  The support vector clustering algorithm is a well-known clustering algorithm
based on support vector machines using Gaussian or polynomial kernels. The
classical support vector clustering algorithm works well in general, but its
performance degrades when applied on big data. In this paper, we have
investigated the performance of support vector clustering algorithm implemented
in a quantum paradigm for possible run-time improvements. We have developed and
analyzed a quantum version of the support vector clustering algorithm. The
proposed approach is based on the quantum support vector machine and quantum
kernels (i.e., Gaussian and polynomial). The proposed quantum version of the
SVM clustering method demonstrates a significant speed-up gain on the overall
run-time complexity as compared to the classical counterpart.
