Title: Quantum vs. classical methods in information security, computing and machine learning domains: an empirical study

Authors: Kriti Srivastava; Akshit Gabhane; Ankit Ladva; Pushkar Waykole; S. Suman Rajest

Addresses: Department of Computer Science and Engineering (Data Science), Dwarkadas J. Sanghvi College of Engineering, Mumbai, India ' Department of Computer Science and Engineering (Data Science), Dwarkadas J. Sanghvi College of Engineering, Mumbai, India ' Department of Computer Science and Engineering (Data Science), Dwarkadas J. Sanghvi College of Engineering, Mumbai, India ' Department of Computer Science and Engineering (Data Science), Dwarkadas J. Sanghvi College of Engineering, Mumbai, India ' Department of Research and Development (R&D) and International Student Affairs (ISA), Dhaanish Ahmed College of Engineering, Chennai-601301, Tamil Nadu, India

Abstract: The rise in big data has necessitated the development of new computing technologies that can process large volumes of data in a faster and more efficient manner. In recent years, quantum computing has emerged as a promising candidate for this purpose due to its ability to work with high dimensionality data and its potential for solving complex problems that classical computers struggle with. This research work conducts a comparative analysis of quantum computing and classical computing in the fields of information security, computing, and machine learning, which are all critical fields in the modern world. The study uses a variety of methods, including theoretical analysis, simulation, and experimental implementation to demonstrate the benefits of quantum computing. This study serves as a basis for future research in the field of quantum computing and its applications, which could lead to significant advancements in various areas of science and technology.

Keywords: comparative analysis; Grover's algorithm; Shor's algorithm; quantum computing; quantum SVM; quantum CNN; information security.

DOI: 10.1504/IJESMS.2025.147415

International Journal of Engineering Systems Modelling and Simulation, 2025 Vol.16 No.4, pp.230 - 240

Received: 29 Feb 2024
Accepted: 18 Jul 2024
Published online: 15 Jul 2025 *

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