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

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

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 lay...

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Bibliographic Details
Main Authors: Alessandro Sebastianelli, Daniela Alessandra Zaidenberg, Dario Spiller, Bertrand Le Saux, Silvia Ullo
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Earth observation (EO)
image classification
land-use and land-cover (LULC) classification
machine learning (ML)
quantum computing (QC)
quantum machine learning (QML)
Online Access:https://ieeexplore.ieee.org/document/9647979/

Internet

https://ieeexplore.ieee.org/document/9647979/

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