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

arXiv: https://arxiv.org/abs/2206.07852 · HTML: https://mlsanity.com/paper/2206.07852/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [New Frameworks for Offline and Streaming Coreset Constructions](https://mlsanity.com/paper/1612.00889/) (2016)
- [One-Shot Coresets: The Case of k-Clustering](https://mlsanity.com/paper/1711.09649/) (2017)
- [Denoising quantum states with Quantum Autoencoders -- Theory and Applications](https://mlsanity.com/paper/2012.14714/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Coreset Selection of Coreset Selection Literature: Introduction and Recent Advances](https://mlsanity.com/paper/2505.17799/) (2025)
- [Coreset selection can accelerate quantum machine learning models with provable generalization](https://mlsanity.com/paper/2309.10441/) (2023)
- [Clustering by Contour coreset and variational quantum eigensolver](https://mlsanity.com/paper/2312.03516/) (2023)
- [SubZeroCore: A Submodular Approach with Zero Training for Coreset Selection](https://mlsanity.com/paper/2509.21748/) (2025)
- [PASS: Certified Subset Repair for Classical and Quantum Pairwise Constrained Clustering](https://mlsanity.com/paper/2601.20157/) (2026)

## Abstract (arXiv)

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.