  In this work, we propose the first quantum Ans\"atze for the statistical
relational learning on knowledge graphs using parametric quantum circuits. We
introduce two types of variational quantum circuits for knowledge graph
embedding. Inspired by the classical representation learning, we first consider
latent features for entities as coefficients of quantum states, while
predicates are characterized by parametric gates acting on the quantum states.
For the first model, the quantum advantages disappear when it comes to the
optimization of this model. Therefore, we introduce a second quantum circuit
model where embeddings of entities are generated from parameterized quantum
gates acting on the pure quantum state. The benefit of the second method is
that the quantum embeddings can be trained efficiently meanwhile preserving the
quantum advantages. We show the proposed methods can achieve comparable results
to the state-of-the-art classical models, e.g., RESCAL, DistMult. Furthermore,
after optimizing the models, the complexity of inductive inference on the
knowledge graphs might be reduced with respect to the number of entities.
