Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm

TL;DR

This paper evaluates how coreset selection techniques influence the performance of quantum K-Means clustering on small, noisy quantum devices, highlighting advantages, limitations, and noise mitigation strategies.

Contribution

It compares two coreset methods and analyzes their impact on quantum K-Means, including noise effects and autoencoder-based noise reduction, providing insights for near-term quantum data science applications.

Findings

Coreset selection can effectively reduce data size for quantum clustering.

Different coreset techniques have varying performance depending on data and noise.

Quantum AutoEncoder helps mitigate noise effects in quantum algorithms.

Abstract

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.