Quantum distance-based classifier with distributed knowledge and state recycling

In this work we examine recently proposed distance-based clas-
sification 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 infor-
mation at a time, we propose a classification protocol with information
distributed among a number of agents. Finally, we show that the infor-
mation 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.