Quantum Graph Learning: Frontiers and Outlook

2023/02/02 by Shuo Yu, Yu, Shuo, Ciyuan Peng +8 · 3 citations
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #FOS: Computer and information sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum and electron transport phenomena

paper · pdf · doi:10.48550/arxiv.2302.00892

openalex publication_date 2023/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy. This survey investigates the current advances in quantum graph learning (QGL) from three perspectives, i.e., underlying theories, methods, and prospects. We first look at QGL and discuss the mutualism of quantum theory and graph learning, the specificity of graph-structured data, and the bottleneck of graph learning, respectively. A new taxonomy of QGL is presented, i.e., quantum computing on graphs, quantum graph representation, and quantum circuits for graph neural networks. Pitfall traps are then highlighted and explained. This survey aims to provide a brief but insightful introduction to this emerging field, along with a detailed discussion of frontiers and outlook yet to be investigated.

Cited by

• Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing
• Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
• Learnable quantum spectral filters for hybrid graph neural networks

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