Title: Quantum-Inspired Constraint Optimization with Adaptive Noise

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-inspired constraint optimization presents a novel approach to solving complex optimization problems by leveraging quantum-mechanical principles. This paper introduces a framework for optimizing systems using quantum-inspired algorithms, incorporating adaptive noise to mitigate the risk of local optima. The core mechanism involves a 'quantum state exploration' – where the algorithm explores the solution space using quantum principles, guided by adaptive noise. This approach aims to overcome limitations of traditional optimization methods, particularly in scenarios where classical methods struggle to find optimal solutions. The study highlights the potential of quantum-inspired algorithms to enhance the robustness and efficiency of optimization processes, offering a promising pathway for tackling complex challenges across diverse domains.

DOI: 10.5281/zenodo.22332016

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