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Efficient AI Data Traffic Management via Quantum Network Routing Algorithms With Genuine Nonlocality in Triangle Networks
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Author(s): Satya Subrahmanyam (Holy Spirit University of Kaslik, Lebanon), Diwakar Chaudhary (Mangalmay Institute of Management and Technology, India), Manesh R. Palav (Global Business School, India), S. B. G. Tilak Babu (Aditya University, India), Ravi Babu Devareddi (Sagi RamaKrishnam Raju Engineering College, India)and Amar M. Chipade (Dr. D.Y. Patil Institute of Technology, India)
Copyright: 2025
Pages: 14
Source title:
Multidisciplinary Applications of AI and Quantum Networking
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)and Jose Anand (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-9336-9.ch016
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Abstract
This chapter explores the integration of amount network routing algorithms to optimize AI data business operation in triangle networks. Using genuine nonlocality essential in amount systems, the study proposes new approaches to enhance effectiveness and scalability in data transmission. By employing amount parcels similar as superposition and trap, the proposed algorithms aim to alleviate traffic and quiescence issues generally encountered in traditional routing styles. Through theoretical analysis and simulations, the effectiveness of the amount routing algorithms is demonstrated, showcasing their eventuality to revise data business operation in AI systems. This interdisciplinary approach islands quantum computing and network optimization, offering promising avenues for advancing communication technologies in complex triangular network infrastructures.
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