Artificial neural network states for non-additive systems

Rzadkowski, Wojciech; Lemeshko, Mikhail; Mentink, Johan H.

Artificial neural network states for non-additive systems

Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, 10.48550/arXiv.2105.15193.

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https://arxiv.org/abs/2105.15193 [Preprint]

DOI

10.48550/arXiv.2105.15193

Preprint | Submitted | English

Author

Rzadkowski, Wojciech^ISTA ; Lemeshko, Mikhail^ISTA ; Mentink, Johan H.

Corresponding author has ISTA affiliation

Department

Lemeshko Group

Grant

Angulon: physics and applications of a new quasiparticle
International IST Doctoral Program

Abstract

Methods inspired from machine learning have recently attracted great interest in the computational study of quantum many-particle systems. So far, however, it has proven challenging to deal with microscopic models in which the total number of particles is not conserved. To address this issue, we propose a new variant of neural network states, which we term neural coherent states. Taking the Fröhlich impurity model as a case study, we show that neural coherent states can learn the ground state of non-additive systems very well. In particular, we observe substantial improvement over the standard coherent state estimates in the most challenging intermediate coupling regime. Our approach is generic and does not assume specific details of the system, suggesting wide applications.

Publishing Year

2021

Date Published

2021-05-31

Journal Title

arXiv

Acknowledgement

We acknowledge fruitful discussions with Giacomo Bighin, Giammarco Fabiani, Areg Ghazaryan, Christoph Lampert, and Artem Volosniev at various stages of this work. W.R. is a recipient of a DOC Fellowship of the Austrian Academy of Sciences and has received funding from the EU Horizon 2020 programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. M. L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). This work is part of the Shell-NWO/FOM-initiative “Computational sciences for energy research” of Shell and Chemical Sciences, Earth and Life Sciences, Physical Sciences, FOM and STW.

Page

2105.15193

IST-REx-ID

10762

Cite this

Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv. doi:10.48550/arXiv.2105.15193

Rzadkowski, W., Lemeshko, M., & Mentink, J. H. (n.d.). Artificial neural network states for non-additive systems. arXiv. https://doi.org/10.48550/arXiv.2105.15193

Rzadkowski, Wojciech, Mikhail Lemeshko, and Johan H. Mentink. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2105.15193.

W. Rzadkowski, M. Lemeshko, and J. H. Mentink, “Artificial neural network states for non-additive systems,” arXiv. .

Rzadkowski W, Lemeshko M, Mentink JH. Artificial neural network states for non-additive systems. arXiv, 10.48550/arXiv.2105.15193.

Rzadkowski, Wojciech, et al. “Artificial Neural Network States for Non-Additive Systems.” ArXiv, doi:10.48550/arXiv.2105.15193.

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