SciPost Submission Page
Basis dependence of Neural Quantum States for the Transverse Field Ising Model
by Ronald Santiago Cortes Santamaria, Aravindh S. Shankar, Marcello Dalmonte, Roberto Verdel, Nils Niggemann
Submission summary
| Authors (as registered SciPost users): | Ronald Santiago Cortes Santamaria |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2512.11632v2 (pdf) |
| Date submitted: | Jan. 14, 2026, 10:17 a.m. |
| Submitted by: | Ronald Santiago Cortes Santamaria |
| Submitted to: | SciPost Physics Core |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Grammarly and chatGPT based on GPT-4.1 for polishing phrases and avoiding grammar mistakes all along the text.
Abstract
Neural Quantum States (NQS) are powerful tools used to represent complex quantum many-body states in an increasingly wide range of applications. However, despite their popularity, at present only a rudimentary understanding of their limitations exists. In this work, we investigate the dependence of NQS on the choice of the computational basis, focusing on restricted Boltzmann machines. Considering a family of rotated Hamiltonians corresponding to the paradigmatic transverse-field Ising model, we discuss the properties of ground states responsible for the dependence of NQS performance, namely the presence of ground state degeneracies as well as the uniformity of amplitudes and phases, carefully examining their interplay. We identify that the basis-dependence of the performance is linked to the convergence properties of a cluster or cumulant expansion of multi-spin operators -- providing a framework to directly connect physical, basis-dependent properties, to performance itself. Our results provide insights that may be used to gauge the applicability of NQS to new problems and to identify the optimal basis for numerical computations.