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.
- Received 28 February 2019
DOI:https://doi.org/10.1103/PhysRevLett.122.250503
© 2019 American Physical Society
Physics Subject Headings (PhySH)
Viewpoint
Neural Networks Take on Open Quantum Systems
Published 28 June 2019
Simulating a quantum system that exchanges energy with the outside world is notoriously hard, but the necessary computations might be easier with the help of neural networks.
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