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

TL;DR

This paper introduces a quantum classification algorithm optimized for limited quantum resources, utilizing partial information, distributed knowledge, and state recycling to enhance efficiency and accuracy.

Contribution

It presents a novel quantum classification method that reduces quantum memory requirements and leverages distributed information and state recycling for improved performance.

Findings

Reduced quantum memory size without efficiency loss

Distributed classification protocol among multiple agents

Enhanced accuracy through state recycling after measurement

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.