01210nas a2200121   4500000000100000008004100001260001200042100002500054245008500079856004600164490000700210520087100217       2018                            d  c12/20181 aPrzemysław Sadowski00aQuantum distance-based classifier with distributed knowledge and state recycling  uhttps://doi.org/10.1142/S02197499184001300 v163 a<p>In this work we examine recently proposed distance-based clas-<br />
sification method designed for near-term quantum processing units<br />
with limited resources. We further study possibilities to reduce the<br />
quantum resources without any efficiency decrease. We show that<br />
only a part of the information undergoes coherent evolution and this<br />
fact allows us to introduce an algorithm with significantly reduced<br />
quantum memory size. Additionally, considering only partial infor-<br />
mation at a time, we propose a classification protocol with information<br />
distributed among a number of agents. Finally, we show that the infor-<br />
mation evolution during a measurement can lead to a better solution<br />
and that accuracy of the algorithm can be improved by harnessing the<br />
state after the final measurement.</p>