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DTSTART:20250613T100000Z
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LOCATION:Auditorium
SUMMARY:ICFO | MARIA SCHULD
CLASS:PUBLIC
DESCRIPTION:ABSTRACT:&nbsp\;\nThe discipline of Quantum Machine Learning ha
 s grown into a popular subfield of quantum computing in the&nbsp\;past yea
 rs\, but finds itself at a crossroads. The claim that trainable quantum ci
 rcuits will outperform neural networks is increasingly under scrutiny\, an
 d known speedups for learning tasks fail to translate into practical appli
 cations. Even worse\,&nbsp\;we don&rsquo\;t seem any closer to understandi
 ng why quantum algorithms could potentially be useful for AI. In this talk
  I want to give an overview of the status quo of&nbsp\;Quantum Machine&nbs
 p\;Learning\, but also advocate for a change in perspective: I will motiva
 te&nbsp\;why the core of Shor's famous algorithm\, an ultra-fast implement
 ation of a Fourier Transform\, could become a unique and useful ingredient
  that&nbsp\;unlocks new ways of learning from data.\nBIO:\nMaria leads the
  quantum machine learning research team at Xanadu\, a Toronto-based quantu
 m computing start-up. She co-authored a book as well as many papers on the
  topic of how quantum computers can help to generalise from data\, and is 
 one of the original developers of the PennyLane software framework for qua
 ntum differentiable programming. Maria received her PhD degree in physics 
 from the University of KwaZulu-Natal in South Africa in 2017\, but also ho
 lds a postgraduate degree in political science and still spends some of he
 r time on the intersection of machine learning and social sciences researc
 h.
DTSTAMP:20260428T225914Z
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