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.

Pauli spin blockade (PSB) can be employed sive; in the few-charges regime it can be found in as a great resource for spin qubit unexpected gate voltage locations or it might be initialisation and readout even at elevated absent, and in the multi-charge regime it has to temperatures but it can be difficult to be found like the proverbial needle in a haystack. We present a machine learning Its detection is challenging even for experienced algorithm capable of automatically identifying human experimenters since evidence for PSB is PSB using charge transport measurements. Those by training the algorithm with simulated details are affected by fluctuations in the disorder data and by using cross-device validation. The an essential step for realising fully scarcity of available data makes reliable automation automatic qubit tuning, is expected to be tough. In addition, PSB data tends to be employable across all types of quantum dot unbalanced, meaning that there are many more devices. Measurements promising candidates for scalable quantum computation exhibiting PSB are therefore rare in an and simulation [1-3]. They can achieve already scarce body of data. An automatic approach universal quantum computation [4] with gates would also allow us to gather sufficient reaching high fidelity [5, 6].