Satellite image classification with neural quantum kernels
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916641700315136 |
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| author | Rodriguez-Grasa, Pablo Farzan-Rodriguez, Robert Novelli, Gabriele Ban, Yue Sanz, Mikel |
| author_facet | Rodriguez-Grasa, Pablo Farzan-Rodriguez, Robert Novelli, Gabriele Ban, Yue Sanz, Mikel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20356 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Satellite image classification with neural quantum kernels Rodriguez-Grasa, Pablo Farzan-Rodriguez, Robert Novelli, Gabriele Ban, Yue Sanz, Mikel Quantum Physics Machine Learning 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. |
| title | Satellite image classification with neural quantum kernels |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2409.20356 |