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METHOD:PUBLISH
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X-ORIGINAL-URL:https://iqus.uw.edu/
X-WR-CALNAME:IQuS
X-WR-CALDESC:InQubator for Quantum Simulation
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BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-4e9cec1f583056459111d63e24f3b8ef@iqus.uw.edu
DTSTART:20230301T220000Z
DTEND:20230301T230000Z
DTSTAMP:20230127T163900Z
CREATED:20230127
LAST-MODIFIED:20230301
PRIORITY:5
SEQUENCE:0
TRANSP:OPAQUE
SUMMARY:Quantum AI: From near-term to fault-tolerance
DESCRIPTION:Junyu Liu, IBM/University of Chicago\n \n\nQuantum machine learning, namely, running machine learning algorithms on quantum devices, has been considered a flag-ship application of quantum computing. In this talk, we will describe two perspectives of quantum machine learning: near-term algorithms and fault-tolerant algorithms. In the near-term realizations, I will discuss applications of variational quantum circuits in machine learning problems, and how a theory of quantum neural tangent kernel could be an analytic principle to optimize quantum neural networks. In the fault-tolerant realizations with quantum error correction, I will briefly discuss some ongoing works with end-to-end applications of the HHL algorithm that provides a provable, generic quantum advantage to a class of machine learning problems. Our works show that fundamental physics research, such as chaos and dissipation, quantum field theory and quantum gravity, could be helpful for important and timely problems of (quantum) machine learning algorithm designs.\n
URL:https://iqus.uw.edu/events/quantum-ai-from-near-term-to-fault-tolerance/
CATEGORIES:Seminars
LOCATION:C421 Physics and Astronomy Building
ATTACH;FMTTYPE=image/jpeg:https://iqus.uw.edu/wp-content/uploads/2023/01/Junyu_Liu_500.jpeg
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