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#Tenet.contract-Tuple{Tensor, Tensor}","page":"Tensors","title":"Tenet.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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Defaults to a random Symbol.\n\n\n\n\n\n","category":"method"},{"location":"tensors.html#LinearAlgebra.lu-Tuple{Tensor}","page":"Tensors","title":"LinearAlgebra.lu","text":"LinearAlgebra.lu(tensor::Tensor; left_inds, right_inds, virtualind, kwargs...)\n\nPerform LU factorization on a tensor.\n\nKeyword arguments\n\nleft_inds: left indices to be used in the LU factorization. Defaults to all indices of t except right_inds.\nright_inds: right indices to be used in the LU factorization. Defaults to all indices of t except left_inds.\nvirtualind: name of the virtual bond. Defaults to a random Symbol.\n\n\n\n\n\n","category":"method"},{"location":"index.html#Tenet.jl","page":"Home","title":"Tenet.jl","text":"","category":"section"},{"location":"index.html","page":"Home","title":"Home","text":"info: BSC-Quantic's Registry\nTenet and some of its dependencies are located in our own Julia registry. 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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