Corpus ID: 237572283

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

@article{Sebastianelli2021OnCH,
  title={On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification},
  author={Alessandro Sebastianelli and Daniela A. Zaidenberg and Dario Spiller and B. L. Saux and Silvia Liberata Ullo},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.09484}
}
This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used… Expand

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