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Applied Data Science and Quantum Computing in Machine Learning

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Applied Data Science and the Emerging Role of Quantum Computing in Machine Learning

Data science has been rapidly growing over the past decade, and its applications have become ubiquitous in our daily lives. As these applications consume more data and need faster response times, new technologies and algorithms are needed to meet the computational demands. Quantum computing is a highly promising emerging technology that could present significant opportunities to accelerate the training of machine learning algorithms and improve data science methods.

This presentation will provide an overview of data science, with a focus on practical applications in industry. The current state of quantum computing technologies will also be explored, including some of the ways that quantum computing can be harnessed to advance machine learning.


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 BS and MS in Electrical Engineering at NC State and 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.

About QxBranch
QxBranch ( develops and deploys advanced data analytics models for global companies in finance, insurance, technology, and sports. We have a diverse team of professionals in systems and software engineering, machine learning, quantum computing, and cyber security.
QxBranch is headquartered in Washington, D.C., with offices in London and Australia. For the most up to date career opportunities, please

Earlier Event: February 7
Arthena: Tech Talk + Recruiting!
Later Event: February 21
Women in Data Science Panel