Quantum and GAN-Driven Digital Twin Approach for IoT-Based Consumer Electronics Manufacturing

Abstract

Quantum computing offers exceptional computational capabilities, but achieving optimal performance and resource efficiency in practical applications remains challenging. Addressing the gap between theoretical quantum algorithms and their real-world implementation, this study introduces QuantGAN, a novel approach designed to enhance sustainability and security in Internet of Things (IoT) and consumer electronics manufacturing. QuantGAN combines state-of-the-art quantum algorithms and generative adversarial networks (GANs) over a multilayered Digital Twin framework. This enables explicit sustainability risk assessment with quantum computing and latent process optimization via GANs. The Digital Twin, foreseen as an interactive metaverse interface, enables a real time touch-and-go framework. Central modules within GENESIS include a multilayered Digital Twin, quantum risk assessment algorithms, and an AI-driven continuous feedback loop orchestrated by GANs. The simulation environment uses Qiskit on Intel Core i7-10700K CPU with 32 GB RAM using Ubuntu 20.04 LTS. Our experimental results show that QuantGan effectively out performs the existing methods achieving 96.4% accuracy in detecting risk.