Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit 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 QD 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 considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, 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 effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.

翻译：量子点（QD）阵列是实现可扩展耦合量子比特系统的一个有前景的候选体系，并作为量子计算机构建的基础单元。在此类半导体量子系统中，器件现在拥有数十个独立的静电和动态电压，必须仔细设定这些电压，以使系统定位在单电子区域并实现良好的量子比特运行性能。将所需的QD位置和电荷映射至栅极电压，构成一个具有挑战性的经典控制问题。随着QD量子比特数量的增加，相关参数空间充分增长，使得启发式控制变得不可行。近年来，人们投入了大量精力，将基于脚本的算法与机器学习（ML）技术相结合，以实现器件控制的自动化。本综述全面概述了QD器件控制自动化领域的最新进展，特别侧重于二维电子气中形成的硅基和砷化镓基QD。将基于物理的建模与现代数值优化及机器学习相结合，已被证明能够有效实现高效、可扩展的控制。进一步将理论、计算和实验工作与计算机科学及机器学习相结合，对于推进半导体及其他量子计算平台具有巨大潜力。