While quantum dots are at the forefront of quantum device technology, tuning
multi-dot systems requires a lengthy experimental process as multiple
parameters need to be accurately controlled. This process becomes increasingly
time-consuming and difficult to perform manually as the devices become more
complex and the number of tuning parameters grows. In this work, we present a
crucial step towards automated tuning of quantum dot qubits. We introduce an
algorithm driven by machine learning that uses a small number of coarse-grained
measurements as its input and tunes the quantum dot system into a pre-selected
charge state. We train and test our algorithm on a GaAs double quantum dot
device and we consistently arrive at the desired state or its immediate
neighborhood.

本文介绍了一种基于机器学习的算法，使用少量粗粒度测量值作为输入，将量子点系统调整到预选的电荷状态，为自动调整量子点 qubit 迈出了关键一步。作者还将该算法在 GaAs 双量子点器件上进行了训练和测试，并成功将系统成功调整到预期状态或其相邻区域。