  In this work we examine recently proposed distance-based classification
method designed for near-term quantum processing units with limited resources.
We further study possibilities to reduce the quantum resources without any
efficiency decrease. We show that only a part of the information undergoes
coherent evolution and this fact allows us to introduce an algorithm with
significantly reduced quantum memory size. Additionally, considering only
partial information at a time, we propose a classification protocol with
information distributed among a number of agents. Finally, we show that the
information evolution during a measurement can lead to a better solution and
that accuracy of the algorithm can be improved by harnessing the state after
the final measurement.
