  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.
