Method to solve quantum few-body problems with artificial neural networks

TL;DR

This paper introduces a machine learning approach using neural networks to compute ground states of quantum few-body systems, demonstrating its effectiveness on models like Calogero-Sutherland and Efimov states.

Contribution

The paper presents a novel neural network-based method for solving quantum few-body problems, capable of handling continuous space and complex interactions.

Findings

Successfully applied to Calogero-Sutherland model

Effectively computed Efimov bound states

Demonstrated potential for quantum many-body problems

Abstract

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.