Quantum-Enhanced Constraint Satisfaction Problem (CSP) Solving

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-enhanced constraint satisfaction problem (CSP) solving represents a significant advancement in computational power, potentially enabling the efficient solution of complex optimization problems. This paper investigates the application of quantum annealing and quantum approximate optimization algorithms (QAOA) to tackle CSPs, aiming to demonstrate a substantial speedup compared to classical methods. We explore the core mechanism of leveraging superposition and entanglement to navigate the solution space and identify optimal solutions. The research focuses on a novel quantum algorithm designed to accelerate CSP solving, with the goal of providing a practical and efficient solution to this computationally intensive field. This work will detail the theoretical framework, implementation details, and preliminary results demonstrating the potential of quantum computing for CSP problems.

DOI: 10.5281/zenodo.22614692

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