Variational neural network ansatz for steady states in open quantum systems

2019/02/28 by Filippo Vicentini, Alberto Biella, Nicolas Regnault +1 · 1 citation
Physics and Astronomy · #quant-ph #cond-mat.dis-nn

paper · pdf · doi:10.1103/physrevlett.122.250503

published as Phys. Rev. Lett. 122, 250503 (2019) · 6 pages, 4 figures, 54 references, 5 pages of Supplemental Informations

arxiv created 2019/05/28 · arxiv updated 2019/07/03

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.

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