Quantum Simplicial Networks, variational quantum circuits acting on simplicial complexes, outperform classical simplicial neural networks on two synthetic classification benchmarks, per the authors.
A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
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This paper explores an innovative fusion of Quantum Computing (QC) and Artificial Intelligence (AI) through the development of a Hybrid Quantum Graph Convolutional Neural Network (HQGCNN), combining a Graph Convolutional Neural Network (GCNN) with a Quantum Multilayer Perceptron (MLP). The study highlights the potentialities of GCNNs in handling global-scale dependencies and proposes the HQGCNN for predicting complex phenomena such as the Oceanic Nino Index (ONI). Preliminary results suggest the model potential to surpass state-of-the-art (SOTA). The code will be made available with the paper publication.
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Quantum Simplicial Neural Networks
Quantum Simplicial Networks, variational quantum circuits acting on simplicial complexes, outperform classical simplicial neural networks on two synthetic classification benchmarks, per the authors.