New ML Algorithm Tunes Quantum Devices Faster Than Human Experts

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.