Modern applications of machine learning in quantum sciences

Dawid, Anna; Arnold, Julian; Requena, Borja; Gresch, Alexander; Płodzień, Marcin; Donatella, Kaelan; Nicoli, Kim A.; Stornati, Paolo; Koch, Rouven; Büttner, Miriam

Modern applications of machine learning in quantum sciences

Anna Dawid
(
- Warsaw U. and
- ICFO, Barcelona
)
,
Julian Arnold
(
- Basel U.
)
,
Borja Requena
(
- ICFO, Barcelona
)
,
Alexander Gresch
(
- Heinrich Heine U., Dusseldorf
)
,
Marcin Płodzień
(
- ICFO, Barcelona
)

Apr 8, 2022

288 pages

e-Print:

2204.04198 [quant-ph]

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Abstract: (arXiv)

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

Note:

288 pages, 92 figures. We have a publishing contract with Cambridge University Press. Figures and tex files are available at https://github.com/Shmoo137/Lecture-Notes

lectures
statistical
programming
feedback
many-body problem

References(614)

Figures(0)

References

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