The machine learning community has high hopes for quantum computers -- devices that can store and process quantum data and are expected to perform many computational tasks exponentially faster than classical computers. The variability among different quantum devices however presents challenges for the scalability of semiconductor quantum devices. In a new Nature paper, researchers from the University of Oxford, DeepMind, University of Basel and Lancaster University propose a novel machine learning (ML) algorithm that can tune quantum devices to optimal performance in a median time of under 70 minutes, faster than a typical tuning process performed by human experts. The proposed algorithm is also approximately 180 times faster than an automated random search of the parameter space, and is capable of dealing with different material systems and device architectures. "Until this work, coarse tuning required manual input or was restricted to a small gate voltage subspace," the researchers explain. Many ML techniques and other automated approaches have been proposed for quantum devices tuning, but these solutions tend to be limited to small regions of a device parameter space or require information about device characteristics.

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. To optimize operating conditions of large scale semiconductor quantum devices, a large parameter space has to be explored. Here, the authors report a machine learning algorithm to navigate the entire parameter space of gate-defined quantum dot devices, showing about 180 times faster than a pure random search.