Random Sampling Neural Network for Quantum Many-Body Problems

TL;DR

This paper introduces RSNN, a neural network-based method that efficiently approximates quantum many-body eigenvalues and properties across different models, scaling linearly with system size and working near phase transitions.

Contribution

The paper presents a novel neural network approach, RSNN, that generalizes to various quantum models and accurately predicts properties with linear computational scaling.

Findings

High accuracy in energy spectra and magnetization near phase transitions

Linear scaling of computation time with system size

Effective in strongly correlated regimes and near quantum critical points

Abstract

The eigenvalue problem of quantum many-body systems is a fundamental and challenging subject in condensed matter physics, since the dimension of the Hilbert space (and hence the required computational memory and time) grows exponentially as the system size increases. A few numerical methods have been developed for some specific systems, but may not be applicable in others. Here we propose a general numerical method, Random Sampling Neural Networks (RSNN), to utilize the pattern recognition technique for the random sampling matrix elements of an interacting many-body system via a self-supervised learning approach. Several exactly solvable 1D models, including Ising model with transverse field, Fermi-Hubbard model, and spin-$1/2$ $XXZ$ model, are used to test the applicability of RSNN. Pretty high accuracy of energy spectrum, magnetization and critical exponents etc. can be obtained within the strongly correlated regime or near the quantum phase transition point, even the corresponding RSNN models are trained in the weakly interacting regime. The required computation time scales linearly to the system size. Our results demonstrate that it is possible to combine the existing numerical methods for the training process and RSNN to explore quantum many-body problems in a much wider parameter regime, even for strongly correlated systems.