Satellite image classification with neural quantum kernels
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Abstract (arXiv)
Achieving practical applications of quantum machine learning for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation (EO) industry, using quantum machine learning techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels (NQKs)-quantum kernels derived from trained quantum neural networks (QNNs)-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 80 references that are in the map. First posted on arXiv 30 Sep 2024.
Builds on
28 of 80 references.
References · 28
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Citing papers per year: 2026: 2.
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