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

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

1-1-2024

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 (GAN) over a multi-layered 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 multi-layered 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 32GB 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.

ISSN

2327-4662

Disciplines

Computer Sciences

Keywords

Consumer Electronics, Digital Twins, Generative Adversarial Networks, Quantum Computing, Secure Manufacturing

Scopus ID

85209885647

Indexed in Scopus

yes

Open Access

no

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