Neural-Network Quantum States: A Systematic Review

TL;DR

This paper provides a comprehensive review of Neural-Network Quantum States, highlighting their role as a powerful variational method in quantum many-body physics and their impact within the intersection of AI and quantum science.

Contribution

It offers a systematic review of existing literature on Neural-Network Quantum States, summarizing recent advances and their significance in quantum many-body problem solving.

Findings

Neural-Network Quantum States are competitive with traditional methods.

Recent literature shows growing interest and development in this field.

The approach has significant potential for future quantum physics research.

Abstract

The so-called contemporary AI revolution has reached every corner of the social, human and natural sciences -- physics included. In the context of quantum many-body physics, its intersection with machine learning has configured a high-impact interdisciplinary field of study; with the arise of recent seminal contributions that have derived in a large number of publications. One particular research line of such field of study is the so-called Neural-Network Quantum States, a powerful variational computational methodology for the solution of quantum many-body systems that has proven to compete with well-established, traditional formalisms. Here, a systematic review of literature regarding Neural-Network Quantum States is presented.