Machine Learning in Cryogenics with the example of data analysis of phase transitions in high-temperature superconductors

Abstract: The usage of artificial neural networks and machine learning methods is presented in estimation of critical temperature from AC susceptibility in cryogenics experiments. Various methodologies are presented. Particular cryogenic experiments are described and future perspectives are drawn.

[ arxiv.org/abs/2002.03452 ]

Computer scienceMathematicsPhysics

Audience: researchers in the topic

( paper )

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Seminars on Quantum Technologies

Series comments: Recording of the seminars are available at YouTube Channel : Quantum Hardware Systems www.youtube.com/channel/UCq9O-MHMMfpR7T-dcHwgbsA .

teams.microsoft.com/l/meetup-join/19%3ameeting_MzM2ZDg3Y2EtOWRmYy00ZjdjLThmNTgtZDBjYjhmOTZhMTY3%40thread.v2/0?context=%7b%22Tid%22%3a%2267ea5955-9b5c-4693-a8f9-960f2a3b49bb%22%2c%22Oid%22%3a%221e0d02d8-58da-4901-85d0-31938494a2b5%22%7d

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