Quantum Machine Learning : Prospects and Challenges
Abstract
Iordanis Kerenidis
We will review recent work on Quantum Machine Learning and discuss the prospects and challenges of applying this new exciting computing paradigm to machine learning applications.
Panelists
Julia Kempe |
Krysta Svore |
Ronald de Wolf |
Speaker Bio
Iordanis Kerenidis (CNRS and QC Ware) received his Ph.D. from the Computer Science Department at the University of California, Berkeley, in 2004. After a two-year postdoctoral position at the Massachusetts Institute of Technology, he joined the Centre National de Recherche Scientifique in Paris as a permanent researcher. He has been the coordinator of a number of EU-funded projects including an ERC Grant, and he is the founder and director of the Paris Centre for Quantum Computing. His research is focused on quantum algorithms for machine learning and optimization, including work on recommendation systems, classification and clustering. He is currently working as the Head of Quantum Algorithms Int. at QC Ware Corp.