Multi-body wave function of ground and low-lying excited states using unornamented deep neural networks

TL;DR

This paper introduces a deep neural network-based method for calculating ground and low-lying excited state wave functions and energies, incorporating symmetrization techniques for identical particles, advancing quantum many-body problem solutions.

Contribution

The paper presents a novel neural network approach capable of computing multiple quantum states and includes a simple symmetrization method for identical particles.

Findings

Successfully computes ground and excited states using neural networks

Implements straightforward symmetrization for bosons and fermions

Demonstrates effectiveness on quantum systems with identical particles

Abstract

We propose a method to calculate wave functions and energies not only of the ground state but also of low-lying excited states using a deep neural network and the unsupervised machine learning technique. For systems composed of identical particles, a simple method to perform symmetrization for bosonic systems and antisymmetrization for fermionic systems is also proposed.