Many-Body Neural Network Wavefunction for a Non-Hermitian Ising Chain

TL;DR

This paper investigates neural network architectures to approximate the ground-state wavefunctions of a non-Hermitian quantum Ising model, demonstrating their effectiveness and scalability compared to traditional methods.

Contribution

It introduces and compares RNN, RBM, and MLP neural networks for modeling non-Hermitian quantum systems, highlighting transfer learning to enhance accuracy for larger systems.

Findings

RNN outperforms RBM and MLP for larger system sizes

Transfer learning improves RBM and MLP accuracy significantly

Neural networks accurately recover ground-state properties in non-Hermitian systems

Abstract

Non-Hermitian (NH) quantum systems have emerged as a powerful framework for describing open quantum systems, non-equilibrium dynamics, and engineered quantum optical materials. However, solving the ground-state properties of NH systems is challenging due to the exponential scaling of the Hilbert space, and exotic phenomena such as the emergence of exceptional points. Another challenge arises from the limitations of traditional methods like exact diagonalization (ED). For the past decade, neural networks (NNs) have shown promise in approximating many-body wavefunctions, yet their application to NH systems remains largely unexplored. In this paper, we explore different NN architectures to investigate the ground-state properties of a parity-time-symmetric, one-dimensional NH, transverse field Ising model with a complex spectrum by employing a recurrent neural network (RNN), a restricted Boltzmann machine~(RBM), and a multilayer perceptron (MLP). We construct the NN-based many-body wavefunctions and validate our approach by recovering the ground-state properties of the model for small system sizes, finding excellent agreement with ED. Furthermore, for larger system sizes, we demonstrate that the RNN outperforms both the RBM and MLP. However, we show that the accuracy of the RBM and MLP can be significantly improved through transfer learning, allowing them to perform comparably to the RNN for larger system sizes. These results highlight the potential of neural network-based approaches--particularly for accurately capturing the low-energy physics of NH quantum systems in case of both weak and strong non-Hermiticity.