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Trapped Ion Quantum Computing
Quantum Machine Learning
A Machine Learning Approach to Trapped Many-Fermion Systems
arXiv
Authors: Paulo F. Bedaque, Hersh Kumar, Andy Sheng
Year
2024
Paper ID
37783
Status
Preprint
Abstract Read
~2 min
Abstract Words
60
Citations
N/A
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".
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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- 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.
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