Victor Kasatkin “Quantum Machine Learning“

Abstract: The field of quantum machine learning attempts to answer the following question: can near-term quantum devices (NISQ: noisy intermediate scale quantum computers) demonstrate a speedup over classical state of the art in practical machine-learning problems. While this question remains open, a number of recent academic contributions explored promising ideas in this area. In this talk we present an overview of these ideas and challenges towards their practical application. These include the algorithms achieving exponential speedup in linear algebra operations but requiring quantum access to the data, quantum kernel estimation support vector machines, and quantum Boltzmann machines. Victor Kasatkin: Viterbi School of Engineering of the University of Southern California Announcement: Seminar site: