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| Contribution to a conference proceedings | FZJ-2024-05824 |
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2024
IEEE
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Please use a persistent id in citations: doi:10.1109/IGARSS53475.2024.10642188 doi:10.34734/FZJ-2024-05824
Abstract: This article presents a hybrid quantum-classical framework by incorporating quantum feature maps into a classical Convolutional Neural Network (CNN) architecture for detecting different subsurface targets in radar sounder signals. The quantum feature maps are generated by quantum circuits to utilize spatially-bound input information from the training samples. The associated spectral probabilistic amplitudes of the feature maps are further fed into the classical CNN-based network to classify the subsurface targets in the radargram. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of enhancing the classical architecture through quantum feature maps for characterizing radar sounder data.
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