Fan, Fan und Shi, Yilei und Guggemos, Tobias und Zhu, Xiao Xiang (2024) Hybrid quantum-classical convolutional neural network model for image classification. IEEE Transactions on Neural Networks and Learning Systems, 35 (2), Seiten 18145-18159. IEEE. doi: 10.1109/TNNLS.2023.3312170. ISSN 2162-237X.
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Offizielle URL: https://ieeexplore.ieee.org/document/10254235
Kurzfassung
Image classification plays an important role in remote sensing. Earth observation (EO) has inevitably arrived in the big data era, but the high requirement on computation power has already become a bottleneck for analyzing large amounts of remote sensing data with sophisticated machine learning models. Exploiting quantum computing might contribute to a solution to tackle this challenge by leveraging quantum properties. This article introduces a hybrid quantum-classical convolutional neural network (QC-CNN) that applies quantum computing to effectively extract high-level critical features from EO data for classification purposes. Besides that, the adoption of the amplitude encoding technique reduces the required quantum bit resources. The complexity analysis indicates that the proposed model can accelerate the convolutional operation in comparison with its classical counterpart. The model’s performance is evaluated with different EO benchmarks, including Overhead-MNIST, So2Sat LCZ42, PatternNet, RSI-CB256, and NaSC-TG2, through the TensorFlow Quantum platform, and it can achieve better performance than its classical counterpart and have higher generalizability, which verifies the validity of the QC-CNN model on EO data classification tasks.
elib-URL des Eintrags: | https://elib.dlr.de/199742/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Hybrid quantum-classical convolutional neural network model for image classification | ||||||||||||||||||||
Autoren: |
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Datum: | Dezember 2024 | ||||||||||||||||||||
Erschienen in: | IEEE Transactions on Neural Networks and Learning Systems | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 35 | ||||||||||||||||||||
DOI: | 10.1109/TNNLS.2023.3312170 | ||||||||||||||||||||
Seitenbereich: | Seiten 18145-18159 | ||||||||||||||||||||
Verlag: | IEEE | ||||||||||||||||||||
ISSN: | 2162-237X | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Image Classification, Quantum Machine Learning, Quantum Circuit, Remote Sensing Imagery | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||
Hinterlegt von: | Fan, Fan | ||||||||||||||||||||
Hinterlegt am: | 29 Nov 2023 13:27 | ||||||||||||||||||||
Letzte Änderung: | 21 Jan 2025 18:08 |
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