  Scalable quantum technologies will present challenges for characterizing and
tuning quantum devices. This is a time-consuming activity, and as the size of
quantum systems increases, this task will become intractable without the aid of
automation. We present measurements on a quantum dot device performed by a
machine learning algorithm. The algorithm selects the most informative
measurements to perform next using information theory and a probabilistic
deep-generative model, the latter capable of generating multiple
full-resolution reconstructions from scattered partial measurements. We
demonstrate, for two different measurement configurations, that the algorithm
outperforms standard grid scan techniques, reducing the number of measurements
required by up to 4 times and the measurement time by 3.7 times. Our
contribution goes beyond the use of machine learning for data search and
analysis, and instead presents the use of algorithms to automate measurement.
This work lays the foundation for automated control of large quantum circuits.
