The growing complexity of Earth observation data demands innovative approaches for efficient processing and analysis. Quantum convolutional neural networks integrate quantum computing principles into classical deep learning architectures, offering enhanced feature extraction capabilities. Building on the Quanv4EO framework, this study introduces a trainable quanvolutional layer within a hybrid neural network, enabling dynamic parameter optimization during training. Experiments using the EuroSAT dataset demonstrate that the trainable quanvolution surpasses both its non-trainable quantum and the classical counterparts, achieving a higher accuracy value with fewer parameters. These findings underscore the potential of trainable quantum models for earth observation applications.
ADVANCING EARTH OBSERVATION WITH TRAINABLE QUANVOLUTIONAL NEURAL NETWORKS FOR CLASSIFICATION TASKS
Gamba P. E.;
2025-01-01
Abstract
The growing complexity of Earth observation data demands innovative approaches for efficient processing and analysis. Quantum convolutional neural networks integrate quantum computing principles into classical deep learning architectures, offering enhanced feature extraction capabilities. Building on the Quanv4EO framework, this study introduces a trainable quanvolutional layer within a hybrid neural network, enabling dynamic parameter optimization during training. Experiments using the EuroSAT dataset demonstrate that the trainable quanvolution surpasses both its non-trainable quantum and the classical counterparts, achieving a higher accuracy value with fewer parameters. These findings underscore the potential of trainable quantum models for earth observation applications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
