Quantum-Driven Constraint Propagation with Adaptive Prioritization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm for constraint propagation, leveraging quantum mechanics to dynamically prioritize constraints during optimization. The core mechanism centers around a quantum process that efficiently evaluates constraint impact, allowing for enhanced optimization speed and improved solution quality. We explore a method for learning a representation of the problem's complexity, enabling the algorithm to intelligently prioritize constraints and ultimately enhance the efficiency of optimization. The study demonstrates the potential for significant improvements in constraint propagation, offering a new approach to tackling complex optimization problems.

DOI: 10.5281/zenodo.22303674

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