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  Machine Learning and Quantum Devices

Marquardt, F. (submitted). Machine Learning and Quantum Devices.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0007-AC4C-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0007-B032-9
Genre: Paper

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 Creators:
Marquardt, Florian1, 2, Author              
Affiliations:
1Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, ou_2421700              
2Friedrich-Alexander-Universität Erlangen-Nürnberg, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2021-01-05
 Publication Status: Submitted
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 Identifiers: arXiv: 2101.01759
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Title: arXiv
Source Genre: Commentary
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Publ. Info: Cornell University Press
Pages: - Volume / Issue: - Sequence Number: 2101.01759 Start / End Page: - Identifier: -