Title: Quantum-Enhanced Constraint Programming with Adaptive Semantics

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-enhanced constraint programming aims to significantly improve the efficiency and effectiveness of constraint satisfaction problems (CSPs) by leveraging quantum algorithms for constraint propagation. This paper proposes a novel technique that dynamically adjusts the semantics of the constraint system during the solution process, optimizing the search space and ultimately enhancing solution quality. We introduce a quantum-enhanced constraint propagation algorithm that explores different constraint representations and refines the solution space using quantum exploration. The core mechanism involves using quantum algorithms to identify promising constraint representations and iteratively refine them based on the results of quantum simulations. This approach offers the potential for substantial performance gains compared to traditional constraint programming methods, particularly for complex CSPs. The paper details the algorithm's design, experimental results, and analysis of its performance characteristics. The focus is on demonstrating the benefits of adaptive semantics in constraint programming through the application of quantum algorithms.

DOI: 10.5281/zenodo.22158973

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