Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies.

Hybrid superconductor-semiconductor structures attract increasing attention owing to a variety of potential applications in quantum computing devices. They can serve to the realization of topological superconducting systems, as well as gate-tunable superconducting quantum bits. Here we combine a SiGe/Ge/SiGe quantum-well heterostructure hosting high-mobility two-dimensional holes and aluminum superconducting leads to realize prototypical hybrid devices, such as Josephson field-effect transistors (JoFETs) and superconducting quantum interference devices (SQUIDs). We observe gate-controlled supercurrent transport with Ge channels as long as one micrometer and 1 arXiv:1810.05012v2 [cond-mat.mes-hall] 23 Oct 2018 estimate the induced superconducting gap from tunnel spectroscopy measurements in superconducting point-contact devices. Transmission electron microscopy reveals the diffusion of Ge into the aluminum contacts, whereas no aluminum is detected in the Ge channel.Modern quantum nanoelectronics takes increasing advantage of newly synthesized hybrid superconductor-semiconductor (S-Sm) interfaces. 1 One of the main motivations is the search for Majorana zero modes that are predicted to appear in a topological superconductor. 2-4 A Josephson field effect transistor (JoFET) is one of the basic devices. It consists of a gatetunable semiconductor channel allowing Cooper-pair exchange between two superconducting contacts mediated by the superconducting proximity effect. 5 Gate control on the Josephson coupling has eventually led to the realization of electrically tunable transmon quantum bits, now often referred to as gatemons. 6-8 Many of the reported experimental realizations of hybrid S-Sm devices rely on bottomup fabrication starting from semiconductor nanowires or carbon nanotubes. 9-16 Recently, new hybrid S-Sm devices were demonstrated using top-down fabrication processes based on two-dimensional systems made of graphene, 17 InAs, 18,19 GaAs, 20 InGaAs 21 or Ge/SiGe. 22,23Top-down nanoscale devices offer significant advantages in terms of complexity and scalability. Those based on p-type SiGe heterostructures are readily compatible with silicon technology, 24 and, thanks to their intrinsically strong spin-orbit coupling, they are an attractive candidate for the development of topological superconducting systems. 22,[25][26][27][28][29][30][31][32] In this work, we present proof-of-concept S-Sm devices in which the semiconducting element consists of an undoped SiGe heterostucture embedding a strained Ge quantum-well (QW). A high-mobility two-dimensional hole gas (2DHG) is electrostatically accumulated in the QW by means of a surface gate electrode. (Hole mobilities as high as 5×10 5 cm 2 /Vs were reported for similar heterostructures. 12,22,33,34 ) The superconducting proximity effect induces gate-tunable superconductivity in the 2DHG enabling JoFET operation. This functionality is exploited for the realization of gate-controlled superconducting quantum interference