Quantum Computing and Machine Learning

For our March Data Science DC Meetup, we will be exploring the rapidly developing field of quantum computing. John Kelly and Tristan Cook, of QxBranch, will introduce us to quantum computing and discuss what it can bring to machine learning.

Agenda:

• 6:30pm -- Networking, Empanadas, and Refreshments

• 7:00pm -- Introduction, Announcements

• 7:15pm -- Presentation and Discussion

• 8:30pm -- Data Drinks (Tonic , 2036 G St NW)

Abstract:

The pace of development of quantum computing technologies has accelerated in recent years and along with it the understanding of their impact on solving today’s computationally intractable problems. Machine learning is one discipline in particular where this emerging technology will present significant opportunities. Quantum computing has the potential to provide vast improvements to existing algorithms, as well as potential new methods for data exploration.

This presentation will provide an overview of the state-of-the-art in quantum computing and the emerging industry ecosystem, and then explore some of the ways that the technology can be harnessed for advances in machine learning. Software engineers from Washington DC based startup, QxBranch, will guide attendees through some example algorithms and applications.

Bios:

John Kelly, Ph.D

John Kelly, Director of Analytics at QxBranch, is leading the company’s development of advanced data analytics technologies. Previously, he was the Technical Lead for Corporate Data Analytics at Lockheed Martin. John has experience applying machine learning to a diverse set of domains including healthcare, supply chain optimization, sustainment, and program management. He completed his Ph.D. in Electrical and Computer Engineering at Carnegie Mellon University, where his work focused on machine learning and signal processing algorithms for brain-computer interfaces.

Tristan Cook

Tristan Cook is a systems engineer at QxBranch, with extensive experience in investigating quantum computing applications and developing algorithms for D-Wave quantum computers. He was previously a mechatronics engineer at Shoal, where he worked on a variety of complex systems engineering projects such as the SUSat, a microsatellite developed for the international QB50 project. Tristan graduated from the University of Adelaide with a Bachelor of Mechatronic Engineering and a Bachelor of Mathematical & Computer Sciences.

Sponsors:

This event is sponsored by the George Washington Business School MS in Business Analytics Program (http://business.gwu.edu/programs/specialized-masters/m-s-in-business-analytics/academic-program/), Statistics.com (http://bit.ly/12YljkP), Elder Research (http://datamininglab.com/), Booz Allen Hamilton (https://www.boozallen.com/consulting/strategic-innovation/nextgen-analytics-data-science), AOL (http://engineering.aol.com/), and Data Society (http://datasociety.co/kickstarter). (Would your organization like to sponsor too? Please get in touch!)

Also, here's a special note from Data Community DC's newest educational partner and sponsor, Data Society (http://datasociety.co/). They have a limited-time offer during the Kickstarter (https://www.kickstarter.com/projects/978832055/data-science-for-everyone) campaign of LIFETIME ACCESS to the hands-on data science bootcamp. You will learn how to use supervised and unsupervised machine learning in your work, how to use text mining to classify text data (tweets, blogs, e-mails) and how to mine social media to identify key connectors and influencers (and lots more!). The courses are taught in R and provide you with downloadable code templates and companion guides. Less than 3 weeks left before the Kickstarter, and this offer, ends for good!