On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

Abstract

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used as reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.

Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network

“…Besides feature engineering algorithms, applying classical deep learning algorithms for feature extraction is also investigated. Sebastianelli et al [4] apply a classical CNN to extract high-level features from images and a quantum circuit for the final prediction. However, note that many studies utilize classical algorithms for image comprehension and feature extraction that are more computationally expensive than the final prediction step.…”