Tensor network (TN) methods are well established for computing partition functions in statistical mechanics, though this use has traditionally been limited to lattice models. We extend the scope of TN methodology to interacting particle systems in continuous space. Through a real-space discretization combined with a cell-based coarse-graining scheme, we formulate an effective lattice model that explicitly preserves spatial locality. The partition function of this model is represented as a TN, and the thermodynamic quantities are computed via boundary contraction. We apply this framework to the two-dimensional hard-disk problem and demonstrate the strengths of the TN formulation compared to existing Monte Carlo simulations.
@article{arxiv.2604.25060,
title = {Statistical mechanics in continuous space with tensor network methods},
author = {Gunhee Park and Tomislav Begušić and Si-Jing Du and Johnnie Gray and Garnet Kin-Lic Chan},
journal= {arXiv preprint arXiv:2604.25060},
year = {2026}
}