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@INPROCEEDINGS{Ghosh:1029394,
author = {Ghosh, Raktim and Delilbasic, Amer and Cavallaro, Gabriele
and Bovolo, Francesca},
title = {{A} {H}ybrid {Q}uantum-{C}lassical {CNN} {A}rchitecture for
{S}emantic {S}egmentation of {R}adar {S}ounder {D}ata},
publisher = {IEEE},
reportid = {FZJ-2024-05102},
pages = {366-370},
year = {2024},
abstract = {The 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.},
month = {Apr},
date = {2024-04-15},
organization = {2024 IEEE Mediterranean and
Middle-East Geoscience and Remote
Sensing Symposium (M2GARSS), Oran
(Algeria), 15 Apr 2024 - 17 Apr 2024},
cin = {JSC},
cid = {I:(DE-Juel1)JSC-20090406},
pnm = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
(SDLs) and Research Groups (POF4-511)},
pid = {G:(DE-HGF)POF4-5111},
typ = {PUB:(DE-HGF)8},
doi = {10.1109/M2GARSS57310.2024.10537440},
url = {https://juser.fz-juelich.de/record/1029394},
}