Support vector machines (SVMs) are a well-established classifier effectively deployed in an array of classification tasks. In this work, we consider extending classical SVMs with quantum kernels and applying them to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) are presented. Here, the pixels are mapped to the Hilbert space using a family of parameterized quantum feature maps (related to quantum kernels). The parameters are optimized to maximize the kernel target alignment. The quantum kernels have been selected such that they enabled analysis of numerous relevant properties while being able to simulate them with classical computers on a real-life large-scale dataset. Specifically, we approach the problem of cloud detection in the multispectral satellite imagery, which is one of the pivotal steps in both on-the-ground and on-board satellite image analysis processing chains. The experiments performed over the benchmark Landsat-8 multispectral dataset revealed that the simulated hybrid SVM successfully classifies satellite images with accuracy comparable to the classical SVM with the RBF kernel for large datasets. Interestingly, for large datasets, the high accuracy was also observed for the simple quantum kernels, lacking quantum entanglement.

翻译：支持向量机（SVM）是一种成熟的分类器，有效部署于多种分类任务中。本研究考虑将经典支持向量机扩展为量子核支持向量机，并将其应用于卫星数据分析。本文提出了量子核支持向量机（混合型SVM）的设计与实现方案。该方法利用一系列参数化量子特征映射（与量子核相关）将像素映射至希尔伯特空间，并通过最大化核目标对齐度来优化参数。所选的量子核不仅能分析众多相关特性，还可通过经典计算机在真实大规模数据集上进行仿真模拟。具体而言，我们针对多光谱卫星图像中的云检测问题展开研究——该环节是地面与星载卫星图像分析处理链中的关键步骤之一。在基准Landsat-8多光谱数据集上的实验表明，对于大规模数据集，模拟混合型SVM的分类精度与采用RBF核的经典SVM相当。值得注意的是，在大规模数据场景中，即使采用缺乏量子纠缠的简单量子核，仍能观测到较高的分类精度。