English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Machine Learning and Quantum Devices

MPS-Authors
/persons/resource/persons201125

Marquardt,  Florian
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Friedrich-Alexander-Universität Erlangen-Nürnberg;

External Resource
No external resources are shared
Fulltext (public)

2101.01759.pdf
(Any fulltext), 5MB

Supplementary Material (public)

21_Marquardt.png
(Supplementary material), 40KB

Citation

Marquardt, F. (2021). Machine Learning and Quantum Devices. arXiv, 2101.01759.


Cite as: http://hdl.handle.net/21.11116/0000-0007-AC4C-3
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 back-propagation, 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 timetraces), 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.