# Approximating power of machine-learning ansatz for quantum many-body states

## TL;DR

This paper analyzes the effectiveness of restricted Boltzmann machine neural networks as variational ansatzes for quantum many-body states, revealing their connection to perturbation theory and their performance limitations related to entanglement.

## Contribution

It uncovers the relation between RBM structure and perturbation series, improves algorithms for better convergence, and compares RBM with other variational wave-functions.

## Key findings

- RBM ansatz achieves high precision in simple models due to perturbation series connection.
- Performance of RBM depends on entanglement properties of target states.
- Introducing alternative wave-functions yields comparable results to RBM.

## Abstract

An artificial neural network (ANN) with the restricted Boltzmann machine (RBM) architecture was recently proposed as a versatile variational quantum many-body wave function. In this work we provide physical insights into the performance of this ansatz. We uncover the connection between the structure of RBM and perturbation series, which explains the excellent precision achieved by RBM ansazt in certain simple models, demonstrated in the literature. Based on this relation, we improve the numerical algorithm to achieve better performance of RBM in cases where local minima complicate the convergence to the global one. We introduce other classes of variational wave-functions, which are also capable of reproducing the perturbative structure, and show that their performance is comparable to that of RBM. Furthermore, we study the performance of a few-layer RBM for approximating ground states of random, translationally-invariant models in 1d, as well as random matrix-product states (MPS). We find that the error in approximating such states exhibits a broad distribution, and is largely determined by the entanglement properties of the targeted state.