Variational classical networks for dynamics in interacting quantum matter
Roberto Verdel, Markus Schmitt, Yi-Ping Huang, Petr Karpov, and Markus, Heyl

TL;DR
This paper introduces a variational neural network-based method to efficiently simulate the dynamics of interacting quantum many-body systems in higher dimensions, including complex models like lattice gauge theories.
Contribution
It presents a novel variational class of wavefunctions using classical spin networks, enabling controlled and scalable simulations of quantum dynamics in multiple dimensions.
Findings
Successfully applied to quantum quenches in 1D and 2D models
Demonstrated the method's effectiveness on a 2D lattice gauge theory
Provided insights into disorder-free localization dynamics
Abstract
Dynamics in correlated quantum matter is a hard problem, as its exact solution generally involves a computational effort that grows exponentially with the number of constituents. While a remarkable progress has been witnessed in recent years for one-dimensional systems, much less has been achieved for interacting quantum models in higher dimensions, since they incorporate an additional layer of complexity. In this work, we employ a variational method that allows for an efficient and controlled computation of the dynamics of quantum many-body systems in one and higher dimensions. The approach presented here introduces a variational class of wavefunctions based on complex networks of classical spins akin to artificial neural networks, which can be constructed in a controlled fashion. We provide a detailed prescription for such constructions and illustrate their performance by studying…
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