SciPost Submission Page
TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
by Simon H. Hille, Attila Szabó
Submission summary
| Authors (as registered SciPost users): | Simon Hans Hille |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2510.05227v2 (pdf) |
| Code repository: | https://github.com/temfpy/temfpy |
| Code version: | v0.2.0 |
| Code license: | MIT |
| Date submitted: | Jan. 23, 2026, 5 p.m. |
| Submitted by: | Simon Hans Hille |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
- Assistance while coding: ChatGPT 3.5-5.2 Pro / mini, Gemini 2.0-3.0 Pro / Flash / Flash-Lite, and the GitHub copilot add-on in VS-code
- Assistance while writing: ChatGPT 3.5-5.2, Gemini 2.0-3.0 Pro
They where used throughout the project timeline (mid 2024-beginning 2026) but every single line of code and sentence generated was checked / altered by the authors.
Use-cases:
- Code suggestions
- Autocomplete for coding
- Analyzing bugs / errors and explaining code of existing libraries
- Checking if efficiency of code could be improved
- Assistance in formulating sentences by suggesting sentences or changes to sentences
- Spelling and grammar check
Abstract
We introduce TeMFpy, a Python library for converting fermionic mean-field states to finite or infinite matrix product state (MPS) form. TeMFpy includes new, efficient, and easy-to-understand algorithms for both Slater determinants and Pfaffian states. Together with Gutzwiller projection, these also allow the user to build variational wave functions for various strongly correlated electron systems, such as quantum spin liquids. We present all implemented algorithms in detail and describe how they can be accessed through TeMFpy, including full example workflows. TeMFpy is built on top of TeNPy and, therefore, integrates seamlessly with existing MPS-based algorithms.