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| Hauptseite > Publikationsdatenbank > A Hybrid Quantum-Classical CNN Architecture for Semantic Segmentation of Radar Sounder Data |
| Hauptseite > Publikationsdatenbank > A Hybrid Quantum-Classical CNN Architecture for Semantic Segmentation of Radar Sounder Data |
| Contribution to a conference proceedings | FZJ-2024-05102 |
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2024
IEEE
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Please use a persistent id in citations: doi:10.1109/M2GARSS57310.2024.10537440 doi:10.34734/FZJ-2024-05102
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.
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