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

Quantum distance-based classifier with constant size memory, distributed knowledge and state recycling