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arXiv · 1803.00853

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

Przemysław Sadowski
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Abstract

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.

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Przemysław Sadowski. 2018-03-02. Quantum distance-based classifier with constant size memory, distributed knowledge and state recycling. https://arxiv.org/abs/1803.00853

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