Quantum Denclue Algorithm (QDA) as a New Clustering Approach Within Quantum Machine Learning

Our current study underscores the central significance of the Quantum kernel within the DENCLUE Clustering Algorithm, providing a novel perspective for understanding data characteristics from a quantum standpoint. While classical kernels depend on conventional distance measures such as Euclidean distance, the quantum kernel employs quantum operators to assess these similarities. Unlike classical kernels, the quantum kernel leverages the unique properties of qubits to precisely quantify data similarities. Our primary objective is to transition classical clustering methods into their quantum counterparts, particularly the various iterations of DENCLUE’s Algorithm. The Quantum Kernel offers an advanced approach for exploring non-trivial relationships within data, presenting exciting opportunities for data analysis and processing within a quantum framework, all while striving to significantly enhance the efficiency of data processing across different versions of DENCLUE’s clustering algorithms. In this paper, we delve into the concept of a quantum Kernel, highlighting its advancements over classical kernels. This methodology possesses the capability to capture intricate correlations and inherent nonlinear relationships within the data, thereby refining the identification of density attractors of DENCLUE’s Algorithms within Quantum Data Sets.