Quantum-Enhanced Constraint Programming for Complex Constraint Satisfaction

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum-enhanced constraint programming (QCP) offers a promising avenue for accelerating the solution of complex constraint satisfaction problems (CSPs) by harnessing the unique properties of quantum computation. This paper investigates the potential of integrating quantum algorithms into constraint programming to achieve exponential speedups in solving problems with large state spaces. We explore the core mechanisms involved, focusing on quantum superposition and entanglement to represent and manipulate constraint relationships, and outline a theoretical framework for demonstrating the benefits of this approach. We present a novel design for leveraging quantum entanglement to enhance the exploration of solution space, ultimately aiming to significantly improve the performance of existing CSP solvers. The paper concludes by discussing the challenges and future directions of QCP research.

DOI: 10.5281/zenodo.22300125

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