Quantum-Inspired Constraint Optimization via Hybrid Neural Networks

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired neural networks to constraint optimization, aiming to address the limitations of traditional optimization methods when faced with complex, high-dimensional problems. We propose a novel hybrid approach that integrates quantum-inspired neural networks into the optimization process, leveraging their ability to capture subtle patterns and relationships within the problem space. The goal is to significantly improve the performance of constraint optimization, particularly in scenarios where conventional methods fall short. The core mechanism focuses on utilizing the neural network to generate a surrogate model that facilitates faster and more efficient search, enabling the solution of challenging problems. The results demonstrate substantial improvements in optimization speed and accuracy compared to existing methods, especially when dealing with intricate constraint formulations.

DOI: 10.5281/zenodo.22277530

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