Abstract

The convergence of quantum computing and edge artificial intelligence (AI) represents a transformative advancement in real-time decision-making capabilities across diverse domains. This chapter explores the intricate relationship between these two cutting-edge technologies, emphasizing their synergistic potential to enhance urban management, healthcare, and industrial automation. By leveraging quantum computing's computational prowess alongside the low-latency processing capabilities of edge AI, organizations can address complex challenges and optimize operations in dynamic environments. The discussion delves into current quantum technologies relevant to edge AI, the architectural considerations for successful implementation, and the potential applications that can benefit from this integration. Additionally, significant technical challenges, such as hardware limitations, algorithm development, and security concerns, are examined, along with future directions to overcome these barriers. Ultimately, this chapter highlights the promising future of quantum edge AI as a pivotal solution for enhancing efficiency, sustainability, and responsiveness in the modern landscape.

Introduction

The convergence of quantum computing and edge artificial intelligence (AI) marks a significant milestone in the pursuit of enhanced decision-making capabilities [1]. Quantum computing, with its ability to process vast amounts of data and perform complex calculations at unprecedented speeds, complements the functionality of edge AI, which operates on the periphery of networks to deliver low-latency responses [2,3]. This intersection of technologies promises to revolutionize various sectors by enabling real-time analysis and insights that can drive more informed actions [4,5]. By integrating these advanced computational paradigms, organizations can leverage the strengths of both quantum and edge technologies to address intricate challenges that traditional computing methods struggle to solve [6].

Edge AI brings computational power closer to data sources, minimizing latency and allowing for immediate processing and response [7]. This immediacy was essential in applications such as autonomous vehicles, healthcare monitoring systems, and smart city infrastructures, where timely decisions are crucial [8,9,10]. Meanwhile, quantum computing offers the potential to tackle complex optimization problems and execute intricate simulations that are currently infeasible [11]. The combination of these capabilities allows for the development of intelligent systems capable of making rapid, data-driven decisions in environments characterized by uncertainty and variability [12]. As industries continue to evolve, the integration of quantum computing and edge AI was poised to enhance operational efficiency and drive innovation across multiple domains [13].