Liouville space neural network representation of density matrices
Kothe, Simon and Kirton, Peter (2023) Liouville space neural network representation of density matrices. Physical Review A. ISSN 1050-2947 (In Press)
Text.
Filename: Kothe-Kirton-PRA-2024-Liouville-space-neural-network-representation-of-density-matrices.pdf
Accepted Author Manuscript Restricted to Repository staff only until 1 January 2099. License: Strathprints license 1.0 Download (1MB) | Request a copy |
Abstract
Neural network quantum states as ansatz wave functions have shown a lot of promise for finding the ground state of spin models. Recently, work has been focused on extending this idea to mixed states for simulating the dynamics of open systems. Most approaches so far have used a purification ansatz where a copy of the system Hilbert space is added which when traced out gives the correct density matrix. Here, we instead present an extension of the Restricted Boltzmann Machine which directly represents the density matrix in Liouville space. This allows the compact representation of states which appear in mean-field theory. We benchmark our approach on two different version of the dissipative transverse field Ising model which show our ansatz is able to compete with other state-of-the-art approaches.
-
-
Item type: Article ID code: 88848 Dates: DateEvent15 April 2023Published15 April 2023AcceptedSubjects: Science > Physics Department: Faculty of Science > Physics Depositing user: Pure Administrator Date deposited: 22 Apr 2024 08:45 Last modified: 26 Apr 2024 08:59 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/88848