The Tensor-Network Kinetic Solver - Classical, Deployable Today
Abstract
The kinetic distribution function is highly compressible in a low-rank tensor-network representation, and that yields a practical classical solver, not a storage trick. On a 1D1V BGK test, a matrix-product-state truncation reaches relative-L² error 2×10−⁴ at rank 8 using ~0.19× the dense storage, with the error falling exponentially in rank. Unlike the fault-tolerant-horizon quantum route, this runs today.
Fields of Study & Subjects
Quantum Computing Algorithms and Architecture
Tensor decomposition and applications
Quantum many-body systems
Publication details
- Journal / source
- OSF Preprints (OSF Preprints)
- Publication date
- 28 August 2026
- Language
- EN
- Document type
- Preprint