In this session, attendees will learn to filter signal from noise in discussions about quantum machine learning (QML), from the perspective of a quantum computer scientist, decade-long AI practitioner, and experienced enterprise technology executive.
We will begin by learning about what a quantum computer is and how it works, and exploring at high level the types of algorithmic innovations offered by both near-term and large-scale quantum computers. We will then explore how quantum computers could be combined with the current state-of-the-art in machine learning (ML) algorithms, leading in some cases to more efficient quantum-enhanced ML algorithms, or potentially for entirely new fully-quantum ML algorithms. We will define concrete use cases and industries where quantum machine learning is likely to have the biggest impact on science and business, and talk about specific opportunities for AI advancement in these areas. A timeline for the development of quantum computers, propositions for architectural/service model considerations for QML platforms, and a discussion of what quantum machine learning cannot do will close out the presentation, offering attendees a sense of why this technology works, where it is going, what types of problems it can (and cannot) solve, how it will impact organizations, and where and why it matters compared to classical (non-quantum) approaches to machine intelligence.
Jennifer Fernick is a computer science researcher, cybersecurity executive, technical advisor, and speaker. She currently serves as Director, Information Security at Scotiabank. She is also completing a PhD in Computer Science from the University of Waterloo, where her research involves quantum algorithms, computational complexity, and cryptography. She is a member of the Institute for Quantum Computing and the Centre for Applied Cryptographic Research, and was a part of the 2018 cohort of the Berkman Assembly at Harvard University and MIT Media Lab, focusing on Artificial Intelligence & its Governance. Her career has included designing and building satellite systems, working on bleeding edge cryptography research, and leading the development of global technology standards. Most recently, she served as Senior Cryptographic Security Architect for a major multinational bank. She holds a Master of Engineering degree in Systems Design Engineering from the University of Waterloo, and an Honours Bachelor of Science in Cognitive Science & Artificial Intelligence from the University of Toronto. A highly-regarded speaker, Jennifer has spoken at major technology conferences such as RSA, Blackhat, ECML, and DEF CON.
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