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PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:1
UID:UW-Physics-Event-6180
DTSTART:20210113T170000Z
DTEND:20210113T181500Z
DTSTAMP:20260415T023503Z
LAST-MODIFIED:20210110T181026Z
LOCATION:Online Seminar: Please sign up for our mailing list at www.ph
 ysicsmeetsml.org for zoom link
SUMMARY:Quantum Machine Learning in High Energy Physics\, Physics ∩ 
 ML Seminar\, Sofia Vallecorsa\, CERN
DESCRIPTION:The High Energy Physics community has a long tradition of 
 using machine learning to solve specific tasks\, in particular related
  to a more efficient selection of interesting events over the overwhel
 ming background produced at colliders such as the LHC. In the recent y
 ears\, several studies have demonstrated the benefits of using deep le
 arning techniques and building on these examples\, many High Energy Ph
 ysics experiments are now working on integrating deep learning into th
 eir workflows for different applications: from pattern recognition\, t
 o real-time selection of interesting collision events\, to simulation 
 and data analysis. At the same time\, quantum computing represents a m
 ost promising breakthrough in computing technology. Today’s hardware
  has not yet reached the level at which it could be put into productio
 n but the number of R&D activities in terms of hardware\, programming 
 platforms\, simulators and applications increases both in the academic
  world and industry. Given both the potential and the uncertainty surr
 ounding this domain\, it is important to explore what these technology
  could bring to High Energy Physics and understand which of our activi
 ties could most benefit from quantum computing algorithms. The availab
 ility of quantum simulators and the development of programming platfor
 ms dedicated to quantum systems provide an exciting environment to sta
 rt developing more realistic applications within our domain. This talk
  highlights the first generation of ideas that use quantum machine lea
 rning on problems in High Energy Physics and provide an outlook on fut
 ure applications.
URL:https://www.physics.wisc.edu/events/?id=6180
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