  Recent progress in building large-scale quantum devices for exploring quantum
computing and simulation paradigms has relied upon effective tools for
achieving and maintaining good experimental parameters, i.e. tuning up devices.
In many cases, including in quantum-dot based architectures, the parameter
space grows substantially with the number of qubits, and may become a limit to
scalability. Fortunately, machine learning techniques for pattern recognition
and image classification using so-called deep neural networks have shown
surprising successes for computer-aided understanding of complex systems. In
this work, we use deep and convolutional neural networks to characterize states
and charge configurations of semiconductor quantum dot arrays when one can only
measure a current-voltage characteristic of transport (here conductance)
through such a device. For simplicity, we model a semiconductor nanowire
connected to leads and capacitively coupled to depletion gates using the
Thomas-Fermi approximation and Coulomb blockade physics. We then generate
labelled training data for the neural networks, and find at least $90\,\%$
accuracy for charge and state identification for single and double dots purely
from the dependence of the nanowire's conductance upon gate voltages. Using
these characterization networks, we can then optimize the parameter space to
achieve a desired configuration of the array, a technique we call
`auto-tuning'. Finally, we show how such techniques can be implemented in an
experimental setting by applying our approach to an experimental data set, and
outline further problems in this domain, from using charge sensing data to
extensions to full one and two-dimensional arrays, that can be tackled with
machine learning.
