Quantum-Guided Optimization for Non-Linear Systems

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-Guided Optimization (QGO) represents a novel approach to solving complex optimization problems, particularly those exhibiting non-linear characteristics. This paper introduces a framework for optimizing non-linear systems utilizing quantum entanglement, aiming to significantly accelerate and enhance traditional optimization techniques. We detail the core mechanism of QGO, outlining its architecture, quantum algorithm, and potential benefits in terms of computational efficiency and solution quality. The proposed method leverages the unique properties of quantum entanglement to facilitate the exploration of the solution space and guide the optimization process towards optimal configurations. The paper provides a comprehensive discussion of the theoretical foundations, implementation details, and preliminary experimental results demonstrating the efficacy of QGO.

DOI: 10.5281/zenodo.22250816

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