Hybrid Quantum–AI Optimization Models for Energy-Efficient and High-Yield Industrial Manufacturing Systems

Abstract: This study investigates hybrid Quantum–AI optimization models for improving energy efficiency and production yield in industrial manufacturing systems under realistic sensor-data constraints. The proposed framework couples (i) a physics-consistent energy model for electro-mechanical drives and process stages with (ii) an AI layer for state estimation and yield-risk scoring, and (iii) a quantum-ready optimizer for multi-objective setpoint selection. The decision problem is formulated as