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TeMFpy is a specialized Python library that bridges mean-field theory and tensor network methods, specifically targeting fermionic systems like quantum spin liquids. By enabling the conversion of Slater determinants and Pfaffian states into matrix product states, it facilitates more accurate simulations of strongly correlated electron systems using established MPS algorithms. This tool represents a significant methodological advance for researchers in condensed matter physics leveraging quantum simulation techniques.

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TeMFpy: a Python library for converting fermionic mean-field states into tensor networks

arXiv31 august 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2510.05227v3 Announce Type: replace-cross 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.

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.

Strongly Correlated Electrons (cond-mat.str-el); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Superconductivity (cond-mat.supr-con); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)

arXiv:2510.05227 [cond-mat.str-el]

(or arXiv:2510.05227v3 [cond-mat.str-el] for this version)

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