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Main Authors: Sarmiento, J. Rozalén, Rios, A.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2411.11605
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author Sarmiento, J. Rozalén
Rios, A.
author_facet Sarmiento, J. Rozalén
Rios, A.
contents We present an overview of the method of Neural Quantum States applied to the many-body problem of atomic nuclei. Through the lens of group representation theory, we focus on the problem of constructing neural-network ansätze that respect physical symmetries. We explicitly prove that determinants, which are among the most common methods to build antisymmetric neural-network wave functions, can be understood as the result of a group convolution. We also identify the reason why this construction is so efficient in practice compared to other group convolutional operations. We conclude that group representation theory is a promising avenue to incorporate explicitly symmetries in Neural Quantum States.
format Preprint
id arxiv_https___arxiv_org_abs_2411_11605
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Fermionic Neural Networks through the lens of Group Theory
Sarmiento, J. Rozalén
Rios, A.
Nuclear Theory
Quantum Physics
We present an overview of the method of Neural Quantum States applied to the many-body problem of atomic nuclei. Through the lens of group representation theory, we focus on the problem of constructing neural-network ansätze that respect physical symmetries. We explicitly prove that determinants, which are among the most common methods to build antisymmetric neural-network wave functions, can be understood as the result of a group convolution. We also identify the reason why this construction is so efficient in practice compared to other group convolutional operations. We conclude that group representation theory is a promising avenue to incorporate explicitly symmetries in Neural Quantum States.
title Fermionic Neural Networks through the lens of Group Theory
topic Nuclear Theory
Quantum Physics
url https://arxiv.org/abs/2411.11605