Title: Quantum-Inspired Constraint Optimization with Adaptive Sampling

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-inspired constraint optimization is a promising approach to accelerate the discovery of optimal solutions in complex problems. This paper explores the application of adaptive sampling techniques to enhance the effectiveness of this method. We introduce a novel 'quantum-perturbed sampling' algorithm that leverages controlled randomness to guide the search process. The core mechanism centers around dynamically adjusting the sampling frequency based on the algorithm's performance, thereby improving the exploration of the solution space. The research demonstrates the potential of this approach to achieve faster convergence and better solution quality compared to traditional optimization methods. We evaluate the algorithm on a range of benchmark problems, showcasing its efficacy in tackling challenging optimization tasks.

DOI: 10.5281/zenodo.22255132

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