Solving the Liouvillian Gap with Artificial Neural Networks

TL;DR

This paper introduces a neural network-based variational method to accurately compute the Liouvillian gap in open quantum systems, enabling analysis of relaxation times and phase transitions across different dimensions.

Contribution

It develops a novel variational approach using restricted Boltzmann machines and the spin bi-base mapping to efficiently determine the Liouvillian gap in open quantum systems.

Findings

Successfully applied to the dissipative Heisenberg model in 1D and 2D.

Achieved accurate results comparable to analytical solutions.

Demonstrated efficiency regardless of system dimensionality and entanglement complexity.

Abstract

We propose a machine-learning inspired variational method to obtain the Liouvillian gap, which plays a crucial role in characterizing the relaxation time and dissipative phase transitions of open quantum systems. By using the "spin bi-base mapping", we map the density matrix to a pure restricted-Boltzmann-machine (RBM) state and transform the Liouvillian superoperator to a rank-two non-Hermitian operator. The Liouvillian gap can be obtained by a variational real-time evolution algorithm under this non-Hermitian operator. We apply our method to the dissipative Heisenberg model in both one and two dimensions. For the isotropic case, we find that the Liouvillian gap can be analytically obtained and in one dimension even the whole Liouvillian spectrum can be exactly solved using the Bethe ansatz method. By comparing our numerical results with their analytical counterparts, we show that the Liouvillian gap could be accessed by the RBM approach efficiently to a desirable accuracy, regardless of the dimensionality and entanglement properties.