Cloud Detection in Multispectral Satellite Images Using Support Vector
  Machines With Quantum Kernels
Artur Miroszewski Jakub Mielczarek Filip Szczepanek Grzegorz Czelusta Bartosz Grabowski Bertrand Le Saux Jakub Nalepa
Support vector machines (SVMs) are a well-established classifier effectively
deployed in an array of pattern recognition and classification tasks. In this
work, we consider extending classic SVMs with quantum kernels and applying them
to satellite data analysis. The design and implementation of SVMs with quantum
kernels (hybrid SVMs) is presented. It consists of the Quantum Kernel
Estimation (QKE) procedure combined with a classic SVM training routine. The
pixel data are mapped to the Hilbert space using ZZ-feature maps acting on the
parameterized ansatz state. The parameters are optimized to maximize the kernel
target alignment. We approach the problem of cloud detection in satellite image
data, 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 on par with classic
SVMs.