Quantum distance-based classifier with distributed knowledge and state recycling

In this work, we examine a recently proposed distance-based classification method designed for near-term quantum processing units with limited resources. We 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 system size requirements. 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 the accuracy of the algorithm can be improved by harnessing the state after the final measurement.