HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification.
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| Title: | HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification. |
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| Authors: | Lianghai, Chen1,2 chenlh@mail.hnust.edu.cn, Yuzhen, Liu1,2 yzhenliu@126.com, Yi, Lu1,2 24010502004@mail.hnust.edu.cn, Xiaoliang, Wang1,2 fengwxl@hnust.edu.cn, Huaning, Song3 2008@163.com |
| Source: | Computer Science & Information Systems. Apr2026, Vol. 23 Issue 2, p1-883. 883p. |
| Subjects: | Remote sensing, Quantum computing, Environmental monitoring, Convolutional neural networks, Remote-sensing images |
| Abstract: | Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum–Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193328678 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lianghai%2C+Chen%22">Lianghai, Chen</searchLink><relatesTo>1,2</relatesTo><i> chenlh@mail.hnust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yuzhen%2C+Liu%22">Yuzhen, Liu</searchLink><relatesTo>1,2</relatesTo><i> yzhenliu@126.com</i><br /><searchLink fieldCode="AR" term="%22Yi%2C+Lu%22">Yi, Lu</searchLink><relatesTo>1,2</relatesTo><i> 24010502004@mail.hnust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiaoliang%2C+Wang%22">Xiaoliang, Wang</searchLink><relatesTo>1,2</relatesTo><i> fengwxl@hnust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Huaning%2C+Song%22">Huaning, Song</searchLink><relatesTo>3</relatesTo><i> 2008@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Apr2026, Vol. 23 Issue 2, p1-883. 883p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Remote sensing image classification is a fundamental task for Earth observation and environmental monitoring. However, conventional convolutional neural networks (CNNs) are limited by computational capacity and struggle to efficiently process the rapidly growing volume of remote sensing data. To address this limitation, we propose HAQCCN (Hybrid Asymmetric Quantum–Classical Convolutional Network), a novel hybrid architecture that integrates quantum computation into the classical convolutional framework through asymmetric quantum convolutional circuits. In HAQCCN, the asymmetric quantum circuits enable a limited number of qubits to process more classical data while maintaining excellent feature extraction capability. Experiments conducted on the IBM Qiskit platform using the Overhead-MNIST, PatternNet, and RSI-CB256 datasets demonstrate that HAQCCN outperforms conventional CNNs and existing quantum models. Furthermore, we systematically investigate the effects of encoding schemes, the number of quantum convolutional kernels, and the number of qubits on model performance, confirming the effectiveness and scalability of the proposed method for remote sensing image classification. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS251029015C Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 883 StartPage: 1 Subjects: – SubjectFull: Remote sensing Type: general – SubjectFull: Quantum computing Type: general – SubjectFull: Environmental monitoring Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Remote-sensing images Type: general Titles: – TitleFull: HAQCCN: A Hybrid Quantum–Classical Convolutional Network with Asymmetric Kernels for Remote Sensing Image Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lianghai, Chen – PersonEntity: Name: NameFull: Yuzhen, Liu – PersonEntity: Name: NameFull: Yi, Lu – PersonEntity: Name: NameFull: Xiaoliang, Wang – PersonEntity: Name: NameFull: Huaning, Song IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 2 Titles: – TitleFull: Computer Science & Information Systems Type: main |
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