Feb 2, 2023 · This survey investigates the current advances in quantum graph learning (QGL) from three perspectives, ie, underlying theories, methods, and prospects.

Feb 2, 2023 · We first look at QGL and discuss the mutualism of quantum theory and graph learning, the specificity of graph-structured data, and the ...

Quantum theory has shown its superiority in enhancing machine learning. However, facilitating quantum theory to enhance graph learning is in its infancy.

Feb 2, 2023 · This survey investigates the current advances in quantum graph learning (QGL) from three perspectives, i.e., underlying theories, meth- ods, and ...

3 days ago · Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification? Chukwudubem Umeano, Stefano Scali, and Oleksandr Kyriienko.

Co-authors ; Quantum Graph Learning: Frontiers and Outlook. S Yu, C Peng, Y Wang, A Shehzad, F Xia, ER Hancock. arXiv preprint arXiv:2302.00892, 2023. 1, 2023.

Quantum Graph Learning: Frontiers and Outlook ... However, facilitating quantum theory to enhance graph learning is in its infancy. Graph Learning · Specificity.

D Zhang, C Peng, X Chang, F Xia. IEEE Transactions on Computational Social Systems 10 (3), 970-981, 2022. 2, 2022. Quantum Graph Learning: Frontiers and Outlook.

This article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to ...

This survey paper conducts a comparative analysis of existing works in multimodal graph learning, elucidating how multimodal learning is achieved across ...