Variational Neural-Network Ansatz for Steady States in Open Quantum Systems

Year: 2019

Authors: Vicentini F., Biella A., Regnault N., Ciuti C.

Autors Affiliation: Univ Paris, Lab Mat & Phenomenes Quant, CNRS, F-75013 Paris, France; Univ Paris, Lab Phys Ecole Normale Super, Univ PSL, Sorbonne Paris Cite,Sorbonne Univ,CNRS,ENS, F-75005 Paris, France.

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.

Journal/Review: PHYSICAL REVIEW LETTERS

Volume: 122 (25)      Pages from: 250503-1  to: 250503-6

More Information: We thank G. Carleo, V. Savona, and G. Orso for fruitful discussions. Full space simulations have been made with QuantumOptics. jl [59] and with QuTiP [60,61]. We acknowledge support from ERC (via Consolidator Grant CORPHO No. 616233). This work was granted access to the HPC resources of TGCC under the allocation 2018-A0050510601 attributed by GENCI (Grand Equipement National de Calcul Intensif).
KeyWords: Boltzmann Machines; Python Framework; Dynamics; Driven; Qutip; Julia
DOI: 10.1103/PhysRevLett.122.250503

ImpactFactor: 8.385
Citations: 146
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