Codebase release 1.0 for QDFlow, by Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M. Taylor, Justyna P. Zwolak
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SciPost Physics Codebases Home Authoring Refereeing Submit a manuscript About Codebase release 1.0 for QDFlow Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M. Taylor, Justyna P. Zwolak SciPost Phys. Codebases 65-r1.0 (2026) · published 3 March 2026 doi: 10.21468/SciPostPhysCodeb.65-r1.0 publication repo live repo (external) BiBTeX RIS Submissions/Reports This Publication is part of a bundle When citing, cite all relevant items (e.g. for a Codebase, cite both the article and the release you used). DOI Type Published on 10.21468/SciPostPhysCodeb.65 Article 2026-03-03 10.21468/SciPostPhysCodeb.65-r1.0 Codebase release 2026-03-03 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 customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research. × TY - JOURPB - SciPost FoundationDO - 10.21468/SciPostPhysCodeb.65-r1.0TI - Codebase release 1.0 for QDFlowPY - 2026/03/03UR - https://scipost.org/SciPostPhysCodeb.65-r1.0JF - SciPost Physics CodebasesJA - SciPost Phys. CodebasesSP - 65-r1.0A1 - Buterakos, Donovan L.AU - Kalantre, Sandesh S.AU - Ziegler, JoshuaAU - Taylor, Jacob M.AU - Zwolak, Justyna P.AB - 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 customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.ER - × @Article{10.21468/SciPostPhysCodeb.65, title={{QDFlow: A Python package for physics simulations of quantum dot devices}}, author={Donovan L. Buterakos and Sandesh S. Kalantre and Joshua Ziegler and Jacob M. Taylor and Justyna P. Zwolak}, journal={SciPost Phys. Codebases}, pages={65}, year={2026}, publisher={SciPost}, doi={10.21468/SciPostPhysCodeb.65}, url={https://scipost.org/10.21468/SciPostPhysCodeb.65},}@Article{10.21468/SciPostPhysCodeb.65-r1.0, title={{Codebase release 1.0 for QDFlow}}, author={Donovan L. Buterakos and Sandesh S. Kalantre and Joshua Ziegler and Jacob M. Taylor and Justyna P. Zwolak}, journal={SciPost Phys. Codebases}, pages={65-r1.0}, year={2026}, publisher={SciPost}, doi={10.21468/SciPostPhysCodeb.65-r1.0}, url={https://scipost.org/10.21468/SciPostPhysCodeb.65-r1.0},} Ontology / Topics See full Ontology or Topics database.
Nanowires Semiconductors Authors / Affiliations: mappings to Contributors and Organizations See all Organizations. 1 2 Donovan L. Buterakos, 1 2 3 Sandesh S. Kalantre, 1 Joshua Ziegler, 1 2 4 Jacob M. Taylor, 1 2 Justyna P. Zwolak 1 National Institute of Standards and Technology [NIST] 2 University of Maryland, College Park [UMCP] 3 Stanford University [SU] 4 Axiomatic AI [AxI] Funders for the research work leading to this publication Army Research Office (ARO) (through Organization: United States Army Research Laboratory [ARL]) National Institute of Standards and Technology [NIST] ബോസ് ഇൻസ്റ്റിറ്റ്യൂട്ട് / Bose Institute