Year: 2023
Type: conference-paper
Abstract: Tensor networks are a representation tool that allow simulation of large quantum systems. However, they only achieve an advantage for systems whose properties fit the correlation structure of the network, which must have some connectivity constraints to remain computationally feasible. Here we propose new structures that can adapt to the correlations of specific systems while keeping these constraints in mind. We find that for generic systems with no structure we can train structures that already achieve a small improvement. We also find ways to check that the dimension of the internal bonds allows a fair comparison.
Authors: Masot Llima, Sergi, Garcia, Artur
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7113339207