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  Deep Learning of Quantum Many-Body Dynamics via Random Driving

Mohseni, N., Fösel, T., Guo, L., Navarrete-Benlloch, C., & Marquardt, F. (2021). Deep Learning of Quantum Many-Body Dynamics via Random Driving. arXiv, 2105.00352.

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Mohseni, Naeimeh1, Author
Fösel, Thomas1, 2, Author              
Guo, Lingzhen1, Author
Navarrete-Benlloch, Carlos1, 3, 4, Author              
Marquardt, Florian1, 2, Author              
Affiliations:
1Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society, ou_2421700              
2Physics Department, University of Erlangen-Nuremberg, Staudtstr. 5, 91058 Erlangen, Germany, ou_persistent22              
3Wilczek Quantum Center, School of Physics and Astronomy,Shanghai Jiao Tong University, Shanghai 200240, China, ou_persistent22              
4Shanghai Research Center for Quantum Sciences, Shanghai 201315, China, ou_persistent22              

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 Abstract: Neural networks have emerged as a powerful way to approach many practical problems in quantumphysics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantummany-body system, where the training isbased purely on monitoring expectation values of observablesunder random driving. The trained recurrent network is able to produce accurate predictions fordriving trajectories entirely different than those observed during training. As a proof of principle,here we train the network on numerical data generated from spin models, showing that it can learnthe dynamics of observables of interest without needing information about the full quantum state.This allows our approach to be applied eventually to actual experimental data generated from aquantum many-body system that might be open, noisy, or disordered, without any need for a detailedunderstanding of the system. This scheme provides considerable speedup for rapid explorations andpulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to timeslonger than those it has been trained on, as well as to the infinite-system-size limit.

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 Dates: 2021-05-012021-05-01
 Publication Status: Published online
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 Identifiers: arXiv: 2105.00352
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Title: arXiv
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