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Machine learning study of the deformed one-dimensional topological superconductor

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Abstract

A one-dimensional p-wave topological superconductor deformed by a sine-square-deformation is studied in the framework of machine learning. A supervised learning algorithm is applied with a convolutional neural network to discern the existence of a Majorana zero mode, which is the hallmark of topological superconductivity. The machine learning algorithm learns features of the Majorana zero mode, and the neural network trained with the dataset from the link deformed case turns out to be the most effective.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2019R1F1A1058671).

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Authors and Affiliations

  1. Department of Physics, Sogang University, Seoul, 04107, South Korea

    Jae Hyuck Lee & Hyun Cheol Lee

  2. Department of Physics and Astronomy, Seoul National University, Seoul, 08826, South Korea

    Jae Hyuck Lee

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  1. Jae Hyuck Lee
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  2. Hyun Cheol Lee
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Correspondence to Hyun Cheol Lee.

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Lee, J.H., Lee, H.C. Machine learning study of the deformed one-dimensional topological superconductor. J. Korean Phys. Soc. 79, 173–184 (2021). https://doi.org/10.1007/s40042-021-00180-5

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