https://doi.org/10.1140/epjp/s13360-025-06855-3
Regular Article
Calculation of entanglement entropy of transverse-field ising model from neural network quantum state based on a restricted Boltzmann machine
Physics Department, Xiamen University Malaysia, 43900, Sepang, Selangor, Malaysia
a
feifang.chung@xmu.edu.my
b
kokwee.song@xmu.edu.my
c
ckong@xmu.edu.my
Received:
24
May
2025
Accepted:
12
September
2025
Published online:
3
October
2025
We study transverse-field Ising model by representing its neural network quantum state wavefunction based on a restricted Boltzmann machine (RBM). The input data for training the RBM is generated using the Metropolis–Hastings algorithm. The RBM parameters are optimized by minimizing the expectation value of the Hamiltonian to generate the quantum ground state. We subsequently use this ground state to compute bipartite entanglement entropy (EE). The results show that EE is proportional to the size of the spin chain at the critical point, and exhibits an area law scaling in the presence of a large external field.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
