Title: Adaptive Graph Quantum Probability Model

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces an adaptive graph quantum probability model, designed to automatically learn data distributions and enhance the efficiency of quantum algorithms. Traditional probabilistic models rely on manual parameter definition, while this work leverages graph structure and relationships to learn data distributions, employing graph neural networks for model explanation. The core mechanism involves utilizing a graph to automatically learn data distribution and incorporating graph neural networks to facilitate model interpretability. The research addresses the limitations of conventional methods by automating the learning process and providing insights into the model's internal structure. We demonstrate the effectiveness of this approach through the implementation of a novel quantum algorithm and its performance comparison with existing methods. The study explores how graph structure can be effectively leveraged to improve the performance of quantum computations.

DOI: 10.5281/zenodo.22480742

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