# SKM 2023 – wissenschaftliches Programm

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# TUT: Tutorien

## TUT 1: Physics Meets Machine Learning (joint session DY/TUT/TT)

### TUT 1.1: Tutorium

### Sonntag, 26. März 2023, 16:00–16:45, HSZ 01

**Machine Learning for Quantum Technologies** — •Florian Marquardt — Max Planck Institute for the Science of Light and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany

Machine learning is revolutionizing science and technology. In the past few years, it has become clear that it promises significant benefits as well for the development of quantum technologies. In this tutorial I will first give a brief introduction to neural networks. I will then discuss a number of areas and examples in which machine learning is being successfully applied in this context. These include measurement data analysis and quantum state representation, approximate quantum dynamics, parameter estimation, discovering strategies for hardware-level quantum control, the optimization of quantum circuits, and the discovery of quantum experiments, discrete quantum feedback strategies, and quantum error correction protocols.

Reference: "Artificial intelligence and machine learning for quantum technologies", M. Krenn, J. Landgraf, T. Foesel, and F. Marquardt, Phys. Rev. A 107, 010101 (2023).