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

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.

Publication
ArXiv Preprint
Moritz Reh
Moritz Reh
PhD Student
Martin Gärttner
Martin Gärttner
Group leader

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