Quantum Algorithm for Constraint Satisfaction with Dynamic Constraints

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm for constraint satisfaction problems (CSPs) designed to dynamically adjust constraints during the search process. Traditional CSP solvers often rely on static constraints, limiting their ability to effectively explore the solution space. This work introduces a reinforcement learning agent that learns optimal constraint adjustments, thereby enabling a more adaptive and potentially more efficient search strategy. We explore the implementation of this agent within a quantum framework and demonstrate its effectiveness on a benchmark CSP. The core mechanism centers around a quantum state representation and a reinforcement learning algorithm optimized for constraint transformation. The resulting algorithm offers a potential advancement over static approaches by incorporating continuous, adaptive constraint manipulation.

DOI: 10.5281/zenodo.22290291

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