  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.
