Quantum Graph Learning: Frontiers and Outlook
- URL: http://arxiv.org/abs/2302.00892v1
- Date: Thu, 2 Feb 2023 05:53:31 GMT
- Title: Quantum Graph Learning: Frontiers and Outlook
- Authors: Shuo Yu, Ciyuan Peng, Yingbo Wang, Ahsan Shehzad, Feng Xia, Edwin R.
Hancock
- Abstract summary: facilitating quantum theory to enhance graph learning is in its infancy.
We first look at QGL and discuss the mutualism of quantum theory and graph learning.
A new taxonomy of QGL is presented, i.e., quantum computing on graphs, quantum graph representation, and quantum circuits for graph neural networks.
- Score: 14.1772249363715
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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