Quantum-Enhanced Optimization of Constraint Programming

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the potential of quantum algorithms to accelerate the solution of constraint programming problems. Traditional methods often struggle with the vast solution spaces, necessitating significant computational resources. We explore the application of quantum annealing and quantum approximate optimization algorithm (QAOA) to enhance the search process, aiming to significantly reduce the time required for optimization. The core mechanism involves leveraging quantum phenomena to overcome the limitations of classical algorithms, offering a promising avenue for improved performance in constraint programming. The research focuses on the theoretical advantages and potential practical implementations of these quantum approaches, specifically examining how they can be applied to benchmark problems and demonstrate a quantifiable speedup compared to traditional methods.

DOI: 10.5281/zenodo.22266228

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