Neural Quantum States in Variational Monte Carlo Method: A Brief Summary

TL;DR

This paper reviews the use of neural quantum states within the variational Monte Carlo method for spin systems, highlighting their ability to efficiently model complex, non-local interactions and larger systems.

Contribution

It summarizes recent advances in neural quantum states for variational Monte Carlo, emphasizing their flexibility and efficiency in representing complex quantum wave functions.

Findings

Neural quantum states enable modeling of highly non-local interactions.

They can represent complex wave functions with less computational resources.

Significant progress has been made in quantum-state tomography using neural quantum states.

Abstract

In this note, variational Monte Carlo method based on neural quantum states for spin systems is reviewed. Using a neural network as the wave function allows for a more generalized expression of various types of interactions, including highly non-local interactions, which are closely related to its non-linear activation functions. Additionally, neural networks can represent relatively complex wave functions with relatively small computational resources when dealing with higher-dimensional systems, which is undoubtedly a "flattening" advantage. In quantum-state tomography, the representation method of neural quantum states has already achieved significant results, hinting at its potential in handling larger-sized systems.