Comparative Analysis of Classical and Quantum Approach for Unsupervised K-Means Algorithm

  • Dr. G. Murali, P. Pravallika, Narreddy Aravind Reddy, Vardhini Chouturu, Kolu Chandana

Abstract

Quantum algorithms are being extensively researched nowadays, seeing the potential to provide exponential speedup compared to classical algorithm execution. This speedup can play a significant role in machine learning, where training a model is usually prolonged. Training a machine learning model requires compressing large vectors into small computational units, and quantum computers inherently are fast at compressing the large vectors into small computations and tensor products. The present work aims to compare the K-means, a classical machine learning algorithm, and Q-means, a Quantum algorithm. Here in this work, we use q-means, a quantum algorithm for clustering, a canonical problem in unsupervised machine learning. The q-means algorithm has convergence and precision guarantees similar to k-means. [14]It outputs with high probability a good approximation of the k cluster centroids like the classical algorithm. The experimental results are demonstrated, which show a significant difference between k-means and q-means algorithms.

Published
2021-07-02
How to Cite
Dr. G. Murali, P. Pravallika, Narreddy Aravind Reddy, Vardhini Chouturu, Kolu Chandana. (2021). Comparative Analysis of Classical and Quantum Approach for Unsupervised K-Means Algorithm. Design Engineering, 2532 - 2543. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/2452
Section
Articles