A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs
A preprint on arXiv describes a framework that uses a graph neural network and proximal policy optimization reinforcement learning to automatically find compact parameterized quantum circuits. These circuits are then used as surrogate models for data from two different semiconductor device types: power GaN high-electron-mobility transistors and logic nanowire field-effect transistors. The framework aims to provide a unified, physics-aware approach to device modeling rather than relying on hand-designed quantum ansatze.
OutlookPlausible
The RL-driven circuit discovery could be reapplied to other semiconductor devices, such as SiC MOSFETs or advanced FinFETs, by retraining on their I-V or C-V datasets.