Real-Time AI Decision Making in IoT with Quantum Computing: Investigating & Exploring the Development and Implementation of Quantum-Supported AI Inference Systems for IoT Applications
Keywords:
Real-Time, AI Decision Making, IoT, Quantum Computing, Quantum-Supported AI Inference Systems, Development, Implementation, Challenges, Future ProspectsAbstract
The Internet of Things (IoT) has revolutionized the way devices communicate and interact, generating vast amounts of data that require real-time processing and decision-making capabilities. Traditional AI systems face challenges in meeting the real-time demands of IoT applications due to computational complexities. Quantum computing has emerged as a potential solution, offering parallel processing power to accelerate AI inference tasks. This paper investigates the integration of quantum computing into AI systems for real-time decision-making in IoT. We explore the development, challenges, and future prospects of quantum-supported AI inference systems for IoT applications, highlighting the potential benefits and limitations of this approach. Through a comprehensive review of existing literature and case studies, we provide insights into the current state of quantum-supported AI inference systems in IoT and discuss the implications for future research and development in this field.
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