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

TL;DR

This paper explores circuit-based hybrid Quantum Convolutional Neural Networks for remote sensing image classification, demonstrating their superior performance over classical models on the EuroSAT dataset and highlighting the benefits of quantum entanglement.

Contribution

It introduces a novel hybrid QCNN architecture for remote sensing classification and provides experimental evidence of its improved accuracy over classical CNNs.

Findings

QCNNs outperform classical CNNs in LULC classification

Quantum entanglement circuits yield the best classification scores

The approach demonstrates the potential of quantum computing in Earth Observation tasks

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.