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arXiv · 1902.10104

Variational neural network ansatz for steady states in open quantum systems

Filippo Vicentini·Alberto Biella·Nicolas Regnault·Cristiano Ciuti
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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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Filippo Vicentini, Alberto Biella, Nicolas Regnault, Cristiano Ciuti. 2019-02-26. Variational neural network ansatz for steady states in open quantum systems. https://doi.org/10.1103/physrevlett.122.250503

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