SciPost Submission Page
QDFlow: A Python package for physics simulations of quantum dot devices
by Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M. Taylor, and Justyna P. Zwolak
Submission summary
| Authors (as registered SciPost users): | Justyna Zwolak |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202510_00006v3 (pdf) |
| Code repository: | https://github.com/QDFlow/QDFlow-sim |
| Code version: | v1.0.1 |
| Code license: | GPL-2.0 |
| Date submitted: | Jan. 30, 2026, 8:07 p.m. |
| Submitted by: | Justyna Zwolak |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| Approaches: | Theoretical, Experimental, Computational |
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 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.
Author comments upon resubmission
Below, we give a point-by-point response to the referee's comments and a summary of additional changes made to the manuscript.
List of changes
-
We have added a discussion of the applicability of QDFlow data to experiments in 2D arrays. We note that, so long as the crosstalk is not too high, QDFlow data is qualitatively similar to that of 2D arrays. This is why QDFlow data was successfully used to train machine learning models for tuning procedures in certain 2D devices. However, QDFlow is unable to fully model all data from 2D arrays, specifically failing to reproduce features from 2D architectures with tight couplings between nearby dots. We have also added a discussion about quantum effects (see below).
-
We have added a discussion and reference on quantum effects. Specifically, we have discussed QDarts, which includes a simulation of finite tunnel coupling, resulting in a broadening of interdot transitions. We note that for some applications, such as gate virtualization, a semiclassical model is sufficient; whereas for other applications, such as readout, quantum effects become more important. We hope to add this functionality in a future update to QDFlow, but for the moment, QDFlow lacks the ability to model such effects.
-
We have clearly listed the dependencies in the README file on the GitHub repository and have included installation instructions on both the GitHub repository and the documentation website. We have also created a comprehensive tutorial that gives examples of basic usage, data generation, adding noise, specifying custom distributions to randomize parameters, and illustrating how the physics simulation works. This tutorial is available as a Jupyter notebook from the GitHub repository. It is also included on the documentation website as a tutorial section alongside the API.