Speaker
Prof.
Frank Pollmann
(TUM)
Description
Abstract
The interplay of quantum fluctuations and interactions can yield to novel quantum phases of matter with fascinating properties. Understanding the physics of such systems is a very challenging problem as it requires to solve quantum many body problems—which are generically exponentially hard on classical computers. In this context, universal quantum computers are potentially an ideal setting for simulating the emergent quantum many-body physics. Here we discuss one concrete application: We use quantum convolutional neural networks (QCNNs) as classifiers and introduce an efficient framework to train these networks.
