TeMFpy: a Python library for converting fermionic mean-field states into tensor networks
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917217913798656 |
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| author | Hille, Simon H. Szabó, Attila |
| author_facet | Hille, Simon H. Szabó, Attila |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_05227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeMFpy: a Python library for converting fermionic mean-field states into tensor networks Hille, Simon H. Szabó, Attila Strongly Correlated Electrons Mesoscale and Nanoscale Physics Superconductivity Computational Physics Quantum Physics 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. |
| title | TeMFpy: a Python library for converting fermionic mean-field states into tensor networks |
| topic | Strongly Correlated Electrons Mesoscale and Nanoscale Physics Superconductivity Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2510.05227 |