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001029394 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05102
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001029394 1001_ $$0P:(DE-HGF)0$$aGhosh, Raktim$$b0
001029394 1112_ $$a2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)$$cOran$$d2024-04-15 - 2024-04-17$$wAlgeria
001029394 245__ $$aA Hybrid Quantum-Classical CNN Architecture for Semantic Segmentation of Radar Sounder Data
001029394 260__ $$bIEEE$$c2024
001029394 300__ $$a366-370
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001029394 520__ $$aThe article presents for the first time a hybrid quantum-classical architecture in the context of subsurface target detection in the radar sounder signal. We enhance the classical convolutional neural network (CNN) based architecture by integrating a quantum layer in the latent space. We investigate two quantum circuits with the classical neural networks by exploiting fundamental properties of quantum mechanics such as entanglement and superposition. The proposed hybrid architecture is used for the downstream task of patch-wise semantic segmentation of radar sounder subsurface images. Experimental results on the MCoRDS and MCoRDS3 datasets demonstrated the capability of the hybrid quantum-classical approach for radar sounder information extraction.
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001029394 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b2$$ufzj
001029394 7001_ $$0P:(DE-HGF)0$$aBovolo, Francesca$$b3
001029394 773__ $$a10.1109/M2GARSS57310.2024.10537440
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