The paper presents an extension of the quantum embeddings of (Gang et al., 2019) -- embeddings that allow for logical expressions to be evaluated. The main contributions are to allow for inductive learning of quantum embeddings, and the design of an algorithm that is significantly faster than the previous one. The experiments show promising results on a fine-grained classification task. The reviewers agreed that the paper presents a solid contribution and the rebuttal answered the reviewers concerns.

Quantum logic inspired embedding (aka Quantum Embedding (QE)) of a Knowledge-Base (KB) was proposed recently by Garg:2019. It is claimed that the QE preserves the logical structure of the input KB given in the form of unary and binary predicates hierarchy. Such structure preservation allows one to perform Boolean logic style deductive reasoning directly over these embedding vectors. The original QE idea, however, is limited to the transductive (not inductive) setting. Moreover, the original QE scheme runs quite slow on real applications involving millions of entities. We start by reformulating the original QE problem to allow for the induction.