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

Published

March 3, 2026

Author(s)

Donovan Buterakos, Sandesh Kalantre, Joshua Ziegler, Jacob Taylor, Justyna Zwolak

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. \textttQDFlow} is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. \textttQDFlow} 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 customizable noise models, \textttQDFlow} supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.

Citation

SciPost Physics Codebases

Pub Type

Journals

Download Paper

https://doi.org/10.21468/SciPostPhysCodeb.65

Local Download

Keywords

quantum dots, simulation, machine learning

Citation

Buterakos, D. , Kalantre, S. , Ziegler, J. , Taylor, J. and Zwolak, J. (2026), QDFlow: A Python package for physics simulations of quantum dot devices, SciPost Physics Codebases, [online], https://doi.org/10.21468/SciPostPhysCodeb.65, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960777 (Accessed March 12, 2026)