Enhanced Dynamic Quantum Clustering Based on von Neumann Entropy

Abstract

Dynamic quantum clustering (DQC) is a quantum algorithm to find possible data clusters. DQC uses quantum states to represent the clusters and the time evolution of the quantum states to predict different ways to match the data to clusters. There are several advantages to this method, as: (1) there is no need to specify the number of clusters; (2) there is no need to reduce the data; (3) hidden patterns within the main pattern can be studied; and (4) it eliminates the need for an operator to determine the point at which to terminate the process visually through the effective implementation of a fitness function. This paper introduces von Neumann entropy as a valuable metric that can be used to evaluate the DQC algorithm's results. Enhanced DQC can also show if there are different possible ways to cluster the data.