Invariant Neural Network Ansatz for weakly symmetric Open Quantum Lattices

Abstract

We consider $d$-dimensional open quantum lattices whose time evolution is governed by a master equation which is weakly symmetric under the action of a finite group $G$ that is a subgroup of all the possible permutations of the lattice sites. We show that, whenever the steady state is unique, one can introduce a neural network representation for the system density operator that explicitly accounts for the system symmetries and can be efficiently optimized by exploring only a relevant subspace of the parameter space. In particular, as a proof of principle, we demonstrate the validity of our approach by determining the steady state structure of the one dimensional dissipative XYZ model in the presence of a uniform magnetic field.

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Cited by in corpus (3)

• Algebraic Theory of Quantum Synchronization and Limit Cycles under Dissipation
• Deep Neural Networks as Variational Solutions for Correlated Open Quantum Systems
• Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems