QDFlow: A Python package for physics simulations of quantum dot devices
This tool addresses data scarcity for researchers in quantum computing, though it is incremental as it builds on existing simulation methods.
The authors tackled the challenge of limited experimental data for machine learning in quantum dot device calibration by developing QDFlow, a physics simulator that generates realistic synthetic data with ground-truth labels, enabling the creation of large, diverse datasets for ML development and quantum device research.
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, representative datasets designed to capture the full spectrum of data quality encountered in practice, with both high- and low-quality data for training, benchmarking, and validation, with labels capturing key features of the device state. Collating such datasets experimentally is challenging due to limited data availability, slow measurement bandwidths, and the labor-intensive nature of labeling. QDFlow is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. QDFlow combines a self-consistent Thomas-Fermi solver, a dynamic capacitance model, and flexible noise modules to simulate charge stability diagrams and ray-based data closely resembling experiments. With an extensive set of parameters that can be varied and customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.