QGRNN can recover Hamiltonian node parameters that encode classical features, giving high reconstruction accuracy on a small set of Iris and MNIST samples, but the evaluation is per-sample fitting rather than true prediction.
Stationary signal processing on graphs,
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Feature Prediction in Quantum Graph Recurrent Neural Networks with Applications in Information Hiding
QGRNN can recover Hamiltonian node parameters that encode classical features, giving high reconstruction accuracy on a small set of Iris and MNIST samples, but the evaluation is per-sample fitting rather than true prediction.