Algorithmic Quantum Sampling for Complex Network Analysis

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of algorithmic quantum sampling to enhance the analysis of complex networks. Complex networks, characterized by their intricate relationships between nodes and edges, offer immense opportunities for understanding societal, economic, and biological systems. However, traditional computational methods struggle to efficiently explore the vast state space of these networks, limiting our ability to uncover subtle patterns and correlations. This research proposes a novel quantum sampling algorithm designed to overcome these limitations, leveraging quantum superposition to rapidly explore the network's potential states and facilitate the discovery of hidden structures. The core mechanism involves employing quantum superposition to effectively sample the entire network state space, significantly accelerating the analysis process. We demonstrate the algorithm's effectiveness through simulations and preliminary results, highlighting its potential to provide a fundamentally new approach to complex network analysis.

DOI: 10.5281/zenodo.22273932

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