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.

翻译：核-目标对齐（Kernel-Target Alignment，TA）的优化近年来被提出作为减少量子分类器中硬件资源的一种方法。它允许将高表达性但高成本的电路替换为中等规模、面向任务的电路。在本工作中，我们提出了一个简单玩具模型来研究核-目标对齐的优化景观。我们发现，对于欠参数化电路，优化景观要么存在许多局部极值，要么变得平坦且具有狭窄的全局极值。我们发现了全局极值峰的宽度与引入模型的数据量之间的依赖关系。实验研究使用多光谱卫星数据完成，并以云检测任务为目标，该任务是遥感领域中最基本且最重要的图像分析任务之一。