  The optimization of Kernel-Target Alignment (TA) has been recently proposed
as a way to reduce the number of hardware resources in quantum classifiers. It
allows to exchange highly expressive and costly circuits to moderate size, task
oriented ones. In this work we propose a simple toy model to study the
optimization landscape of the Kernel-Target Alignment. We find that for
underparameterized circuits the optimization landscape possess either many
local extrema or becomes flat with narrow global extremum. We find the
dependence of the width of the global extremum peak on the amount of data
introduced to the model. The experimental study was performed using
multispectral satellite data, and we targeted the cloud detection task, being
one of the most fundamental and important image analysis tasks in remote
sensing.
