  Spanning over two decades, the study of qubits in semiconductors for quantum
computing has yielded significant breakthroughs. However, the development of
large-scale semiconductor quantum circuits is still limited by challenges in
efficiently tuning and operating these circuits. Identifying optimal operating
conditions for these qubits is complex, involving the exploration of vast
parameter spaces. This presents a real 'needle in the haystack' problem, which,
until now, has resisted complete automation due to device variability and
fabrication imperfections. In this study, we present the first fully autonomous
tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a
clear indication of successful qubit operation. We demonstrate this automation,
achieved without human intervention, in a Ge/Si core/shell nanowire device. Our
approach integrates deep learning, Bayesian optimization, and computer vision
techniques. We expect this automation algorithm to apply to a wide range of
semiconductor qubit devices, allowing for statistical studies of qubit quality
metrics. As a demonstration of the potential of full automation, we
characterise how the Rabi frequency and g-factor depend on barrier gate
voltages for one of the qubits found by the algorithm. Twenty years after the
initial demonstrations of spin qubit operation, this significant advancement is
poised to finally catalyze the operation of large, previously unexplored
quantum circuits.
