DATE:

Friday, February 26, 2021

TIME:

3:30 PM

LOCATION:

Virtual Zoom Conference

SPEAKER:

Dr. Andrew Sornborger, Research Scientist, Information Sciences, Los Alamos National Laboratory

ABSTRACT:

In this talk, I will show how quantum entanglement, a unique feature of quantum mechanical systems may be used as a resource for quantum machine learning (QML). I will describe how entanglement may be used to hugely reduce the amount of data needed to learn a quantum system. I will present a quantum simulation algorithm that makes use of entanglement-based QML to simulate dynamical quantum systems. I will go on to show that, in some circumstances, even huge reductions in data requirements cannot overcome fundamental difficulties in learning quantum systems (with an application to black holes); whereas, in other circumstances, it can (with an application to quantum convolutional neural networks).

Bio: Andrew Sornborger was trained as a physicist. He has worked in the fields of cosmology, particle physics, quantum computing, applied mathematics, multivariate data and signal analysis, neuroscience, and neural computing. He is currently focused on the development of methods and algorithms for quantum and neuromorphic computing.

HOST:

Jose Castillo and Fridolin Weber

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