Quantum Random Number Generation - Security Analysis: Investigating security analysis of quantum random number generation (QRNG) methods for generating truly random numbers with quantum mechanical processes
Keywords:
Quantum Key DistributionAbstract
Quantum Random Number Generation (QRNG) has emerged as a promising solution for generating truly random numbers, which are crucial for various applications in cryptography, simulations, and data encryption. This paper presents a comprehensive analysis of the security aspects of QRNG methods. We discuss the principles behind QRNG, compare different approaches, and evaluate their security against common attacks. Our findings suggest that QRNG methods offer a high level of security, especially when compared to classical pseudo-random number generators. However, we also identify potential vulnerabilities and discuss strategies for enhancing the security of QRNG systems. This research provides valuable insights for researchers and practitioners working in the field of quantum cryptography and random number generation.
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References
Tatineni, Sumanth, and Anirudh Mustyala. "Advanced AI Techniques for Real-Time Anomaly Detection and Incident Response in DevOps Environments: Ensuring Robust Security and Compliance." Journal of Computational Intelligence and Robotics 2.1 (2022): 88-121.
Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
Bojja, Giridhar Reddy, Jun Liu, and Loknath Sai Ambati. "Health Information systems capabilities and Hospital performance-An SEM analysis." AMCIS. 2021.
Vemoori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
Jeyaraman, Jawaharbabu, and Muthukrishnan Muthusubramanian. "Data Engineering Evolution: Embracing Cloud Computing, Machine Learning, and AI Technologies." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 1.1 (2023): 85-89.
Shahane, Vishal. "Serverless Computing in Cloud Environments: Architectural Patterns, Performance Optimization Strategies, and Deployment Best Practices." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 23-43.
Devan, Munivel, Ravish Tillu, and Lavanya Shanmugam. "Personalized Financial Recommendations: Real-Time AI-ML Analytics in Wealth Management." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online)2.3 (2023): 547-559.
Sharma, Kapil Kumar, Manish Tomar, and Anish Tadimarri. "Optimizing sales funnel efficiency: Deep learning techniques for lead scoring." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 261-274.
Abouelyazid, Mahmoud. "Adversarial Deep Reinforcement Learning to Mitigate Sensor and Communication Attacks for Secure Swarm Robotics." Journal of Intelligent Connectivity and Emerging Technologies 8.3 (2023): 94-112.
Prabhod, Kummaragunta Joel. "Leveraging Generative AI and Foundation Models for Personalized Healthcare: Predictive Analytics and Custom Treatment Plans Using Deep Learning Algorithms." Journal of AI in Healthcare and Medicine 4.1 (2024): 1-23.
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Althati, Chandrashekar, Manish Tomar, and Jesu Narkarunai Arasu Malaiyappan. "Scalable Machine Learning Solutions for Heterogeneous Data in Distributed Data Platform." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 4.1 (2024): 299-309.
