Constructing neural stationary states for open quantum many-body systems

2019/06/28 by Nobuyuki Yoshioka, Ryusuke Hamazaki · 4 citations
Physics and Astronomy · Computer Science · #Quantum many-body systems #Spectroscopy and Quantum Chemical Studies #Neural Networks and Reservoir Computing

paper · doi:10.1103/physrevb.99.214306

openalex publication_date 2019/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A theoretical exploration of exotic properties in open quantum many-body systems requires an efficient search of the nonequillibrum stationary states. Aiming to accelerate the process, the authors develop here a variational method, in which the ansatz for the mixed states is based on the restricted Boltzmann machine, motivated by its high expressive power shown in various recent works. It is demonstrated that the ansatz successfully simulates dissipative spin systems both in one and two dimensions.

Citations

• Natural Gradient Works Efficiently in Learning
• Fidelity for Mixed Quantum States
• An open-system quantum simulator with trapped ions

Cited by

• Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
• Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
• Fermionic neural-network states for ab-initio electronic structure
• Neural network (machine learning) [wikipedia]