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Vicentini, Filippo; Biella, Alberto; Regnault, Nicolas; Ciuti, Cristiano
Physical review letters, 06/2019, Volume: 122, Issue: 25Journal Article
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.
