Deep Learning of Quantum Many-Body Dynamics via Random Driving
- URL: http://arxiv.org/abs/2105.00352v4
- Date: Wed, 16 Nov 2022 10:09:58 GMT
- Title: Deep Learning of Quantum Many-Body Dynamics via Random Driving
- Authors: Naeimeh Mohseni, Thomas F\"osel, Lingzhen Guo, Carlos
Navarrete-Benlloch, and Florian Marquardt
- Abstract summary: We show the power of deep learning to predict the dynamics of a quantum many-body system.
We show the network is able to extrapolate the dynamics to times longer than those it has been trained on.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neural networks have emerged as a powerful way to approach many practical
problems in quantum physics. In this work, we illustrate the power of deep
learning to predict the dynamics of a quantum many-body system, where the
training is \textit{based purely on monitoring expectation values of
observables under random driving}. The trained recurrent network is able to
produce accurate predictions for driving 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 learn
the 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 a quantum many-body system that might be open,
noisy, or disordered, without any need for a detailed understanding of the
system. This scheme provides considerable speedup for rapid explorations and
pulse optimization. Remarkably, we show the network is able to extrapolate the
dynamics to times longer than those it has been trained on, as well as to the
infinite-system-size limit.
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