Time-dependent variational principle for open quantum systems with artificial neural networks

TL;DR

This paper introduces a neural network-based variational method for simulating the dynamics of open quantum many-body systems, effectively handling dissipation and complex interactions.

Contribution

It presents a novel approach combining deep autoregressive neural networks with a time-dependent variational principle for open quantum systems.

Findings

Successfully simulated dissipative quantum Heisenberg models in 1D and 2D.

Achieved efficient representation of mixed quantum states with neural networks.

Demonstrated applicability to confinement dynamics with dissipation.

Abstract

We develop a variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks. The parameters of a compressed representation of a mixed quantum state are adapted dynamically according to the Lindblad master equation by employing a time-dependent variational principle. We illustrate our approach by solving the dissipative quantum Heisenberg model in one and two dimensions for up to 40 spins and by applying it to the simulation of confinement dynamics in the presence of dissipation.