Equivariant Quantum Graph Circuits
- URL: http://arxiv.org/abs/2112.05261v1
- Date: Fri, 10 Dec 2021 00:14:12 GMT
- Title: Equivariant Quantum Graph Circuits
- Authors: P\'eter Mernyei, Konstantinos Meichanetzidis, \.Ismail \.Ilkan Ceylan
- Abstract summary: We propose equivariant quantum graph circuits (EQGCs) as a class of parameterized quantum circuits with strong inductive bias for learning over graph-structured data.
Our theoretical perspective on quantum graph machine learning methods opens many directions for further work, and could lead to models with capabilities beyond those of classical approaches.
- Score: 10.312968200748116
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate quantum circuits for graph representation learning, and
propose equivariant quantum graph circuits (EQGCs), as a class of parameterized
quantum circuits with strong relational inductive bias for learning over
graph-structured data. Conceptually, EQGCs serve as a unifying framework for
quantum graph representation learning, allowing us to define several
interesting subclasses subsuming existing proposals. In terms of the
representation power, we prove that the subclasses of interest are universal
approximators for functions over the bounded graph domain, and provide
experimental evidence. Our theoretical perspective on quantum graph machine
learning methods opens many directions for further work, and could lead to
models with capabilities beyond those of classical approaches.
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