BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Maria Schuld (Xanadu) DTSTART:20241105T150000Z DTEND:20241105T160000Z DTSTAMP:20260423T021427Z UID:TalentQ/13 DESCRIPTION:Title: But why would we use quantum computers after all? Approaching Quantum Mac hine Learning a little differently\nby Maria Schuld (Xanadu) as part o f Quantum Spain\n\n\nAbstract\nThe last years of research in quantum machi ne learning have taught us a lot. There are problems where quantum compute rs have a provable advantage for learning (just apply Shor somewhere!). Tr aining variational "quantum neural networks" is a matter of a few lines of code\, but you need to be careful not to be dequantized\, and the results are a little disappointing. We all hope that things look better for "quan tum data". And a lot has been written about barren plateaus. But why\, on earth\, should we use quantum computers for machine learning at all? It se ems that we have not come any closer to answering this question. In this i nformal talk based on arXiv2409.00172\, I suggest a slightly different app roach to QML: One where we stare hard at a famous family of quantum algori thms\, try to understand why they work (not when they are faster) and muse how this could be turned into a learning principle. Expect no speedup and no end-to-end learning algorithm\, but a lot of educated speculation.\n LOCATION:https://researchseminars.org/talk/TalentQ/13/ END:VEVENT END:VCALENDAR