Variational neural network ansatz for steady states in open quantum systems
arXiv:1902.10104 · doi:10.1103/PhysRevLett.122.250503
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.
6 pages, 4 figures, 54 references, 5 pages of Supplemental Informations
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Cited by in corpus (11)
- Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
- Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
- Constructing neural stationary states for open quantum many-body systems
- Creating and concentrating quantum resource states in noisy environments using a quantum neural network
- Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems
- Neural networks in quantum many-body physics: a hands-on tutorial
- Nonuniform phases in the geometrically frustrated dissipative XYZ model
- Steady-state phases of dissipative spin-1/2 XYZ model with frustrated interaction
- Investigating Network Parameters in Neural-Network Quantum States
- Steady-state susceptibility in continuous phase transitions of dissipative systems
- A Tensor Network Approach to Finite Markov Decision Processes