Quantum Convolutional Circuits for Earth Observation Image Classification | IEEE Conference Publication | IEEE Xplore

Quantum Convolutional Circuits for Earth Observation Image Classification


Abstract:

The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resou...Show More

Abstract:

The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Earth Observations, where Machine Learning (ML) plays an important role due to its extensive amount of data to be manipulated. This paper presents our preliminary results of binary quantum classifiers, which consist of Quantum Convolutional Neural Networks (QCNNs), applied on Earth Observation datasets, EuroSAT and SAT4, with classically-reduced features. Especially, we compare the performance of different data embedding techniques and quantum circuits for binary classification tasks.
Date of Conference: 17-22 July 2022
Date Added to IEEE Xplore: 28 September 2022
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Conference Location: Kuala Lumpur, Malaysia

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