Characterizes constituents of n-qubit graph quantum ML models and supplies a toolbox enabling integration with classical models, generalization of prior GQML approaches, and classical pre-training.
Pearce-Crump, Connecting permutation equivariant neural networks and partition diagrams, arXiv preprint arXiv:2212.08648 (2022)
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Quantum machine learning models for graphs
Characterizes constituents of n-qubit graph quantum ML models and supplies a toolbox enabling integration with classical models, generalization of prior GQML approaches, and classical pre-training.