex optimization problems. The core concept revolves around exploiting the instantaneous information transfer capabilities of entangled states to guide the optimization process, thereby mitigating the pitfalls of local optima and enabling global optimization. The algorithm incorporates a dynamic parameter adjustment mechanism predicated on entanglement, allowing it to adapt to environmental changes and potentially achieve superior performance compared to traditional optimization techniques. This work explores the theoretical foundations of entanglement-based optimization and demonstrates its potential for significant advancements in the field of adaptive learning and problem-solving.

DOI: 10.5281/zenodo.22635192

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