An Investigation on Support Vector Clustering for Big Data in Quantum Paradigm

TL;DR

This paper explores a quantum implementation of support vector clustering to improve performance on big data, demonstrating significant speed-up over classical methods.

Contribution

It introduces a quantum support vector clustering algorithm utilizing quantum SVM and kernels, enhancing scalability and run-time efficiency.

Findings

Quantum SVM clustering achieves faster run-time.

Significant speed-up compared to classical algorithms.

Potential for handling large-scale data more effectively.

Abstract

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.