Solving the Quantum Many-Body Problem with Artificial Neural Networks

TL;DR

This paper introduces a neural network-based variational method to efficiently approximate quantum many-body wave functions, enabling accurate simulation of complex quantum systems' equilibrium and dynamical properties.

Contribution

The authors develop a neural network variational ansatz combined with reinforcement learning to solve the quantum many-body problem more effectively than traditional methods.

Findings

Achieves high accuracy in modeling equilibrium states.

Successfully describes dynamical evolution of quantum systems.

Applicable to both one- and two-dimensional spin models.

Abstract

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form, for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with variable number of hidden neurons. A reinforcement-learning scheme is then demonstrated, capable of either finding the ground-state or describing the unitary time evolution of complex interacting quantum systems. We show that this approach achieves very high accuracy in the description of equilibrium and dynamical properties of prototypical interacting spins models in both one and two dimensions, thus offering a new powerful tool to solve the quantum many-body problem.