Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

Quantum Physics [Submitted on 27 Mar 2026] Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models Vinicius Hernandes, Joseph Rogers, Rouven Koch, Thomas Spriggs, Brennan Undseth, Anasua Chatterjee, Lieven M. K. Vandersypen, Eliska Greplova Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability diagrams (or CSDs, data maps which crucially define the occupation of QDs) is time-consuming, particularly in emerging architectures where CSDs must be acquired with remote sensors that cannot probe the charge of the relevant dots directly. In this work, we present a generative approach to accelerate acquisition by reconstructing full CSDs from sparse measurements, using a conditional diffusion model. We evaluate our approach using two experimentally motivated masking strategies: uniform grid-based sampling, and line-cut sweeps. Our lightweight architecture, trained on approximately 9,000 examples, successfully reconstructs CSDs, maintaining key physically important features such as charge transition lines, from as little as 4\% of the total measured data. We compare the approach to interpolation methods, which fail when the task involves reconstructing large unmeasured regions. Our results demonstrate that generative models can significantly reduce the characterization overhead for quantum devices, and provides a robust path towards an experimental implementation. Comments: Code available at this https URL. Data available at this https URL Subjects: Quantum Physics (quant-ph); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Machine Learning (cs.LG) Cite as: arXiv:2603.26432 [quant-ph]   (or arXiv:2603.26432v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2603.26432 Focus to learn more Submission history From: Vinicius Hernandes [view email] [v1] Fri, 27 Mar 2026 14:03:15 UTC (6,778 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-03 Change to browse by: cond-mat cond-mat.mes-hall cs cs.LG References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)