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    Machine Learning Helps Tune, Characterize Quantum Dots Quickly

    Article obtained from Photonics RSS Feed.

    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…

    Sep, 27 2019 |

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