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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Comparison of Quantum Neural Network Algorithms For Earth Observation Data Classification

Matthijs van Waveren, Mickaël Savinaud, Guillaume Pasero, Veronique Defonte, Pierre-Marie Brunet, Orphée Faucoz, Piotr Gawron, Bartłomiej Gardas, Zbigniew Puchała, Łukasz Pawela

Published Jul 16, 2023• 6 citations
Publisher

Abstract

This article describes a practical Earth Observation use case that would benefit from quantum computing. We analyze three quantum neural network algorithms. We implemented one of the algorithms on the EuroSAT dataset. We compare the algorithms with respect to complexity and degree of quantization. We believe that the algorithms we propose would be useful for the remote sensing community when quantum computing technologies become widely available.1

DOI
10.1109/igarss52108.2023.10281429

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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.
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Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.
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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 convolutional neural networks 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 classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the 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 future investigations.