Equivariant Quantum Graph Circuits. (arXiv:2112.05261v2 [cs.LG] UPDATED)

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 …

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