Quantum machine learning of graph-structured data
Kerstin Beer (Macquarie University, Leibniz Universität Hannover)
Megha Khosla (TU Delft - Multimedia Computing)
Julius Köhler (Leibniz Universität Hannover)
Tobias J. Osborne (Leibniz Universität Hannover)
Tianqi Zhao (TU Delft - Industrial Design Engineering)
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Abstract
Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that exploits the quantum source's graph structure to improve learning via an arbitrary quantum neural network (QNN) ansatz. In particular, we devise and optimize a self-supervised objective to capture the information-theoretic closeness of the quantum states in the training of a QNN. Numerical simulations show that our approach improves the learning efficiency and the generalization behavior of the base QNN. On a practical note, scalable quantum implementations of the learning procedure described in this paper are likely feasible on the next generation of quantum computing devices.
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