Quantum Computing and Machine Learning

Topic repeated at 2 different times, please join the session that is best suited for your time zone.
Quantum computing is poised to potentially have an impact on machine learning methods. In this seminar, we will cover the current state and future prospects of machine learning with quantum computers. This includes algorithms and models such as quantum kernel estimation, variational quantum classifiers, quantum neural networks, and quantum generative-adversarial networks (QGANs). We will also demonstrate the capabilities of the Qiskit Machine Learning open source software project.

Note that this is part 4 of an 8-session series on Quantum Computing on Mar 22/23, Apr 5/6, Apr 19/20, May 3/4, May 17/18, May 31/Jun 1, Jun 14/15, Jul 5/6. The sessions are not prerequisites for each other, and are not recorded. We will provide reference links and do quick recaps of previous content as required, so if you miss an earlier session, you can still get value from subsequent sessions.

Presenter: Dr. Sean Wagner

Sean Wagner is a Research Scientist and a Quantum Technical Ambassador at IBM. When he's not programming and experimenting with Qiskit, Sean spends his time working with researchers at academic institutions and industry partners in Canada on projects involving high-performance computing, hardware acceleration, quantum computing, and data science and AI.

Dr. Wagner holds a B.A.Sc. degree in Computer Engineering from the University of Waterloo, and M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Toronto.

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Please join us at the session that is best suited to your time zone. Note that this topic is:
1. Repeated at two different times to accommodate various time zones, because it is
2. Posted simultaneously in multiple meetup groups world-wide
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It is recommended that you register at this Webex link ahead of time to receive a calendar invite and reminder. https://ibm.webex.com/weblink/register/rb70561f1b19a08a765f17d382e0e8d24