Method to Solve Quantum Few-Body Problems with Artificial Neural Networks

A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave...

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Bibliographic Details
Published in:Journal of the Physical Society of Japan 2018-07, Vol.87 (7), p.74002
Main Author: Saito, Hiroki
Format: Article
Language:English
Subjects:
Artificial neural networks
Bosons
Machine learning
Neural networks
Particle physics
Quantum physics
Three dimensional imaging
Three dimensional models
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Summary:A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave function is output from the network. The method is applied to the Calogero–Sutherland model in one-dimensional space and Efimov bound states in three-dimensional space.
ISSN:0031-9015
1347-4073
DOI:10.7566/JPSJ.87.074002