Clustering by Contour coreset and variational quantum eigensolver
This work addresses the challenge of improving quantum algorithm performance for clustering tasks, representing an incremental advance by customizing coreset techniques specifically for quantum computing.
The authors tackled the problem of low accuracy and inconsistency in quantum k-means clustering by proposing a variational quantum eigensolver (VQE) combined with a quantum-tailored Contour coreset, which outperformed existing QAOA-based methods with higher accuracy and lower standard deviation in simulations.
Recent work has proposed solving the k-means clustering problem on quantum computers via the Quantum Approximate Optimization Algorithm (QAOA) and coreset techniques. Although the current method demonstrates the possibility of quantum k-means clustering, it does not ensure high accuracy and consistency across a wide range of datasets. The existing coreset techniques are designed for classical algorithms and there has been no quantum-tailored coreset technique which is designed to boost the accuracy of quantum algorithms. In this work, we propose solving the k-means clustering problem with the variational quantum eigensolver (VQE) and a customised coreset method, the Contour coreset, which has been formulated with specific focus on quantum algorithms. Extensive simulations with synthetic and real-life data demonstrated that our VQE+Contour Coreset approach outperforms existing QAOA+Coreset k-means clustering approaches with higher accuracy and lower standard deviation. Our work has shown that quantum tailored coreset techniques has the potential to significantly boost the performance of quantum algorithms when compared to using generic off-the-shelf coreset techniques.