Variational Neural-Network 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 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.
- 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
- E-Print:
- 6 pages, 4 figures, 54 references, 5 pages of Supplemental Informations