用生成模型从少量测量数据重建量子点电荷图谱。
Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models
- 用条件扩散模型从稀疏数据重建电荷稳定性图谱。
- 仅需4%原始数据即可保留关键电荷跃迁线特征。
- 适合需要快速表征的量子芯片研发团队。
高效表征量子点（QD）器件是基于受限自旋构建量子处理器时的关键瓶颈。高分辨率电荷稳定性图谱（CSDs）对定义量子点占据状态至关重要，但其测量耗时，尤其在新兴架构中需通过远程传感器间接探测，难以直接获取相关量子点电荷。本文提出一种生成式方法，利用条件扩散模型从稀疏测量中重建完整CSDs。我们在两种实验启发的掩码策略下评估该方法：均匀网格采样和线性扫描。轻量级模型在约9,000个样本上训练，仅需4%总测量数据即可成功重建CSDs，保持关键物理特征如电荷跃迁线。与插值方法相比，本方法在大范围未测量区域重建中表现更优。结果表明，生成模型可显著降低量子器件表征开销，为实验实现提供稳健路径。
原文摘要 · Abstract (English)
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.
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