Electron spin qubits in quantum dot devices are promising for scalable quantum computing. However, architectural support is currently hindered by the lack of realistic and performant simulation methods for real devices. Physics-based tools are accurate yet too slow for simulating device behavior in real-time, while qualitative models miss layout and wafer heterostructure. We propose a new simulation approach capable of simulating real devices from the cold-start with real-time performance. Leveraging a key phenomenon observed in physics-based simulation, we train a compact convolutional neural network (CNN) to infer the qubit-layer electrostatic potential from gate voltages. Our GPU-accelerated inference delivers >1000x speedup with >96% agreement to the physics-based simulation. Integrated into the experiment control stack, the simulator returns results with millisecond scale latency, reproduces key tuning features, and yields device behaviors and metrics consistent with measurements on devices operated at 9 mK.
@article{arxiv.2509.02872,
title = {NeuroQD: A Learning-Based Simulation Framework For Quantum Dot Devices},
author = {Shize Che and Junyu Zhou and Seong Woo Oh and Jonathan Hess and Noah Johnson and Mridul Pushp and Robert Spivey and Anthony Sigillito and Gushu Li},
journal= {arXiv preprint arXiv:2509.02872},
year = {2025}
}