Quantum Science Seminar #59

Classical Machine Learning for Quantum Technologies

November 04, 2021
Florian Marquardt - Max-Planck-Institute for the Science of Light (Erlangen - Germany)

In this talk, I will give an overview of current efforts to employ classical machine learning for quantum technologies. Recent years have seen machine learning techniques like deep neural networks revolutionize many fields of science and technology. Since about 2016, they are being applied increasingly with success to challenges for quantum technologies. Examples include the use of neural networks for interpreting measurement results, generation of novel experimental setups, and deep reinforcement learning for discovering strategies in quantum control and feedback or for optimizing quantum circuits.


[1] Florian Marquardt

"Machine learning and quantum devices"

SciPost Physics Lecture Notes, 29 (2021)

[2] Vedran Djunko and Hans Briegel

"Machine learning & artificial intelligence in the quantum domain: a review of recent progress"

Reports on Progress in Physics 81, 074001 (2018)

[3] Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová

"Machine learning and the physical sciences"

Reviews of Modern Physics 91, 045002 (2019)

Access the live talk at 17.00 CEST or the recording afterwards via this link:


Here you can find the recording of the last lecture by Sheila Rowan.

Quantum Science Seminar #58 - Sheila Rowan

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