Quantum-Enhanced Constraint Programming for Robotics

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-enhanced constraint programming (QCP) offers a promising avenue for enhancing the efficiency and robustness of robot control algorithms. This paper investigates the application of quantum algorithms to constraint programming, focusing on improvements to solution space exploration and optimization. We introduce a novel quantum-enhanced constraint programming framework, leveraging quantum superposition and entanglement to accelerate search and potentially identify optimal solutions. The core mechanism involves employing quantum annealing for the search phase and quantum optimization for the constraint satisfaction process. We demonstrate the potential benefits of this approach, particularly in complex robot control scenarios, through theoretical analysis and preliminary simulations. The research explores the impact of quantum enhancements on problem-solving time and the likelihood of finding valid solutions. This work represents an advancement toward utilizing quantum computation to address challenges in robotic control.

DOI: 10.5281/zenodo.22194141

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