Machine learning approach to trapped many-fermion systems

Bedaque, Paulo F. [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000152109997); Kumar, Hersh [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000206603107); Sheng, Andy [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000261688131)

doi:10.1103/33jq-ks53

DOE OSTI · 3007458

Machine learning approach to trapped many-fermion systems

Bedaque, Paulo F. [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000152109997)·Kumar, Hersh [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000206603107)·Sheng, Andy [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000261688131)

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Abstract

For this work, we apply a variational ansatz based on neural networks to the problem of spin-$^1_2$ fermions in a harmonic trap interacting through a short distance potential. We showed that standard machine learning techniques lead to a quick convergence to the ground state, especially in weakly coupled cases. Higher couplings can be handled efficiently by increasing the strength of interactions during “training”.

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1-dimensional systems approximation methods for many-body systems artificial neural networks fermions few-body systems machine learning quantum many-body systems

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Bedaque, Paulo F. [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000152109997), Kumar, Hersh [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000206603107), Sheng, Andy [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000261688131). 2025-07-07. Machine learning approach to trapped many-fermion systems. https://doi.org/10.1103/33jq-ks53

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