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While quantum dots are at the forefront of quantum device technology, tuningmulti-dot systems requires a lengthy experimental process as multipleparameters need to be accurately controlled. This process becomes increasinglytime-consuming and difficult to perform manually as the devices become morecomplex and the number of tuning parameters grows. In this work, we present acrucial step towards automated tuning of quantum dot qubits. We introduce analgorithm driven by machine learning that uses a small number of coarse-grainedmeasurements as its input and tunes the quantum dot system into a pre-selectedcharge state. We train and test our algorithm on a GaAs double quantum dotdevice and we consistently arrive at the desired state or its immediateneighborhood.

Automated tuning of double quantum dots into specific charge states using neural networks