NISQ-era Quantum Devices for Hard Tasks in Machine Learning w/Jonathan Foldager

Content level: 200 -- Event is for the "Quantum Climber"

Title: Using NISQ-era Quantum Devices for Hard Tasks in Machine Learning

Speaker: Jonathan Foldager, PhD Student in Machine Learning & Quantum Computing, Copenhagen, Denmark

ABSTRACT: Through popular science publications on what quantum computing can do for machine learning, one is likely to get the impression that a revolution in computational power is just around the corner. But the reality is likely much more strict due to the fact that current and near-term quantum computers are small, noisy and can only do few bursts of calculations before the information is lost. In my talk, I will introduce recent ideas and highlight specific protocols on how to use near-term quantum devices for hard tasks in machine learning. I will try to take a basic introductory standpoint and subsequently explain my research focus.

BIO: Jonathan is a PhD student in machine learning and quantum computing at the Technical University of Denmark. He holds a BSc and MSc in biomedical engineering with strong focus on statistical machine learning methods. His current work focuses on how quantum computers can be used to solve computationally hard problems in machine learning, with emphasis on architectures such as variational circuits.

These "Meetup-Lite" events are Free and are online-only {connect & password information will be emailed to registered participants ~30 minutes prior to meeting}

Event artifacts, e.g. slides & recording (when available):
http://quantum.harrisburg.tech/meetups/20200915/

For a current listing of quantum-related live online events--globally--visit http://live.quantumapalooza.com/

Sponsor: Harrisburg University of Science and Technology
https://harrisburgU.edu/quantum email: quantum@HarrisburgU.edu