利用扩散模型重建量子点电荷稳定性图
Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models
AI总结：
本文提出利用条件扩散模型从稀疏测量中重建量子点电荷稳定性图，通过两种实验驱动的掩码策略验证，展示了生成模型在减少量子设备表征开销方面的有效性。
摘要：
高效表征量子点（QD）设备是基于受限自旋的量子处理器扩展的关键瓶颈。高分辨率电荷稳定性图（或CSDs，定义QD占据的关键数据图）的测量耗时，特别是在新兴架构中，需使用无法直接探测相关点电荷的远程传感器。本文提出生成方法，通过条件扩散模型从稀疏测量中重建完整CSDs。我们使用两种实验驱动的掩码策略：基于均匀网格的采样和线扫。我们的轻量级架构在约9000个示例上训练，能够从总测量数据的4%中重建CSDs，保持关键物理特征如电荷跃迁线。我们比较了该方法与插值方法，后者在重建大未测量区域时失效。结果表明，生成模型可显著减少量子设备的表征开销，并提供了一条向实验实现稳健发展的途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
