A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability

Rushat Rai; Yun-Yuan Wang; Autsada Kakaen; Pei-Jie Chang; Doan Viet Nguyen; Yuan-Chieh Chiu; Doldet Tantraviwat; Niall Tumilty

cs.AISep 4, 2026

A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability

Authors: Rushat Rai, Yun-Yuan Wang, Autsada Kakaen, Pei-Jie Chang, Doan Viet Nguyen, Yuan-Chieh Chiu, Doldet Tantraviwat, Niall Tumilty, +5 more

Organizations: National Yang Ming Chiao Tung University, Taiwan · NVIDIA AI Technology Center, Taiwan · Chiang Mai University, Thailand · NVIDIA AI Technology Center, Singapore · National Center for High-performance Computing, Taiwan

Abstract

We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuits (PQCs) for data-scarce device modeling. A graph neural network (GNN) policy optimized by proximal policy optimization (PPO) searches circuit architectures using leave-one-group-out cross-validation (LOGOCV) error on held-out process or geometry groups as the reward. The framework achieves the lowest mean absolute error (MAE) on all 11 targets versus six classical baselines, with 59% lower error (Ioff) and 81% tighter fold variability (VTH) for HEMTs and 84% lower error (VTH, SS, Ioff) and 82% tighter fold variability (Ioff) for NWFETs. These results demonstrate the potential of RL-selected, classically simulated PQCs as compact surrogates with low OOD error and improved physical consistency, despite imposing no explicit physical constraints, penalty terms, or device-specific equations, on the two evaluated device datasets.

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Modeling quantum neural network gradient with reinforcement learning

Nhan Trong Luu, Duong Trung Luu, Nam Ngoc Pham, Thang Cong Truong

College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · Center for Digital Transformation and Communications, Can Tho University, Can Tho, Vietnam+1

Quantum Neural Networks Quantum Circuit Trainability Hybrid Quantum-Classical ML Barren Plateaus

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Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the

\mathcal{O}(L \cdot 2^n)

time and memory cost of differentiating through an

n

-qubit,

L

-layer circuit. We propose RLQ-Grad, a reinforcement-learning-based optimizer in which a classical policy

π_φ

(a spectrally-normalized PPO agent) learns to propose parameter updates directly, conditioned on the QNN's current parameters, loss, accuracy, and previous update. Because the surrogate gradient is emitted by a classical network rather than obtained by differentiating through the unitary

U(θ)

, its variance is not constrained by the barren plateau concentration bound, and its cost scales with the number of trainable parameters rather than the Hilbert-space dimension. We prove these properties formally and verify them on a hardware-efficient ansatz across four supervised benchmarks with up to

n=20

qubits. RLQ-Grad preserves a near-flat gradient-variance curve where backpropagation, parameter-shift, and adjoint differentiation decay by 1 to 2 orders of magnitude. Accounting for the full training pipeline (PPO rollouts, actor-critic updates, and optimizer states), RLQ-Grad needs under 2 MB of memory and runs

2490\times

7876\times

, and

673\times

faster per iteration than these three methods at

n=20

. It improves top-1 accuracy by up to

+10\%

over gradient-based baselines on circuits of up to 12 qubits, and matches dedicated barren plateau mitigation methods on CIFAR-10 at 14 to 20 qubits, where evolutionary and gradient-free optimizers collapse to chance.

Abstract

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