Learning about the quantum K-means algorithm

We have already discussed the classical K-means clustering algorithm, and that will now help you to understand the quantum version of the K-means algorithm. As you already know, with the increase in the dimensions of big data recently, classical computers are becoming slower at processing data, and the same applies to the K-means algorithm as well. It has been found that the classical version of K-means has a time complexity of , where N is the number of features of the data points, M is the total number of input data points, and K is the number of clusters. However, with the quantum K-means algorithm, we get a time complexity of because only qubits are required to load the N-dimensional input data points using the amplitude encoding technique.

For the implementation of the quantum K-means algorithm, three main components are utilized – the swap test circuit, the distance calculation circuit, and Grover's optimization circuit...