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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.
Din et al. (Thu,) studied this question.