Quantum-Enhanced Constraint Optimization for Complex Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This research investigates the potential of quantum algorithms to enhance constraint optimization techniques applied to complex networks. Complex networks, characterized by interconnected nodes and relationships, present significant challenges for optimization problems. Classical methods often struggle with scalability and require extensive computational resources. This study proposes a novel approach leveraging quantum annealing and gate operations to improve efficiency and accuracy in solving optimization problems involving these networks. The core mechanism involves iteratively refining network structure and constraints using quantum optimization, offering a potential paradigm shift in this domain. The study explores the advantages of quantum algorithms over classical methods in the context of complex network optimization, aiming to demonstrate a significant improvement in performance. This work presents a framework for applying quantum optimization to complex network problems, and it's designed to be a foundation for future research into quantum-enhanced optimization.

DOI: 10.5281/zenodo.22191712

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