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Evaluating Variational Quantum Circuit Designs for Knowledge Graph Completion

Knowledge graphs (KGs) are informative knowledge bases for various applications such as semantic web search or question answering. Since KGs often have missing facts mainly due to large construction costs, KG completion is a fundamental problem. Nowadays, as KGs have become large-scale, KG completion has become gradually intractable for classical computers. One promising direction is to utilize quantum circuits to capture complex structural information that entities and relations in KGs have. By encoding entities into quantum states and representing relations between entities as quantum circuits running on the quantum states, it becomes able to score and predict plausible missing entities or relations. However, it is still unclear how to design the quantum circuits to enhance KG completion accuracy. In this paper, we evaluate variations of quantum circuits in terms of KG completion accuracy, and investigate the relationship between the accuracy and the quality of the circuits (e.g., expressibility and entangling capacity). The experiments using a real KG show some correlation between the completion accuracy and the goodness of the circuits.