Quantum-Inspired Optimization and Resource Allocation in AI-Driven Data Centers and Edge Networks

This study presents a comprehensive review and synthesis of recent research advancements integrating quantum-inspired optimization and artificial intelligence (AI) in data centers and edge computing networks. With the exponential growth in data generation and the demand for real-time processing, AI-driven infrastructures face challenges in scalability, latency, and energy efficiency. Through the evaluation of 29 scholarly works authored or co-authored by Vinod Veeramachaneni, Srinivasa Rao Bittla, and Srimaan Yarram, the study highlights innovations across diagnostics in electrical systems, cybersecurity through Zero Trust and blockchain, large language models, anomaly detection in IoT, and advanced software engineering practices. A key contribution lies in the identification of hybrid AI models—ranging from Graph Neural Networks (GNNs) to federated learning—and their application to optimize resource allocation and fault tolerance. The insights provided form a foundational framework for future development of intelligent, secure, and resource-efficient infrastructures in critical computing environments.