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Quantum-Enhanced Iterative Constraint Optimization

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

Quantum-enhanced iterative constraint optimization addresses the limitations of classical optimization techniques by utilizing quantum algorithms to accelerate the search for optimal solutions. This paper explores the potential of quantum annealing and QAOA to improve convergence rates in complex constraint satisfaction problems. We detail the core mechanism, outlining the design of a quantum optimization framework and the rationale behind its implementation. The investigation focuses on leveraging quantum parallelism to effectively navigate the solution space, ultimately aiming to provide a significant advancement in the efficiency of iterative constraint optimization. This research presents a novel approach to tackling challenges inherent in optimization, particularly those involving intricate constraints and high-dimensional spaces.

DOI
10.5281/zenodo.22326141

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