  Semantic knowledge graphs are large-scale triple-oriented databases for
knowledge representation and reasoning. Implicit knowledge can be inferred by
modeling and reconstructing the tensor representations generated from knowledge
graphs. However, as the sizes of knowledge graphs continue to grow, classical
modeling becomes increasingly computational resource intensive. This paper
investigates how quantum resources can be capitalized to accelerate the
modeling of knowledge graphs. In particular, we propose the first quantum
machine learning algorithm for making inference on tensorized data, e.g., on
knowledge graphs. Since most tensor problems are NP-hard, it is challenging to
devise quantum algorithms to support that task. We simplify the problem by
making a plausible assumption that the tensor representation of a knowledge
graph can be approximated by its low-rank tensor singular value decomposition,
which is verified by our experiments. The proposed sampling-based quantum
algorithm achieves exponential speedup with a runtime that is polylogarithmic
in the dimension of knowledge graph tensor.
