Boosting Inference and Decision Making in Edge AI Networks Through Quantum Computing and Collective Dynamics of 'Small-World' Networks

Abstract

This research investigates a novel strategy to improve the capabilities of edge artificial intelligence networks to reach conclusions and make decisions. This is accomplished through the utilization of various computing methods and the utilization of the collaborative dynamics of ‘small world' networks. Because of resource constraints and complex data environments, traditional edge AI networks usually suffer limitations in processing power and decision delicacy. These limitations can be a source of frustration. The purpose of this investigation is to suggest a paradigm shift toward more efficient and significant calculation at the edge by using the concepts of amount calculation. Additionally, the study analyzes how collaborative relations among bumps can improve information propagation and decision agreement inside the network. This investigation is motivated by the geste of small-world networks. Through theoretical analysis and simulation trials, this chapter demonstrates the potential for this approach to significantly improve performance and scalability.