[2603.26432] Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

[2603.26432] Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2603.26432: Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

Quantum Physics arXiv:2603.26432 (quant-ph) [Submitted on 27 Mar 2026] Title:Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models Authors:Vinicius Hernandes, Joseph Rogers, Rouven Koch, Thomas Spriggs, Brennan Undseth, Anasua Chatterjee, Lieven M. K. Vandersypen, Eliska Greplova View a PDF of the paper titled Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models, by Vinicius Hernandes and 7 other authors View PDF HTML (experimental) Abstract: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 whe...

Originally published on March 30, 2026. Curated by AI News.

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