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
| Ontological classification |
| Academic field: |
Physics |
| Specialties:
|
- Condensed Matter Physics - Experiment
- Condensed Matter Physics - Theory
|
| 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
We would like to thank the Referee for their careful reading of our manuscript and their comments and critiques, which we appreciate. We have addressed all suggestions and comments in the revised version of the manuscript. Our detailed point-by-point answers to all Referees’ comments are provided within the "Reply to Report."
List of changes
- Added a full section and figure discussing benchmarks, testing, and limitations of QDFlow.
- Added several sentences to the introduction emphasizing prior work validating QDFlow data in experimental contexts.
- Added a link to the QDFlow API documentation in the introduction and in the code availability section.
- Added a sentence in the introduction mentioning the unit and benchmark tests in the QDFlow repository.
- Added a few sentences in the conclusion discussing planned future updates to QDFlow.
