利用机器学习自动提取2x2量子点阵列的虚拟电压
Automatic virtual voltage extraction of a 2x2 array of quantum dots with machine learning
AI总结：
本文提出一种基于机器学习的自动化方法，通过霍夫变换和回归模型集成提取电荷稳定性图中的梯度，从而确定2x2量子点阵列的虚拟栅极，有效减轻交叉电容效应，并可扩展至更大阵列。
摘要：
量子点中的自旋量子比特是实现容错量子计算的一个引人注目的平台，因为它们具有制造具有最近邻耦合的密集二维阵列的潜力，这是实现表面码的必要条件。然而，由于表面栅电极的邻近性，交叉耦合电容可能相当大，这使得独立控制每个量子点变得困难。增加量子点的数量会增加校准过程的复杂性，这使得启发式方法变得不切实际。受近期工业级硅量子点双线性阵列演示的启发，我们开发了一个理论框架来减轻2x2量子点阵列中交叉电容的影响，并将其扩展到2xN和NxN阵列。该方法基于在多个二维电荷稳定性图中提取不同电荷跃迁在栅极电压空间中的梯度，以确定系统的虚拟栅极。为了实现过程自动化，我们训练了一个回归模型集成，从电荷稳定性图的霍夫变换中提取梯度，并在2x2量子点阵列的模拟和实验数据上验证了该算法。我们的方法提供了一种完全自动化的工具来减轻量子点阵列中的交叉电容效应，可用于研究大型阵列中器件静电特性的变异性。
英文摘要：
Spin qubits in quantum dots are a compelling platform for fault-tolerant quantum computing due to the potential to fabricate dense two-dimensional arrays with nearest neighbour couplings, a requirement to implement the surface code. However, due to the proximity of the surface gate electrodes, cross-coupling capacitances can be substantial, making it difficult to control each quantum dot independently. Increasing the number of quantum dots increases the complexity of the calibration process, which becomes impractical to do heuristically. Inspired by recent demonstrations of industrial-grade silicon quantum dot bilinear arrays, we develop a theoretical framework to mitigate the effect of cross-capacitances in 2x2 arrays of quantum dots and extend it to 2xN and NxN arrays. The method is based on extracting the gradients in gate-voltage space of different charge transitions in multiple two-dimensional charge stability diagrams to determine the system's virtual gates. To automate the process, we train an ensemble of regression models to extract the gradients from a Hough transformation of charge stability diagrams and validate the algorithm on simulated and experimental data of a 2x2 quantum dot array. Our method provides a completely automated tool to mitigate cross-capacitance effects in arrays of QDs which could be utilised to study variability in device electrostatics across large arrays.
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