Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm
This work addresses the challenge of processing large datasets on noisy, near-term quantum devices, providing incremental insights for data science algorithm implementation.
The study compared two coreset selection techniques (BFL16 and ONESHOT) for reducing data size in quantum K-Means clustering, analyzing their performance across datasets and the impact of quantum noise, while implementing Quantum AutoEncoder to mitigate noise effects.
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.