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

This paper explores an innovative fusion of Quantum Computing (QC) andArtificial Intelligence (AI) through the development of a Hybrid Quantum GraphConvolutional Neural Network (HQGCNN), combining a Graph Convolutional NeuralNetwork (GCNN) with a Quantum Multilayer Perceptron (MLP). The study highlightsthe potentialities of GCNNs in handling global-scale dependencies and proposesthe HQGCNN for predicting complex phenomena such as the Oceanic Nino Index(ONI). Preliminary results suggest the model potential to surpassstate-of-the-art (SOTA). The code will be made available with the paperpublication.