Reinforcement Learning for Quantum Technologies
Abstract: In this talk, I will illustrate how a set of techniques from computer science that go under the name of reinforcement learning can be helpful in modern quantum devices. These techniques allow to discover from scratch quantum control and feedback strategies, which can help to prepare and stabilize quantum states and perform quantum error correction. In addition, whole quantum circuits can be optimized with the help of these approaches. Beyond our theoretical proposals in this area I will also discuss the first reinforcement learning of real-time quantum feedback in an experiment, performed in a collaboration with the superconducting-qubit team at ETH.
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.
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