A Hybrid MLP-Quantum approach in Graph Convolutional Neural Networks for Oceanic Nino Index (ONI) prediction
arxiv(2024)
摘要
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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