  Recently the use of Noisy Intermediate Scale Quantum (NISQ) devices for
machine learning tasks has been proposed. The propositions often perform poorly
due to various restrictions. However, the quantum devices should perform well
in sampling tasks. Thus, we recall theory of sampling-based approach to machine
learning and propose a quantum sampling based classifier. Namely, we use
randomized feature map approach. We propose a method of quantum sampling based
on random quantum circuits with parametrized rotations distribution. We obtain
simple to use method with intuitive hyper-parameters that performs at least
equally well as top out-of-the-box classical methods. In short we obtain a
competitive quantum classifier with crucial component being quantum sampling --
a promising task for quantum supremacy.
