Deep learning of many-body observables and quantum information scrambling
Mohseni N, Shi J, Byrnes T, Hartmann M (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 8
DOI: 10.22331/q-2024-07-18-1417
Abstract
Machine learning has shown significant breakthroughs in quantum science, where in particular deep neural networks exhibit remarkable power in modeling quantum many-body systems. Here, we explore how the capacity of data-driven deep neural networks in learning the dynamics of physical observables is correlated with the scrambling of quantum information. We train a neural network to find a mapping from the parameters of a model to the evolution of observables in random quantum circuits for various regimes of quantum scrambling and test its generalization and extrapolation capabilities in applying it to unseen circuits. Our results show that a specific type of recurrent neural network can generalize its predictions within the system size and time window that it has been trained on across both, localized and scrambled regimes. Moreover, the considered neural network succeeds in extrapolating its predictions beyond the time window and system size that it has been trained on for models that show localization, but not in scrambled regimes.
Authors with CRIS profile
Naeimeh Mohseni
Lehrstuhl für Theoretische Physik
Michael Hartmann
Lehrstuhl für Theoretische Physik
Additional Organisation(s)
Involved external institutions
China (CN)How to cite
APA:
Mohseni, N., Shi, J., Byrnes, T., & Hartmann, M. (2024). Deep learning of many-body observables and quantum information scrambling. Quantum, 8. https://doi.org/10.22331/q-2024-07-18-1417
MLA:
Mohseni, Naeimeh, et al. "Deep learning of many-body observables and quantum information scrambling." Quantum 8 (2024).
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