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Advances in Hybrid Quantum-Classical Machine Learning
By Washington DC Quantum Computing MeetupFollow
When and where
Date and time
Saturday, July 8 · 10am – 12pm PDT
Location
Online
About this event
- 2 hours
- Mobile eTicket
The development of machine learning (ML) and quantum computing (QC) hardware has generated a lot of interest in creating quantum machine learning (QML) applications. This presentation will provide a broad overview of the hybrid quantum-classical machine learning approach, including key concepts such as quantum gradient calculation. Additionally, recent advancements in QML across multiple fields, including distributed or federated learning, natural language processing, reinforcement learning, sequential learning and classification, will be discussed. Potential benefits, scalability, and use cases of QML in the NISQ era will also be covered.
Bio:
Dr. Samuel Yen-Chi Chen received the Ph.D. and B.S. degree in physics from National Taiwan University, Taipei City, Taiwan. He is now a senior software engineer at Wells Fargo Bank. Prior to that, he was an assistant computational scientist in the Computational Science Initiative, Brookhaven National Laboratory. His research interests include building quantum machine learning algorithms as well as applying classical machine learning techniques to solve quantum computing problems. He won the First Prize In the Software Competition (Research Category) from Xanadu Quantum Technologies, in 2019.
Facilitators: Pawel Gora, CEO of Quantum AI Foundation. Dr. Sebastian Zajac, board member of QPoland.
Zoom registration form will be emailed no later than noon of July 8.
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https://www.eventbrite.com/e/advances-in-hybrid-quantum-classical-machine-learning-tickets-637828741937
