K-Means Clustering on Noisy Intermediate Scale Quantum Computers
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.
Cite
@article{arxiv.1909.12183,
title = {K-Means Clustering on Noisy Intermediate Scale Quantum Computers},
author = {Sumsam Ullah Khan and Ahsan Javed Awan and Gemma Vall-Llosera},
journal= {arXiv preprint arXiv:1909.12183},
year = {2019}
}