Quantum-Enhanced Optimization via Adaptive Constraint Distribution

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum-enhanced optimization leverages the unique properties of quantum mechanics to accelerate the solution of complex optimization problems. This paper introduces a novel algorithm, Quantum-Enhanced Optimization via Adaptive Constraint Distribution, designed to intelligently distribute constraint constraints during optimization. The core mechanism centers around dynamically adjusting constraint distribution based on the optimization state, aiming to improve convergence speed and overall efficiency. We explore the potential benefits of this approach, particularly in tackling problems exhibiting inherent complexity and requiring nuanced constraint management. The paper details the algorithm's design, its implementation using a quantum simulator, and preliminary results demonstrating improved convergence compared to conventional optimization methods. Keywords: Quantum Optimization, Constraint Distribution, Adaptive Algorithm, Quantum Simulation, Convergence, Optimization.

DOI: 10.5281/zenodo.22473205

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