Variational NeuralNetwork Ansatz for Steady States in Open Quantum Systems
Abstract
We present a general variational approach to determine the steady state of open quantum lattice systems via a neuralnetwork approach. The steadystate density matrix of the lattice system is constructed via a purified neuralnetwork 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.
 Publication:

Physical Review Letters
 Pub Date:
 June 2019
 DOI:
 10.1103/PhysRevLett.122.250503
 arXiv:
 arXiv:1902.10104
 Bibcode:
 2019PhRvL.122y0503V
 Keywords:

 Quantum Physics;
 Condensed Matter  Disordered Systems and Neural Networks
 EPrint:
 6 pages, 4 figures, 54 references, 5 pages of Supplemental Informations