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The Tensor-Network Kinetic Solver - Classical, Deployable Today
Authors: Ford, P. I.
Summary
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.
Canonical records
- Zenodo (canonical DOI): https://doi.org/10.5281/zenodo.22132192
- Figshare DOI: https://doi.org/10.6084/m9.figshare.33361440
- Publisher: Kronos Fusion Energy — 2026 Physics De-Risking series
What's in this repository
*_Editorial_2026.pdf— the editorial edition of the paper.*_reproducibility_bundle.zip— the reproducibility bundle: toolkit source, the per-gate runs the paper cites, and a student pack (concept notes, tutorial, glossary,reproduce.ipynb,requirements.txt).
Reproduce
unzip *_reproducibility_bundle.zip -d bundle && cd bundle
pip install -r requirements.txt
jupyter notebook reproduce.ipynb
Scope
Physics and engineering only; no economics. Every quantity traces to a documented gate in the KRONOS de-risking register. Honest gates are stated in the paper.
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