Quantum version of the k-NN classifier based on a quantum sorting algorithm

TL;DR

This paper introduces a quantum sorting algorithm and a quantum k-NN classifier, demonstrating improved efficiency over classical methods and comparable performance to existing quantum approaches.

Contribution

It presents a new quantum sorting algorithm with adaptable resources and a quantum k-NN classifier that outperforms previous quantum versions in efficiency.

Findings

Quantum algorithms are more efficient than classical k-NN.

The proposed quantum k-NN outperforms Schuld et al.'s quantum k-NN.

The quantum k-NN has similar performance to classical k-NN.

Abstract

In this work we introduce a quantum sorting algorithm with adaptable requirements of memory and circuit depth, and then use it to develop a new quantum version of the classical machine learning algorithm known as k-nearest neighbors (k-NN). Both the efficiency and performance of this new quantum version of the k-NN algorithm are compared to those of the classical k-NN and another quantum version proposed by Schuld et al. \cite{Int13}. Results show that the efficiency of both quantum algorithms is similar to each other and superior to that of the classical algorithm. On the other hand, the performance of our proposed quantum k-NN algorithm is superior to the one proposed by Schuld et al. and similar to that of the classical k-NN.