Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning.

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

Magnetic cooling for microkelvin nanoelectronics on a cryofree platform.

We present a parallel network of 16 demagnetization refrigerators mounted on a cryofree dilution refrigerator aimed to cool nanoelectronic devices to sub-millikelvin temperatures. To measure the refrigerator temperature, the thermal motion of electrons in a Ag wire-thermalized by a spot-weld to one of the Cu nuclear refrigerators-is inductively picked-up by a superconducting gradiometer and amplified by a SQUID mounted at 4 K. The noise thermometer as well as other thermometers are used to characterize the performance of the system, finding magnetic field independent heat-leaks of a few nW/mol, cold times of several days below 1 mK, and a lowest temperature of 150 µK of one of the nuclear stages in a final field of 80 mT, close to the intrinsic SQUID noise of about 100 µK. A simple thermal model of the system capturing the nuclear refrigerator, heat leaks, and thermal and Korringa links describes the main features very well, including rather high refrigerator efficiencies typically above 80%.