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Priya Natarajan

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Open access 2021

Quantum Communication and Its Impact on Future Data Transmission Systems

Quantum communication is an emerging field that promises to revolutionize data transmission by leveraging the principles of quantum mechanics. This paper explores the fundamental concepts, underlying principles, and potential impact of quantum communication on future data transmission systems. The study delves into quantum key distribution (QKD), quantum teleportation, and quantum networks, highlighting their advantages over classical communication systems. A comparative analysis of classical cryptographic methods and quantum-enhanced security mechanisms is provided. Furthermore, the paper discusses the challenges associated with implementing quantum communication, such as decoherence, quantum error correction, and scalability. The methodology section outlines experimental setups, simulations, and practical implementations of quantum communication networks. The results emphasize the benefits of quantum encryption and the potential of quantum internet. The discussion explores real-world applications in banking, defense, and cloud computing. Finally, the paper concludes with future perspectives, emphasizing the necessity for ongoing research and technological advancements to achieve a fully functional quantum communication infrastructure.

Rajesh Sharma, Priya Natarajan · 0 citations
Review Open access 2021

Edge Computing Architectures for Ultra-Low Latency Applications

The evaluation of the proposed architecture through analytical models and simulation-based evaluations shows that the proposed architecture can reduce the latency onto 65 percent of the time relative to the conventional cloud-based architecture, affirm the claim that edge computing is an essential enabler of the next-generation applications that demand deterministic response time, high reliability and localized intelligence.

Priya Natarajan · 0 citations
Open access 2021

Reinforcement Learning for Adaptive Resource Management in Cloud Software

Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.

Rajesh Sharma, Priya Natarajan · 0 citations