Title: Quantum Complexity Networks (QCNs) - Integrated with Bayesian Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Complexity Networks (QCNs) represent a novel framework for network design, leveraging the principles of quantum computing and Bayesian optimization to dynamically adjust network topology. This paper explores the integration of Bayesian optimization directly into the QCN architecture, enabling adaptive network modification based on measured data and uncertainty. The core claim is to extend the concept of QCNs to encompass a probabilistic, evolving system where the network's connectivity and weightings are continuously refined through Bayesian optimization applied to a physical system, such as a material's microstructure. This research addresses a critical gap in network design, offering a robust and adaptive approach that surpasses traditional static network models. The benefits of this integration include enhanced robustness, improved adaptability to dynamic environments, and a potential pathway to uncovering complex system behaviors.

DOI: 10.5281/zenodo.22625554

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