Machine learning and quantum devices

Marquardt F (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Journal Issue: 29

DOI: 10.21468/SciPostPhysLectNotes.29

Abstract

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforcement learning (for discovering control strategies), recurrent neural networks (for analyzing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.

Authors with CRIS profile

Florian Marquardt Lehrstuhl für Theoretische Physik

How to cite

APA:

Marquardt, F. (2021). Machine learning and quantum devices. SciPost Physics, 29. https://dx.doi.org/10.21468/SciPostPhysLectNotes.29

MLA:

Marquardt, Florian. "Machine learning and quantum devices." SciPost Physics 29 (2021).

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