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SUMMARY:Machine learning for scalable quantum computing: eventually\, you 
 run out of PhD students. - David Wise - Quantum Motion
DTSTART:20230427T120000Z
DTEND:20230427T143000Z
UID:TALK198655@talks.cam.ac.uk
CONTACT:James Fergusson
DESCRIPTION:Quantum computing based on quantum dots in silicon is starting
  to move from university laboratories and into commercial settings. With t
 his progress\, scientists are having to adapt to a new way of working. Ins
 tead of picking a good device and studying it for potentially months on en
 d\, 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 usin
 g 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.
LOCATION:Kavli Large Meeting Room\, Kavli Building
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