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Physics Seminar

Deep Learning for Quantum Technologies

Speaker: Prof Florian Marquardt (Friedrich Alexander University Erlangen-Nuremberg)
Date: Wednesday 9 June 2021
Time: 15:00
Venue: Zoom

Machine learning that exploits the power of deep neural networks has the potential to transform the way we do science. In this talk, I will describe our recent efforts to apply deep reinforcement learning to questions related to quantum computation. Reinforcement learning is a technique where the computer discovers from scratch novel strategies to solve a given problem as well as possible. I will recall our initial work on quantum error correction and then will describe our most recent result, where we used deep reinforcement learning to find strategies to optimize quantum circuits.

Bio: Florian Marquardt is a theoretical physicist whose current focus is on applying machine learning to scientific discovery and discovering physical systems that help for machine learning. He has a long-standing track record in areas bridging nanophysics and quantum optics, among them significant contributions to the theory of cavity optomechanics and the theory of superconducting circuit quantum electrodynamics. He is currently a scientific director at the Max Planck Institute for the Science of Light in Erlangen, Germany, as well as a professor at the local university. He studied at the university of Bayreuth, Germany, then did his PhD in Basel, Switzerland (finishing in 2002), afterwards went on to a postdoctoral stay at Yale university and a junior research group leader position at the university of Munich, before moving to Erlangen.