A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction

Francesco Mauro; Alessandro Sebastianelli; Bertrand Le Saux; Paolo Gamba; Silvia Liberata Ullo

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arXiv · 2401.16049

A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction

Francesco Mauro·Alessandro Sebastianelli·Bertrand Le Saux·Paolo Gamba·Silvia Liberata Ullo

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Abstract

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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Francesco Mauro, Alessandro Sebastianelli, Bertrand Le Saux, Paolo Gamba, Silvia Liberata Ullo. 2024-01-29. A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction. https://arxiv.org/abs/2401.16049

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