Machine Learning Helps Tune, Characterize Quantum Dots Quickly

Using a machine-learning approach, scientists from the Universities of Oxford, Basel, and Lancaster are automating the process of characterizing and tuning individual semiconductor quantum dots (QDs) for use as qubits. This machine-learning approach to tuning could reduce the measuring time and the number of measurements by a factor of approximately four compared with conventional methods of data acquisition. Semiconductor QDs are not identical and must be characterized individually. When several QDs are combined to scale a device up to a large number of qubits, this tuning process can become enormously time-consuming. Artistic illustration of the potential landscape defined by voltages applied to nanostructures in order to...