Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubits systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite dot locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been a considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, we present a comprehensive overview of the recent progress in the automation of QD device control, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven quite effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds tremendous potential in advancing semiconductor and other platforms for quantum computing.
翻译:量子点阵列(QDs)是一个大有希望的候选系统,可以实现可缩放、配对的qubits系统,并成为量子计算机的基本构件。在这种半导体量子系统中,设备现在拥有数十种单独的静电和动态电压,必须小心地设置这些电压,以便将系统定位到单一电子系统,并实现良好的qubit操作性能。对必要点点和门压压的绘图是一个具有挑战性的传统控制问题。随着QDQbits数量不断增加,相关参数空间的扩大足以使超常控制变得不可行。近年来,在将基于脚本的算法与机器学习(ML)技术相结合的自动设备控制装置已经做了大量努力。在这次学术讨论会中,我们全面概述了QD装置控制自动化方面的最新进展,特别强调在二维电子气体中形成的硅和以GAAADs为基础的QDs。将基于物理的模型与现代数字优化模型和ML的ML模型相结合,在生成高效的计算机实验平台上也证明具有了相当有效的高级的理论操作能力。