Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capacitive crosstalk between gates hinders independent tuning of chemical potentials and interdot couplings. While virtual gates offer a practical solution, determining all the required cross-capacitance matrices accurately and efficiently in large quantum dot registers is an open challenge. Here, we establish a Modular Automated Virtualization System (MAViS) -- a general and modular framework for autonomously constructing a complete stack of multi-layer virtual gates in real time. Our method employs machine learning techniques to rapidly extract features from two-dimensional charge stability diagrams. We then utilize computer vision and regression models to self-consistently determine all relative capacitive couplings necessary for virtualizing plunger and barrier gates in both low- and high-tunnel-coupling regimes. Using MAViS, we successfully demonstrate accurate virtualization of a dense two-dimensional array comprising ten quantum dots defined in a high-quality Ge/SiGe heterostructure. Our work offers an elegant and practical solution for the efficient control of large-scale semiconductor quantum dot systems.

翻译：栅极定义的半导体量子点阵列是构建可扩展量子处理器的主要候选方案之一。自旋量子比特寄存器的高保真初始化、控制与读取，需要对定义静电环境的关键哈密顿参数进行精确且有针对性的调控。然而，由于栅极间距极小，栅极间的电容串扰阻碍了化学势与点间耦合的独立调节。虽然虚拟栅极提供了一种实用解决方案，但在大型量子点寄存器中准确且高效地确定所有必需的交叉电容矩阵仍是一个悬而未决的挑战。本文中，我们建立了一个模块化自主虚拟化系统（MAViS）——一个通用且模块化的框架，用于实时自主构建完整的多层虚拟栅极栈。我们的方法采用机器学习技术从二维电荷稳定图中快速提取特征，随后利用计算机视觉与回归模型，自洽地确定在低隧道耦合与高隧道耦合两种状态下虚拟化 plunger 栅极与 barrier 栅极所需的所有相对电容耦合。借助 MAViS，我们成功演示了在高质量 Ge/SiGe 异质结构中定义的、包含十个量子点的密集二维阵列的精确虚拟化。我们的工作为高效调控大规模半导体量子点系统提供了一种优雅且实用的解决方案。