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DTSTART:20220327T030000
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DTSTAMP:20260404T110149Z
UID:615ab8b5cbccc795435330@ist.ac.at
DTSTART:20211116T113000
DTEND:20211116T123000
DESCRIPTION:Speaker: Richard Küng\nhosted by Georgios Katsaros\nAbstract: 
 Extracting relevant information from a quantum system as efficiently and t
 ractably as possible is an important subroutine in most near-term applicat
 ions of quantum hardware.We present an efficient method for constructing a
 n approximate classical description of a quantum state using very few meas
 urements of the state. This description\, called a classical shadow\, can 
 be used to predict many different properties. The required number of measu
 rements is independent of the system size and saturates information-theore
 tic lower bounds. I will also illustrate how one can combine classical sha
 dows with machine learning (ML). This combination showcases that training 
 data obtained from quantum experiments can be very empowering for classica
 l ML methods. This is joint work with Robert Huang and John Preskill (both
  Caltech)\, as well as Giacomo Torlai (AWS) and Victor Albert (Qju Marylan
 d)
LOCATION:Heinzel Seminar Room / Office Bldg West (I21.EG.101)\, ISTA
ORGANIZER:swiddman@ist.ac.at
SUMMARY:Richard Küng: Classical shadows: efficient quantum-to-classical co
 nverters with many applications
URL:https://talks-calendar.ista.ac.at/events/3319
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