Cloud-Native Quantum Generative AI Architecture for Real-Time ERP, ETL, and DC Motor Control Systems
DOI:
https://doi.org/10.15662/IJARCST.2023.0606007Keywords:
Quantum Computing, Generative AI, Cloud-Native Architecture, ERP Systems, ETL Automation, Real-Time Control, DC Motor Optimization, Intelligent AutomationAbstract
The convergence of quantum computing, generative artificial intelligence (AI), and cloud-native architectures is redefining the future of intelligent automation in industrial and enterprise systems. This paper introduces a Cloud-Native Quantum Generative AI Architecture designed for real-time ERP integration, automated ETL processing, and adaptive DC motor control. The proposed framework leverages quantum-enhanced generative models to accelerate data-driven decision-making, enabling predictive process automation and optimization across distributed enterprise environments. Cloud-native deployment ensures scalability, resilience, and continuous service orchestration, allowing seamless synchronization between ERP workflows and IoT-enabled DC motor systems. The generative AI engine synthesizes operational patterns, forecasts system demands, and autonomously refines control parameters using quantum-assisted optimization algorithms. Furthermore, the architecture’s ETL module automates real-time data extraction, transformation, and loading, ensuring efficient data flow between industrial sensors, ERP databases, and cloud storage. Experimental evaluations demonstrate significant improvements in response latency, forecasting precision, and energy efficiency of DC motor operations. The results confirm that integrating quantum generative AI within cloud-native infrastructures offers a transformative approach for achieving autonomous, intelligent, and self-optimizing industrial ecosystems.
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