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Coarse-graining in quantum mechanics: Distinguishable and indistinguishable particles
Patrick G Sahrmann1, Gregory A Voth1
1Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, USA.
The Journal of Chemical Physics
|July 22, 2025
Summary
This study introduces novel coarse-grained (CG) modeling for quantum systems, enabling simulations of indistinguishable particles. These advances accelerate the simulation of thermal quantum systems across various temperatures.
Area of Science:
- Computational physics
- Quantum mechanics
- Statistical mechanics
Background:
- Coarse-grained (CG) modeling enhances molecular simulation scales.
- Current CG methods primarily address classical systems, leaving quantum systems largely unexplored.
Purpose of the Study:
- To develop fundamental advances in particle-based, bottom-up CG theory for quantum statistical mechanics.
- To extend CG modeling to include indistinguishable quantum particles (bosons and fermions).
- To introduce a variational optimization procedure for CG model parameterization.
Main Methods:
- Expansion of the bottom-up CG formalism to incorporate indistinguishable quantum particles.
- Introduction of a variational optimization procedure based on the relative entropy minimization (REM) principle.
- Bridging classical and quantum REM methods via a semiclassical expansion.
Main Results:
- Demonstration of CG models for both distinguishable and indistinguishable quantum systems.
- Numerical examples include a harmonically trapped bosonic system and liquid water.
- The developed methods are applicable to a range of quantum systems.
Conclusions:
- The presented theoretical results enable accelerated simulations of thermal quantum systems.
- This approach facilitates the study of distinguishable particles at higher temperatures and indistinguishable particles at lower temperatures.
- Advances in CG theory open new avenues for quantum system simulations.
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