Satellite image classification with neural quantum kernels

• URL: http://arxiv.org/abs/2409.20356v1
• Date: Mon, 30 Sep 2024 14:52:00 GMT
• Title: Satellite image classification with neural quantum kernels
• Authors: Pablo Rodriguez-Grasa, Robert Farzan-Rodriguez, Gabriele Novelli, Yue Ban, Mikel Sanz,
• Abstract summary: We use quantum kernels to classify images which include solar panels. In the latter, we iteratively train an $n$-qubit QNN to ensure scalability, using the resultant architecture to directly form an $n$-qubit EQK. Results are robust against a suboptimal training of the QNN.
• Score: 0.0699049312989311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A practical application of quantum machine learning in real-world scenarios in the short term remains elusive, despite significant theoretical efforts. Image classification, a common task for classical models, has been used to benchmark quantum algorithms with simple datasets, but only few studies have tackled complex real-data classification challenges. In this work, we address such a gap by focusing on the classification of satellite images, a task of particular interest to the earth observation (EO) industry. We first preprocess the selected intrincate dataset by reducing its dimensionality. Subsequently, we employ neural quantum kernels (NQKs)- embedding quantum kernels (EQKs) constructed from trained quantum neural networks (QNNs)- to classify images which include solar panels. We explore both $1$-to-$n$ and $n$-to-$n$ NQKs. In the former, parameters from a single-qubit QNN's training construct an $n$-qubit EQK achieving a mean test accuracy over 86% with three features. In the latter, we iteratively train an $n$-qubit QNN to ensure scalability, using the resultant architecture to directly form an $n$-qubit EQK. In this case, a test accuracy over 88% is obtained for three features and 8 qubits. Additionally, we show that the results are robust against a suboptimal training of the QNN.

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