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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
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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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