Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models

TL;DR

This paper introduces a diffusion model-based method to reconstruct quantum dot charge stability diagrams from sparse measurements, significantly reducing data acquisition time in quantum device characterization.

Contribution

The authors develop a conditional diffusion model that accurately reconstructs charge stability diagrams from minimal data, outperforming traditional interpolation methods.

Findings

Successfully reconstructs CSDs from as little as 4% of data

Maintains key physical features like charge transition lines

Outperforms interpolation methods in large unmeasured regions

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 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.