Dresden 2026 – scientific programme

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TT: Fachverband Tiefe Temperaturen

TT 53: Nonequilibrium Quantum Systems II (joint session TT/DY)

TT 53.9: Talk

Wednesday, March 11, 2026, 17:15–17:30, HSZ/0105

Many-body neural network wavefunction for a non-Hermitian Ising chain — •Lavoisier Wah — Max Planck Institute for the Science of Light, 91058 Erlangen, Germany

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 (NN) 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. Then, for larger system sizes, we demonstrate that the RNN outperforms both the RBM and MLP. These results highlight the potential of neural network-based approaches - particularly for accurately capturing the low-energy physics of NH quantum systems both in case of weak and strong non-Hermiticity.

Keywords: Non-Hermitian; Neural quantum state; Many-body wavefunction; Ground state; Low energy