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SUMMARY:Quantum Machine Learning: An Overview
DTSTART;VALUE=DATE-TIME:20260910T090000Z
DTEND;VALUE=DATE-TIME:20260910T094000Z
DTSTAMP;VALUE=DATE-TIME:20260922T182702Z
UID:indico-contribution-2049@events.ncbj.gov.pl
DESCRIPTION:Speakers: Tobias Christoph Sutter (University of Vienna)\nQuan
 tum machine learning asks whether quantum computers can offer machine lear
 ning capabilities beyond classical methods. Assessing this potential requi
 res a firm understanding of its fundamental concepts\, the basic models\, 
 and their limitations. This talk reviews these foundations and the challen
 ges of working with quantum devices before focusing on supervised quantum 
 machine learning with variational quantum circuits (VQCs). We discuss how 
 to encode classical data into quantum models and how to train their parame
 ters using gradient-based optimization. Two VQC-based architectures serve 
 as case studies: data re-uploading and dissipative quantum neural networks
 . We conclude with some central questions facing the field\, including the
  trainability and scalability of variational models.\n\nhttps://events.ncb
 j.gov.pl/event/468/contributions/2049/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2049/
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