Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning to a regime suitable for qubit operations.

翻译：栅极定义的量子点（QDs）作为量子计算平台具有诱人的特性。然而，近期设备在调谐与操控过程中存在多种潜在的不完美因素，其中金属栅极间的电容串扰是定义和控制量子点量子比特的关键问题。通过使用虚拟栅极可在不依赖耦合的情况下补偿电容串扰并实现特定量子点的定向控制。本文展示了一种结合机器学习与传统拟合优点的可靠自动电容耦合识别方法，该方法充分利用了两者的理想特性。同时，我们展示了如何利用交叉电容测量来识别调谐实验设备中偶然形成的杂散量子点。我们的系统能够自主标记在运行区域附近存在杂散量子点的设备，这是实现量子比特操作所需可靠调谐的关键信息。