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Machine learning for scalable quantum computing: eventually, you run out of PhD students.

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If you have a question about this talk, please contact James Fergusson .

Quantum computing based on quantum dots in silicon is starting to move from university laboratories and into commercial settings. With this progress, scientists are having to adapt to a new way of working. Instead of picking a good device and studying it for potentially months on end, 100s or even 1000s of devices must tuned up, measured and analysed in quick succession. At this point, even the most able PhD student becomes a bottleneck in the process. I will talk about why Quantum Motion are using silicon chips from commercial foundries as a potential quantum computing architecture, the challenges that this approach produces and how (some) of these challenges can be tackled with machine learning techniques.

This talk is part of the Data Intensive Science Seminar Series series.

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