Artificial Intelligence and Machine Learning for Quantum Technologies

Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt
8/7/2022
quant-phcs.AIcs.LG

No official code found. Best implementation: ML4QTech/Collection (51 stars, last commit 2022-08-13).

Abstract

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade.

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ML4QTech/CollectionCommunity100%
5110Jul 21, 20224 years ago

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Cite this paper

@article{krenn2022artificial,
  title  = {Artificial Intelligence and Machine Learning for Quantum Technologies},
  author = {Mario Krenn and Jonas Landgraf and Thomas Foesel and Florian Marquardt},
  year   = {2022},
  eprint = {2208.03836},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.03836v1}
}

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