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TensorKit.jl offers a specialized Julia solution for large-scale tensor computations, uniquely addressing internal symmetries ranging from abelian to anyonic types via its TensorMap architecture. The package prioritizes extensibility and performance, making it a relevant tool for researchers in mathematical software, strongly correlated electrons, and quantum physics who require flexible handling of complex tensor structures. Its design philosophy bridges computational efficiency with theoretical depth, providing a practical framework for extending to novel tensor types and symmetries.
TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory
arXiv:2508.10076v2 Announce Type: replace-cross Abstract: TensorKit$.$jl is a Julia-based software package for tensor computations, especially focusing on tensors with internal symmetries. This paper introduces the design philosophy, core functionalities, and distinctive features, including how to handle abelian, non-abelian, and anyonic symmetries through the ``TensorMap'' type. We highlight the software's flexibility, performance, and its capability to extend to new tensor types and symmetries, illustrating its practical applications through select case studies.
Title:TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory
Abstract:TensorKit$.$jl is a Julia-based software package for tensor computations, especially focusing on tensors with internal symmetries. This paper introduces the design philosophy, core functionalities, and distinctive features, including how to handle abelian, non-abelian, and anyonic symmetries through the ``TensorMap'' type. We highlight the software's flexibility, performance, and its capability to extend to new tensor types and symmetries, illustrating its practical applications through select case studies.
Mathematical Software (cs.MS); Strongly Correlated Electrons (cond-mat.str-el); Quantum Physics (quant-ph)
arXiv:2508.10076 [cs.MS]
(or arXiv:2508.10076v2 [cs.MS] for this version)
https://doi.org/10.48550/arXiv.2508.10076 Focus to learn more arXiv-issued DOI via DataCite
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