Quantum-Enhanced Constraint Satisfaction Problem Solving

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of quantum algorithms to revolutionize the field of constraint satisfaction problems (CSPs). CSPs represent a significant challenge in artificial intelligence, particularly in areas like puzzle-solving and optimization. Traditional computational approaches struggle with the vast search space inherent in many CSPs, leading to computationally intensive and time-consuming solutions. This work proposes a novel quantum approach, utilizing Grover's algorithm on quantum state representations, to accelerate the search process and enhance the efficiency of solving these problems. We will detail the theoretical underpinnings of the proposed algorithm, its expected advantages over classical methods, and preliminary experimental results demonstrating its potential for improved performance. The core claim is that quantum-enhanced CSP solving, specifically through Grover's algorithm, can significantly reduce the time required to find solutions within a reasonable computational constraint.

DOI: 10.5281/zenodo.22277004

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