Hybrid Classical-Quantum Neural Networks for Multi-Characteristic Co-Optimization of Recessed-Gate AlGaN/GaN MIS-HEMTs
This paper proposes a hybrid classical-quantum neural network (HQNN) that significantly outperforms classical baselines in optimizing six electrical characteristics of recessed-gate AlGaN/GaN MIS-HEMTs by leveraging experimental data, while demonstrating that circuit depth, parameter count, and specific entanglement strategies are critical for accuracy and near-term hardware viability.
Rushat Rai, Pei-Jie Chang, Doan Viet Nguyen, Yuan-Chieh Chiu, Niall Tumilty, Yun-Yuan Wang, Simon See, Wen-Jay Lee, Tai-Yue Li, Nan-Yow Chen, Tian-Li Wu2026-05-28🔬 physics.app-ph