  Quantum computing is anticipated to offer immense computational capabilities
which could provide efficient solutions to many data science problems. However,
the current generation of quantum devices are small and noisy, which makes it
difficult to process large data sets relevant for practical problems. Coreset
selection aims to circumvent this problem by reducing the size of input data
without compromising the accuracy. Recent work has shown that coreset selection
can help to implement quantum K-Means clustering problem. However, the impact
of coreset selection on the performance of quantum K-Means clustering has not
been explored. In this work, we compare the relative performance of two coreset
techniques (BFL16 and ONESHOT), and the size of coreset construction in each
case, with respect to a variety of data sets and layout the advantages and
limitations of coreset selection in implementing quantum algorithms. We also
investigated the effect of depolarisation quantum noise and bit-flip error, and
implemented the Quantum AutoEncoder technique for surpassing the noise effect.
Our work provides useful insights for future implementation of data science
algorithms on near-term quantum devices where problem size has been reduced by
coreset selection.
