# Finding Quantum Many-Body Ground States with Artificial Neural Network

## TL;DR

This paper introduces an unsupervised neural network-based algorithm to find quantum many-body ground states, demonstrating high accuracy on 1D Ising and Heisenberg models without assuming eigenvector forms.

## Contribution

It presents a novel neural network method for unbiased ground state determination in quantum many-body systems, improving flexibility and accuracy.

## Key findings

- Matches exact diagonalization results for 1D models
- Unbiased approach without assuming eigenvector forms
- Controlled accuracy in ground state calculations

## Abstract

Solving ground states of quantum many-body systems has been a long-standing problem in condensed matter physics. Here, we propose a new unsupervised machine learning algorithm to find the ground state of a general quantum many-body system utilizing the benefits of artificial neural network. Without assuming the specific forms of the eigenvectors, this algorithm can find the eigenvectors in an unbiased way with well controlled accuracy. As examples, we apply this algorithm to 1D Ising and Heisenberg models, where the results match very well with exact diagonalization.