The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Open accessAbstract
This article aims to explore the potential of current approaches for quantum image classification in the context of remote sensing. After a brief outline of quantum computers and an analysis of the current bottlenecks, it shows for the first time experiments with quantum neural networks on a reference Earth observation (EO) dataset: EuroSAT. Moreover, it establishes the proof of concept of quantum computing for EO: the models trained and run on a quantum simulator are on par with classical ones. We make the open-source code available for further developments11QNN4EO repository: https://github.com/ESA-PhiLab/QNN4EO..
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“In our preliminary work [26], hybrid quantum-classical neural networks for remote sensing applications are discussed, and a proof of concept for binary classification, using multispectral optical data, is reported.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [26]
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“Moreover, it is worth highlighting that the hybrid solutions are the preferred ones in the current stage of QML, mostly due to technology bottlenecks and limitations [26], [27].”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [26]
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Cites
Quantum Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Quantum computational advantage using photons
Zhong, H.-S., Wang, H., Deng, Y.-H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Qin, J., Wu, D., Ding, X., Hu, Y., et al. (2020) · Science · doi:10.1126/science.abe8770 · Cited by 2,405
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2918242 · Cited by 1,951
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
Dunjko, V., & Briegel, H. J. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aab406 · Cited by 1,241
An Introduction to Quantum Computing
Kaye, P., Laflamme, R., & Mosca, M. (2006) · Oxford University Press eBooks · doi:10.1093/oso/9780198570004.001.0001 · Cited by 738
Cavallaro, G., Willsch, D., Willsch, M., Michielsen, K., & Riedel, M. (2020) · IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium · doi:10.1109/igarss39084.2020.9323544 · Cited by 53
(2020) · Research Publications (Maastricht University) · Cited by 19
Research works printed but not linked: 2
- asfaw, 2020, Learn quantum computation using qiskit
- gawron, 0, Multi-spectral image classification with quantum neural networks, Proc IGARSS
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On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162
Self-citationHybrid Quantum-Classical Convolutional Neural Network Model for Image Classification
Fan, F., Shi, Y., Guggemos, T., & Zhu, X. X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3312170 · Cited by 161
Artificial Intelligence for Trusted Autonomous Satellite Operations
Thangavel, K., Sabatini, R., Gardi, A., Ranasinghe, K. C., Hilton, S., Servidia, P. A., & Spiller, D. (2023) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2023.100960 · Cited by 128
Self-citationHybrid Quantum-Classical Neural Networks for Downlink Beamforming Optimization
Zhang, J., Zheng, G., Koike–Akino, T., Wong, K., & Burton, F. A. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3442091 · Cited by 38
Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?
Masum, M., Nazim, M. T. B., Faruk, M. J. H., Shahriar, H., Valero, M., Khan, M. A. H., Uddin, G., Barzanjeh, S., Sağlamyürek, E., Rahman, A., et al. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00097 · Cited by 32
Metadata
- Authors
- Zaidenberg, D. A., Sebastianelli, A., Spiller, D., Le Saux, B., & Ullo, S. L.
- Affiliations
- Massachusetts Institute of Technology (US)
- University of Sannio (IT)
- Agenzia Spaziale Italiana (IT)
- Venue
- 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
- Publisher
- IEEE
- Published
- 2021-07-11
- Pages
- 5680-5683
- OpenAlex
- W3122759250
- Cited by
- 52
- References
- 10
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 12 · 2023: 9 · 2022: 5 · 2021: 1
- FWCI
- 3.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- arXiv (Cornell University)
- Funders
- MIT
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- quantum machine learning, quantum neural networks, remote sensing, Earth observation, EuroSAT, quantum simulation
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