| Article | |
| Title | Quantum Convolutional Circuits for Earth Observation Image Classification |
| Author(s) | Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Le Saux, Bertrand (European Space Agency) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN) |
| Publication | 2022 |
| Number of pages | 4 |
| In: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 Jul 2022, pp.4907-4910 |
| DOI | 10.1109/IGARSS46834.2022.9883992 |
| Subject category | Quantum Technology |
| 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. |
| Copyright/License | © 2022-2025 IEEE |
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Record created 2023-06-07, last modified 2023-06-07
