Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm. (arXiv:2206.07852v1 [quant-ph])

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 …

algorithm analysis arxiv clustering clustering algorithm implementation k-means performance performance analysis quantum