Abstract: The knowledge graph completion task completes the knowledge graph by predicting missing facts in the knowledge graph.The quantum-based knowledge graph embedding(KGE) model uses variational quantum circuits to score triples by mea-suring the probability distribution of qubit states,and triples with high scores are the missing facts.But the current quantum-based KGE either loses the quantum advantage in the optimization process and the matrix unitary property is destroyed,or requires a large number of parameters for storing quantum states,resulting in overfitting and low performance.Furthermore,these methods ignore the theoretical analysis that is essential for understanding model performance.In order to solve the performance problem and bridge the theoretical gap,we propose QubitE:entities are embedded as qubits(unit complex vectors),relations are embedded as quantum gates(unit unitary matrices),the scoring process is complex matrix multiplication,and kernel methods are used for optimization.The parameterization method of the model can maintain the quantum advantage in optimization,the space-time complexity is linear,and it can even further realize semantic-based quantum logic calculation.In addition,the model can be proved to be fully expressive,relational schema reasoning ability and inclusiveness,etc.theoretically,which is helpful to understand the model performance.Experiments show that QubitE can achieve results comparable to state-of-the-art classical models on some benchmark knowledge graphs.

Key words: Knowledge graph, Knowledge graph completion, Knowledge graph embedding, Representation learning, Qubit