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Enhancing Quantum Random Number Generators, Cryptographic Security, and Machine Learning-Based Cryptoanalysis
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Author(s): U. Jayalatsumi (Dr. M.G.R. Educational and Research Institute, India), T. Godhavari (Dr. M.G.R. Educational and Research Institute, India), Borra Keerthana (Dr. M.G.R. Educational and Research Institute, India), R. Rahul (Dr. M.G.R. Educational and Research Institute, India)and B. Swapna (Dr. M.G.R. Educational and Research Institute, India)
Copyright: 2025
Pages: 16
Source title:
Humans and Generative AI Tools for Collaborative Intelligence
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada), V. Vinoth Kumar (Vellore Institute of Technology, India), Polinpapilinho F. Katina (University of South Carolina Upstate, USA)and Joseph Richards (California State University, Sacramento, USA)
DOI: 10.4018/979-8-3693-8332-2.ch014
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Abstract
Randomness is essential in cryptography for key generation and secure information exchange, ensuring confidentiality and protection. Quantum Random Number Generators (QRNGs) leverage quantum mechanics to produce truly random numbers, offering a major advantage over Pseudo Random Number Generators (PRNGs), which are slower and less secure due to their deterministic nature. Quantum computing boosts QRNG efficiency, making them ideal for cryptographic applications, such as secure key exchange and encrypted communication. QRNGs generate stronger encryption systems by utilizing quantum phenomena like Bloch sphere rotations. The integration of machine learning (ML) with QRNGs further enhances their effectiveness, optimizing random sequences for improved speed and security. This combination of QRNGs and ML provides a powerful approach to cryptography, ensuring more secure communication channels and better protection against potential attacks.
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