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Quantum-Enhanced Constraint Optimization via Adaptive Resonance

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to constraint optimization addressing the limitations of traditional methods. We introduce a quantum-enhanced constraint optimization algorithm leveraging adaptive resonance to dynamically adjust constraint parameters. The core mechanism involves a quantum process that analyzes the solution space and automatically optimizes constraint parameters to improve solution quality and robustness. This algorithm offers a potential improvement over existing methods by dynamically adapting to the problem's characteristics, potentially leading to more efficient and reliable optimization results. The paper details the algorithm's design, implementation, and preliminary results, demonstrating its effectiveness in solving a challenging optimization problem.

DOI
10.5281/zenodo.22441014

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