K-Means Clustering on Noisy Intermediate Scale Quantum Computers

TL;DR

This paper explores the use of noisy intermediate-scale quantum computers for K-means clustering, proposing strategies to reduce circuit depth and demonstrating comparable accuracy to classical methods on IBMQX2.

Contribution

It introduces three novel strategies to generate shorter-depth quantum circuits for K-means clustering on NISQ computers, addressing hardware limitations.

Findings

NISQ computers can perform K-means clustering with accuracy comparable to classical computers.

Proposed strategies effectively reduce quantum circuit depth.

Experimental validation on IBMQX2 supports feasibility of quantum clustering.

Abstract

Real-time clustering of big performance data generated by the telecommunication networks requires domain-specific high performance compute infrastructure to detect anomalies. In this paper, we evaluate noisy intermediate-scale quantum (NISQ) computers characterized by low decoherence times, for K-means clustering and propose three strategies to generate shorter-depth quantum circuits needed to overcome the limitation of NISQ computers. The strategies are based on exploiting; i) quantum interference, ii) negative rotations and iii) destructive interference. By comparing our implementations on IBMQX2 machine for representative data sets, we show that NISQ computers can solve the K-means clustering problem with the same level of accuracy as that of classical computers.