Variational neural network ansatz for steady states in open quantum systems
Quantum Physics
2019-07-03 v2 Disordered Systems and Neural Networks
Abstract
We present a general variational approach to determine the steady state of open quantum lattice systems via a neural network approach. The steady-state density matrix of the lattice system is constructed via a purified neural network ansatz in an extended Hilbert space with ancillary degrees of freedom. The variational minimization of cost functions associated to the master equation can be performed using a Markov chain Monte Carlo sampling. As a first application and proof-of-principle, we apply the method to the dissipative quantum transverse Ising model.
Cite
@article{arxiv.1902.10104,
title = {Variational neural network ansatz for steady states in open quantum systems},
author = {Filippo Vicentini and Alberto Biella and Nicolas Regnault and Cristiano Ciuti},
journal= {arXiv preprint arXiv:1902.10104},
year = {2019}
}
Comments
6 pages, 4 figures, 54 references, 5 pages of Supplemental Informations