Design and Implementation of Intelligent Electronic Systems Using Embedded AI and Quantum Computing

Abstract

Computational methods that surpass the capabilities of traditional embedded intelligence are required due to the quick development of intelligent electronic devices. In order to improve system performance and decision-making efficiency, this study describes the development and execution of a smart electronic system that combines quantum computing and embedded artificial intelligence. The suggested framework uses quantum computer techniques for optimization and difficult problem solving, while integrating resource-constrained embedded devices with predictive models deployed at the edge. A hybrid traditional and quantum architecture is shown, in which quantum-assisted computation facilitates data processing and model optimisation while embedded AI conducts real-time inference. An embedded processor platform is used to implement the system, and representative smart electronics applications are used to assess it. When compared to conventional embedded AI techniques, experimental results show increased scalability, decreased computational delay, and higher accuracy. The suggested concept offers a scalable route to next-generation intelligent electronics and demonstrates the viability of combining quantum-assisted cognition with embedded electronic systems. For upcoming embedded systems that need high-performance intelligence with limited resources, this study provides a useful paradigm.