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Article

Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer

by 1,*,† and 1,2,†
1
German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
2
Department of Computers, Politehnica University of Bucharest (UPB), 060042 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Current address: DLR, Münchener Straße 20, 82234 Weßling, Germany.
Academic Editor: Jian-Qiang You
Electronics 2021, 10(20), 2482; https://doi.org/10.3390/electronics10202482
Received: 30 August 2021 / Revised: 3 October 2021 / Accepted: 11 October 2021 / Published: 12 October 2021
(This article belongs to the Special Issue Quantum Computing System Design and Architecture)
Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM poses a quadratic programming problem, and quantum computers including quantum annealers (QA) as well as gate-based quantum computers promise to solve an SVM more efficiently than a conventional computer; training the SVM by employing a quantum computer/conventional computer represents a quantum SVM (qSVM)/classical SVM (cSVM) application. However, quantum computers cannot tackle many practical EO problems by using a qSVM due to their very low number of input qubits. Hence, we assembled a coreset (“core of a dataset”) of given EO data for training a weighted SVM on a small quantum computer, a D-Wave quantum annealer with around 5000 input quantum bits. The coreset is a small, representative weighted subset of an original dataset, and its performance can be analyzed by using the proposed weighted SVM on a small quantum computer in contrast to the original dataset. As practical data, we use synthetic data, Iris data, a Hyperspectral Image (HSI) of Indian Pine, and a Polarimetric Synthetic Aperture Radar (PolSAR) image of San Francisco. We measured the closeness between an original dataset and its coreset by employing a Kullback–Leibler (KL) divergence test, and, in addition, we trained a weighted SVM on our coreset data by using both a D-Wave quantum annealer (D-Wave QA) and a conventional computer. Our findings show that the coreset approximates the original dataset with very small KL divergence (smaller is better), and the weighted qSVM even outperforms the weighted cSVM on the coresets for a few instances of our experiments. As a side result (or a by-product result), we also present our KL divergence findings for demonstrating the closeness between our original data (i.e., our synthetic data, Iris data, hyperspectral image, and PolSAR image) and the assembled coreset. View Full-Text
Keywords: coreset assembly; quantum support vector machines; hyperspectral images; PolSAR images; quantum machine learning coreset assembly; quantum support vector machines; hyperspectral images; PolSAR images; quantum machine learning
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MDPI and ACS Style

Otgonbaatar, S.; Datcu, M. Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer. Electronics 2021, 10, 2482. https://doi.org/10.3390/electronics10202482

AMA Style

Otgonbaatar S, Datcu M. Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer. Electronics. 2021; 10(20):2482. https://doi.org/10.3390/electronics10202482

Chicago/Turabian Style

Otgonbaatar, Soronzonbold, and Mihai Datcu. 2021. "Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer" Electronics 10, no. 20: 2482. https://doi.org/10.3390/electronics10202482

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