Quantum K Mean

Quantum k-means clustering aims to leverage quantum computing's power to improve the efficiency and accuracy of the classical k-means algorithm, a fundamental unsupervised machine learning technique for grouping data points. Current research focuses on hybrid quantum-classical approaches, employing variational quantum algorithms (VQAs) like VQE and QAOA, often incorporating coreset methods to handle large datasets and mitigate the limitations of noisy intermediate-scale quantum (NISQ) devices. These advancements hold promise for accelerating clustering tasks in various fields, particularly where large datasets or complex data structures pose challenges for classical methods.