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Variational neural network ansatz for steady states in open quantum systems

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 نشر من قبل Filippo Vicentini
 تاريخ النشر 2019
  مجال البحث فيزياء
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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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