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Quantum-Guided Constraint Optimization (QGCO)

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

Quantum-Guided Constraint Optimization (QGCO) presents a novel approach to solving complex optimization problems by integrating quantum annealing with constraint optimization techniques. This paper explores the potential of leveraging quantum annealing to accelerate the search process while simultaneously enforcing constraints, resulting in enhanced solution quality. We delve into the theoretical framework, algorithmic implementation, and preliminary results demonstrating the efficacy of this method. The core claim focuses on the synergistic combination of quantum annealing and constraint optimization to achieve a significant improvement in optimization speed and solution quality compared to traditional methods. This research addresses limitations of existing techniques by providing a robust and efficient framework for tackling challenging optimization problems across diverse domains.

DOI
10.5281/zenodo.22628703

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