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.

中文翻译：

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研讨会：量子点设备控制自动化的进展

量子点 (QD) 阵列是一种很有前途的候选系统，可实现可扩展的耦合量子位系统，并可作为量子计算机的基本构建块。在此类半导体量子系统中，设备现在具有数十个单独的静电和动态电压，必须仔细设置这些电压，以将系统定位到单电子状态并实现良好的量子位操作性能。将必要的 QD 位置和电荷映射到栅极电压提出了一个具有挑战性的经典控制问题。随着 QD 量子位数量的增加，相关参数空间足够大，使得启发式控制变得不可行。近年来，人们在将基于脚本的算法与机器学习 (ML) 技术相结合的自动化设备控制方面做出了相当大的努力。在本次研讨会上，全面概述了量子点器件控制自动化的最新进展，特别强调了在二维电子气中形成的基于硅和砷化镓的量子点。事实证明，将基于物理的建模与现代数值优化和机器学习相结合可以有效产生高效、可扩展的控制。理论、计算和实验工作与计算机科学和机器学习的进一步整合在推进半导体和其他量子计算平台方面具有巨大的潜力。