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A Machine Learning Approach to Trapped Many-Fermion Systems

2024/10/22 by Bedaque, Paulo F., Kumar, Hersh, Sheng, Andy · 1 citation
#Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Nuclear Theory (nucl-th) #Quantum Physics (quant-ph)

paper · doi:10.48550/arxiv.2410.17383

Abstract

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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