[2509.13298] QDFlow: A Python package for physics simulations of quantum dot devices

Condensed Matter > Mesoscale and Nanoscale Physics arXiv:2509.13298 (cond-mat) [Submitted on 16 Sep 2025 (v1), last revised 3 Mar 2026 (this version, v3)] Title:QDFlow: A Python package for physics simulations of quantum dot devices Authors:Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M. Taylor, Justyna P. Zwolak View a PDF of the paper titled QDFlow: A Python package for physics simulations of quantum dot devices, by Donovan L. Buterakos and 4 other authors View PDF HTML (experimental) Abstract: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 that closely resemble experimental results. With an extensive set of parameters that can be varied and ...