Computer Science Guest Speaker Series - Understanding the role of data and learning through a quantum lens

THE DEPARTMENT OF COMPUTER SCIENCE GRADUATE SEMINAR 2022
Guest Speaker Series Presented By:

Dr. Jarrod McClean
"Understanding the role of data and learning through a quantum lens"

Friday, November 4th, 2022
1 pm

Abstract:
As quantum technology continues to rapidly advance, it is interesting to stop and ask what it has already taught us about how we do science. If we believe both that quantum computers may be able to do some computations exponentially faster than their classical counterparts and that we live in a quantum world, then our ability to learn from observational data as scientists may fundamentally change what we can do. Here, I will first give a broad introduction to quantum computing and quantum science in general. I will then review some recent results in quantum machine learning that allow us to put ideas about learning from the physical world on a rigorous footing. We then show that quantum computers, and more specifically quantum memory, offer us an opportunity to learn from a quantum world with exponentially less data than traditional experiments. This exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today's relatively noisy quantum processors. I will then give an outlook on this technology and challenges that we face in expanding the reach of quantum technology in learning.


Dr. Jarrod McClean is a staff research scientist in Google's Quantum Artificial Intelligence Lab working on the development of practical quantum algorithms for quantum simulation and other problems. He received his PhD in Chemical Physics from Harvard University specializing in quantum chemistry and quantum computation supported by the US Department of Energy as a Computational Science Graduate Fellow. His research interests broadly include quantum computation, machine learning, artificial intelligence, and the limits of computation. Jarrod is often known for the invention of a popular algorithm in quantum computing, the variational quantum eigensolver. Recently, he was part of the team that achieved beyond classical computation on a quantum computer, and personally showed the first formal separation between classical algorithms that can learn from data and traditional computation.


To register for this virtual event, please email grad.compsci@lakeheadu.ca and a Zoom link will be shared.

Everyone is welcome.