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Support vector machines (SVMs) are a well-established classifier effectivelydeployed in an array of pattern recognition and classification tasks. In thiswork, we consider extending classic SVMs with quantum kernels and applying themto satellite data analysis. The design and implementation of SVMs with quantumkernels (hybrid SVMs) is presented. It consists of the Quantum KernelEstimation (QKE) procedure combined with a classic SVM training routine. Thepixel data are mapped to the Hilbert space using ZZ-feature maps acting on theparameterized ansatz state. The parameters are optimized to maximize the kerneltarget alignment. We approach the problem of cloud detection in satellite imagedata, which is one of the pivotal steps in both on-the-ground and on-boardsatellite image analysis processing chains. The experiments performed over thebenchmark Landsat-8 multispectral dataset revealed that the simulated hybridSVM successfully classifies satellite images with accuracy on par with classicSVMs.

Cloud Detection in Multispectral Satellite Images Using Support Vector Machines With Quantum Kernels