A Variational Quantum Circuit Model for Knowledge Graph Embeddings

MCML Authors

Yunpu Ma

Dr.

→ Group Volker Tresp
 Database Systems, Data Mining and AI
→ Co-Group Hinrich Schütze

Volker Tresp

Prof. Dr.

Core PI

Database Systems, Data Mining and AI

Abstract

Can quantum computing resources facilitate representation learning? In this work, we propose the first quantum Ansatz for statistical relational learning on knowledge graphs using parametric quantum circuits. We propose a variational quantum circuit for modeling knowledge graphs by introducing quantum representations of entities. In particular, latent representations of entities are encoded as coefficients of quantum states, while predicates are characterized by parametric gates acting on the quantum states. We show that quantum representations can be trained efficiently meanwhile preserving the quantum advantages. Simulations on classical machines with different datasets show that our proposed quantum circuit Ansatz and quantum representations 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 can be reduced with respect to the number of entities.

inproceedings MT20

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QTNML @NeurIPS 2020

1st Workshop on Quantum Tensor Networks in Machine Learning at the 34th Conference on Neural Information Processing Systems. Virtual, Dec 06-12, 2020.

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Authors

Y. Ma • V. Tresp

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Links

PDF

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Research Area

 A3 | Computational Models