Spin qubit tuning automation for computer scientists

Abstract Tuning gate-defined quantum dots for qubit operation is an often frustrating and time-consuming activity in spin qubit research due to inherent device variability and the complex interactions between dots. Furthermore, tuning these devices grows more complicated as quantum computers grow to utility-scale. Computer-automated and machine learning techniques are popular approaches to accelerate the tuning process. However, these techniques need to become much faster and more accurate to address the tuning of millions of qubits in utility-scale quantum computing. We examine the literature of computer-automated and machine learning spin qubit tuning algorithms, grouping the algorithms by similar tasks and analyzing them from a computer science perspective to keep necessary background knowledge of quantum computing/physics at a minimum. We look at which approaches seem propitious for scaling up to utility-scale systems and where computer scientists could potentially have the most impact.