Alternatives
Tenet is strongly opinionated. We acknowledge that it may not suit all cases (although we try 🙂). If your case doesn't fit Tenet's design, you can try the following libraries:
- quimb (Gray, 2018) Flexible Tensor Network written in Python. Main source of inspiration for
Tenet. - tenpy (Hauschild et al., 2021) Tensor Network library written in Python with a strong focus on physics.
- ITensors.jl (Fishman et al., 2022) Mature Tensor Network framework written in Julia.
- tensorkrowch (Ramón Pareja Monturiol et al., 2023) A new Tensor Network library built on top of PyTorch.
![](\"assets/tensor.excalidraw.svg\")
","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"Letters are used to identify each of the vector spaces the tensor relates to. In computer science, you would intuitively think of tensors as \"n-dimensional arrays with named dimensions\".","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"T_ijk iff mathttTijk","category":"page"},{"location":"tensors.html#The-Tensor-type","page":"Tensors","title":"The Tensor type","text":"","category":"section"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"In Tenet, a tensor is represented by the Tensor type, which wraps an array and a list of symbols. As it subtypes AbstractArray, many array operations can be dispatched to it.","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"You can create a Tensor by passing an array and a list of Symbols that name indices.","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"Tᵢⱼₖ = Tensor(rand(3,5,2), (:i,:j,:k))","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"The dimensionality or size of each index can be consulted using the size function.","category":"page"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"Base.size(::Tensor)","category":"page"},{"location":"tensors.html#Base.size-Tuple{Tensor}","page":"Tensors","title":"Base.size","text":"Base.size(::Tensor[, i])\n\nReturn the size of the underlying array or the dimension i (specified by Symbol or Integer).\n\n\n\n\n\n","category":"method"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"size(Tᵢⱼₖ)\nsize(Tᵢⱼₖ, :j)\nlength(Tᵢⱼₖ)","category":"page"},{"location":"tensors.html#Operations","page":"Tensors","title":"Operations","text":"","category":"section"},{"location":"tensors.html#Contraction","page":"Tensors","title":"Contraction","text":"","category":"section"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"Tenet.contract(::Tensor, ::Tensor)","category":"page"},{"location":"tensors.html#Graphs.LinAlg.contract-Tuple{Tensor, Tensor}","page":"Tensors","title":"Graphs.LinAlg.contract","text":"contract(a::Tensor[, b::Tensor]; dims=nonunique([inds(a)..., inds(b)...]))\n\nPerform tensor contraction operation.\n\n\n\n\n\n","category":"method"},{"location":"tensors.html#Factorizations","page":"Tensors","title":"Factorizations","text":"","category":"section"},{"location":"tensors.html","page":"Tensors","title":"Tensors","text":"LinearAlgebra.svd(::Tensor)\nLinearAlgebra.qr(::Tensor)\nLinearAlgebra.lu(::Tensor)","category":"page"},{"location":"tensors.html#LinearAlgebra.svd-Tuple{Tensor}","page":"Tensors","title":"LinearAlgebra.svd","text":"LinearAlgebra.svd(tensor::Tensor; left_inds, right_inds, virtualind, kwargs...)\n\nPerform SVD factorization on a tensor.\n\nKeyword arguments\n\nleft_inds: left indices to be used in the SVD factorization. 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In order to download Tenet, add our registry to your Julia installation by using the Pkg mode in a REPL session,using Pkg\npkg\"registry add https://github.com/bsc-quantic/Registry\"","category":"page"},{"location":"index.html","page":"Home","title":"Home","text":"A Julia library for Tensor Networks. Tenet can be executed both at local environments and on large supercomputers. Its goals are,","category":"page"},{"location":"index.html","page":"Home","title":"Home","text":"Expressiveness Simple to use 👶\nFlexibility Extend it to your needs 🔧\nPerformance Goes brr... fast 🏎️","category":"page"},{"location":"index.html","page":"Home","title":"Home","text":"A video of its presentation at JuliaCon 2023 can be seen here:","category":"page"},{"location":"index.html","page":"Home","title":"Home","text":"![\"Sketch](\"assets/tn-sketch.svg\")
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