Journal Club for Quantum Physics and Machine Learning

Synopsis

The Journal Club is organized by the ultracold.org team. Our aim is to gain an overview of the state of the art and developments in the field of quantum physics and machine learning.

 

Organization

Every two weeks, a different participant selects and presents a relevant recent article or pre-print on the subject. The presentations should last around 30 minutes, focus on stimulating discussion about the selected contribution and possibly point to pertinent related literature. The presentations are informal and on voluntary basis. We also solicit contributed talks of own original work and presentations of suitable review articles. 

 

Schedule

  

Participation

The Journal Club takes place on zoom every second Tuesday at 3pm CET. Please send an e-mail to theThis email address is being protected from spambots. You need JavaScript enabled to view it. if you would like to receive the Zoom link before every session, or if you want to present or suggest a paper. Contact: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Paper suggestions

This is a list of interesting papers that could be suitable for the Journal Club:

  1. Statistical Physics of Unsupervised Learning with Prior Knowledge in Neural Networks, Phys. Rev. Lett. 124, 248302 (2020).

  2. Extrapolating Quantum Observables with Machine Learning: Inferring Multiple Phase Transitions from Properties of a Single Phase, Phys. Rev. Lett. 121, 255702 (2018).

  3. Mastering Atari, Go, chess and shoji by planning with a learned model, Nature 588, 604–609 (2020).

  4. Extracting Interpretable Physical Parameters from Spationtemporal Systems Using Unsupervised Learning, Phys. Rev. X 10, 031056 (2020).

  5. Analyzing non-equilibrium quantum states through snapshots with artificial neural networks, arXiv:2012.11586 (2020).

  6. Is deeper better? It depends on locality of relevant features, arXiv:2005.12488 (2020).

  7. Fourier neural operator for Parametric Partial Differential Equations, arXiv:2010.08895 (2020).

  8. Machine learning in spectral domain, arXiv:2005.14436 (2020).

  9. Quantum inspired K-means algorithm using matrix product states, arXiv:2006.06164 (2020).

  10. Learning Potentials of Quantum Systems using Deep Neural Networks, arXiv:2006.13297 (2020).

  11. Random Sampling Neural Network for Quantum Many-Body Problems, arXiv:2011.05199 (2020).

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