D01.00001. Discovering feedback strategies for open quantum systems via deep reinforcement learning

Presented by: Florian Marquardt


Abstract

Recent rapid advances in deep neural networks are helping to revolutionize science and technology. In this talk, I will describe how neural networks can discover from scratch feedback strategies to help control open quantum systems, by exploiting the toolbox of reinforcement learning. A few-qubit quantum memory can be protected against decoherence via quantum error correction strategies that have been autonomously constructed in this way. Additional illustrative examples from our recent research include reinforcement learning applied to systems from the domain of cavity quantum electrodynamics and to arrays of coupled modes.

Authors

  • Florian Marquardt


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