diff --git a/dev/404.html b/dev/404.html index 63f79c27..39b5d732 100644 --- a/dev/404.html +++ b/dev/404.html @@ -9,7 +9,7 @@ - + @@ -19,7 +19,7 @@
- + \ No newline at end of file diff --git a/dev/api/ansatz.html b/dev/api/ansatz.html index 7cbd862f..8f9f6c42 100644 --- a/dev/api/ansatz.html +++ b/dev/api/ansatz.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Tenet.jl

Appearance

Ansatz ​

MPS ​

Tenet.MPS Type
julia
MPS <: AbstractAnsatz

A Matrix Product State Ansatz Tensor Network.

source

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/api/quantum.html b/dev/api/quantum.html index 37b033b4..8c9098b4 100644 --- a/dev/api/quantum.html +++ b/dev/api/quantum.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Tenet.jl

Appearance

Quantum ​

Tenet.Quantum Type
julia
Quantum

Tensor Network with a notion of "causality". This leads to the notion of sites and directionality (input/output).

Notes

  • Indices are referenced by Sites.

source

Tenet.TensorNetwork Method
julia
TensorNetwork(q::AbstractQuantum)

Returns the underlying TensorNetwork of an AbstractQuantum.

source

Base.adjoint Method
julia
adjoint(q::Quantum)

Returns the adjoint of a Quantum Tensor Network; i.e. the conjugate Tensor Network with the inputs and outputs swapped.

source

Tenet.sites Function
julia
sites(q::AbstractQuantum)

Returns the sites of a AbstractQuantum Tensor Network.

source

Tenet.nsites Function
julia
nsites(q::AbstractQuantum)

Returns the number of sites of a AbstractQuantum Tensor Network.

source

Missing docstring.

Missing docstring for Tenet.inds(::Quantum; kwargs...). Check Documenter's build log for details.

Missing docstring.

Missing docstring for Tenet.tensors(::Quantum; kwargs...). Check Documenter's build log for details.

Missing docstring.

Missing docstring for inputs. Check Documenter's build log for details.

Missing docstring.

Missing docstring for outputs. Check Documenter's build log for details.

Missing docstring.

Missing docstring for lanes. Check Documenter's build log for details.

Missing docstring.

Missing docstring for ninputs. Check Documenter's build log for details.

Missing docstring.

Missing docstring for noutputs. Check Documenter's build log for details.

Missing docstring.

Missing docstring for nlanes. Check Documenter's build log for details.

Missing docstring.

Missing docstring for Socket. Check Documenter's build log for details.

Tenet.socket Method
julia
socket(q::Quantum)

Returns the socket of a Quantum Tensor Network; i.e. whether it is a Scalar, State or Operator.

source

Tenet.Scalar Type
julia
Scalar <: Socket

Socket representing a scalar; i.e. a Tensor Network with no open sites.

source

Tenet.State Type
julia
State <: Socket

Socket representing a state; i.e. a Tensor Network with only input sites (or only output sites if dual = true).

source

Tenet.Operator Type
julia
Operator <: Socket

Socket representing an operator; i.e. a Tensor Network with both input and output sites.

source

Missing docstring.

Missing docstring for Base.merge(::Quantum, ::Quantum...). Check Documenter's build log for details.

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/api/tensor.html b/dev/api/tensor.html index ab924ec5..5b55c2f3 100644 --- a/dev/api/tensor.html +++ b/dev/api/tensor.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Tenet.jl

Appearance

Tensor ​

Missing docstring.

Missing docstring for Base.parent(::Tensor). Check Documenter's build log for details.

Missing docstring.

Missing docstring for inds(::Tensor). Check Documenter's build log for details.

Base.size Method
julia
Base.size(::Tensor[, i])

Return the size of the underlying array or the dimension i (specified by Symbol or Integer).

source

Graphs.LinAlg.contract Method
julia
contract(a::Tensor[, b::Tensor]; dims=nonunique([inds(a)..., inds(b)...]))

Perform tensor contraction operation.

source

LinearAlgebra.svd Method
julia
LinearAlgebra.svd(tensor::Tensor; left_inds, right_inds, virtualind, kwargs...)

Perform SVD factorization on a tensor.

Keyword arguments

  • left_inds: left indices to be used in the SVD factorization. Defaults to all indices of t except right_inds.

  • right_inds: right indices to be used in the SVD factorization. Defaults to all indices of t except left_inds.

  • virtualind: name of the virtual bond. Defaults to a random Symbol.

source

LinearAlgebra.qr Method
julia
LinearAlgebra.qr(tensor::Tensor; left_inds, right_inds, virtualind, kwargs...)

Perform QR factorization on a tensor.

Keyword arguments

  • left_inds: left indices to be used in the QR factorization. Defaults to all indices of t except right_inds.

  • right_inds: right indices to be used in the QR factorization. Defaults to all indices of t except left_inds.

  • virtualind: name of the virtual bond. Defaults to a random Symbol.

source

LinearAlgebra.lu Method
julia
LinearAlgebra.lu(tensor::Tensor; left_inds, right_inds, virtualind, kwargs...)

Perform LU factorization on a tensor.

Keyword arguments

  • left_inds: left indices to be used in the LU factorization. Defaults to all indices of t except right_inds.

  • right_inds: right indices to be used in the LU factorization. Defaults to all indices of t except left_inds.

  • virtualind: name of the virtual bond. Defaults to a random Symbol.

source

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/api/tensornetwork.html b/dev/api/tensornetwork.html index 2085780e..db4d2445 100644 --- a/dev/api/tensornetwork.html +++ b/dev/api/tensornetwork.html @@ -9,9 +9,9 @@ - + - + @@ -28,7 +28,7 @@ replace(tn::AbstractTensorNetwork, old => new...)

Replace the element in old with the one in new. Depending on the types of old and new, the following behaviour is expected:

source

Base.selectdim Function
julia
selectdim(tn::AbstractTensorNetwork, index::Symbol, i)

Return a copy of the AbstractTensorNetwork where index has been projected to dimension i.

See also: view, slice!.

source

Tenet.slice! Function
julia
slice!(tn::AbstractTensorNetwork, index::Symbol, i)

In-place projection of index on dimension i.

See also: selectdim, view.

source

Base.view Method
julia
view(tn::AbstractTensorNetwork, index => i...)

Return a copy of the AbstractTensorNetwork where each index has been projected to dimension i. It is equivalent to a recursive call of selectdim.

See also: selectdim, slice!.

source

Base.copy Method
julia
copy(tn::TensorNetwork)

Return a shallow copy of a TensorNetwork.

source

Missing docstring.

Missing docstring for Base.rand(::Type{TensorNetwork}, n::Integer, regularity::Integer). Check Documenter's build log for details.

Transformations ​

Tenet.transform Function
julia
transform(tn::TensorNetwork, config::Transformation)
 transform(tn::TensorNetwork, configs)

Return a new TensorNetwork where some Transformation has been performed into it.

See also: transform!.

source

Tenet.transform! Function
julia
transform!(tn::TensorNetwork, config::Transformation)
 transform!(tn::TensorNetwork, configs)

In-place version of transform.

source

Tenet.HyperFlatten Type
julia
HyperFlatten <: Transformation

Convert hyperindices to COPY-tensors, represented by DeltaArrays. This transformation is always used by default when visualizing a TensorNetwork with plot.

See also: HyperGroup.

source

Tenet.HyperGroup Type
julia
HyperGroup <: Transformation

Convert COPY-tensors, represented by DeltaArrays, to hyperindices.

See also: HyperFlatten.

source

Tenet.ContractSimplification Type
julia
ContractSimplification <: Transformation

Preemptively contract tensors whose result doesn't increase in size.

source

Tenet.DiagonalReduction Type
julia
DiagonalReduction <: Transformation

Reduce the dimension of a Tensor in a TensorNetwork when it has a pair of indices that fulfil a diagonal structure.

Keyword Arguments

source

Tenet.AntiDiagonalGauging Type
julia
AntiDiagonalGauging <: Transformation

Reverse the order of tensor indices that fulfill the anti-diagonal condition. While this transformation doesn't directly enhance computational efficiency, it sets up the TensorNetwork for other operations that do.

Keyword Arguments

source

Tenet.Truncate Type
julia
Truncate <: Transformation

Truncate the dimension of a Tensor in a TensorNetwork when it contains columns with all elements smaller than atol.

Keyword Arguments

source

Missing docstring.

Missing docstring for Tenet.SplitSimplificationd. Check Documenter's build log for details.

- + \ No newline at end of file diff --git a/dev/assets/app.CU2jmcSI.js b/dev/assets/app.DdcgUpy-.js similarity index 95% rename from dev/assets/app.CU2jmcSI.js rename to dev/assets/app.DdcgUpy-.js index ae4068d2..70fa08a1 100644 --- a/dev/assets/app.CU2jmcSI.js +++ b/dev/assets/app.DdcgUpy-.js @@ -1 +1 @@ -import{R as p}from"./chunks/theme.CxGtD-cc.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a4 as S}from"./chunks/framework.Cn53cJ0c.js";function i(e){if(e.extends){const a=i(e.extends);return{...a,...e,async enhanceApp(t){a.enhanceApp&&await a.enhanceApp(t),e.enhanceApp&&await e.enhanceApp(t)}}}return e}const s=i(p),T=P({name:"VitePressApp",setup(){const{site:e,lang:a,dir:t}=R();return w(()=>{y(()=>{document.documentElement.lang=a.value,document.documentElement.dir=t.value})}),e.value.router.prefetchLinks&&C(),b(),E(),s.setup&&s.setup(),()=>S(s.Layout)}});async function D(){globalThis.__VITEPRESS__=!0;const e=j(),a=_();a.provide(c,e);const t=l(e.route);return a.provide(f,t),a.component("Content",d),a.component("ClientOnly",m),Object.defineProperties(a.config.globalProperties,{$frontmatter:{get(){return t.frontmatter.value}},$params:{get(){return t.page.value.params}}}),s.enhanceApp&&await s.enhanceApp({app:a,router:e,siteData:h}),{app:a,router:e,data:t}}function _(){return g(T)}function j(){let e=o,a;return A(t=>{let n=v(t),r=null;return n&&(e&&(a=n),(e||a===n)&&(n=n.replace(/\.js$/,".lean.js")),r=import(n)),o&&(e=!1),r},s.NotFound)}o&&D().then(({app:e,router:a,data:t})=>{a.go().then(()=>{u(a.route,t.site),e.mount("#app")})});export{D as createApp}; +import{R as p}from"./chunks/theme.BC13wDs5.js";import{R as o,a6 as u,a7 as c,a8 as l,a9 as f,aa as d,ab as m,ac as h,ad as g,ae as A,af as v,d as P,u as R,v as w,s as y,ag as C,ah as b,ai as E,a4 as S}from"./chunks/framework.Cn53cJ0c.js";function i(e){if(e.extends){const 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The Tensor type ​

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.

You can create a Tensor by passing an array and a list of Symbols that name indices.

julia
julia> Tᵢⱼₖ = Tensor(rand(3,5,2), (:i,:j,:k))
 3×5×2 Tensor{Float64, 3, Array{Float64, 3}}:
 [:, :, 1] =
- 0.426856  0.867249  0.0830756  0.655334   0.121413
- 0.677611  0.351992  0.701102   0.0664307  0.596111
- 0.260722  0.552243  0.0945193  0.272519   0.358345
+ 0.605838  0.00103334  0.00905273  0.416001  0.120496
+ 0.790712  0.978051    0.0476998   0.439927  0.559657
+ 0.761911  0.492562    0.469827    0.969238  0.118661
 
 [:, :, 2] =
- 0.583467   0.825058  0.397573  0.71843    0.800232
- 0.327648   0.634156  0.766745  0.0923975  0.778579
- 0.0513208  0.3293    0.492367  0.261051   0.552892

The dimensionality or size of each index can be consulted using the size function.

julia
julia> size(Tᵢⱼₖ)
+ 0.423997   0.86812   0.740979  0.456698  0.871045
+ 0.739981   0.64263   0.251913  0.100772  0.739933
+ 0.0850391  0.424834  0.306362  0.66196   0.270062

The dimensionality or size of each index can be consulted using the size function.

julia
julia> size(Tᵢⱼₖ)
 (3, 5, 2)
 
 julia> size(Tᵢⱼₖ, :j)
diff --git a/dev/assets/manual_tensors.md.DyyslztA.js b/dev/assets/manual_tensors.md.C454M1C9.lean.js
similarity index 95%
rename from dev/assets/manual_tensors.md.DyyslztA.js
rename to dev/assets/manual_tensors.md.C454M1C9.lean.js
index f18f19bb..58a83b4d 100644
--- a/dev/assets/manual_tensors.md.DyyslztA.js
+++ b/dev/assets/manual_tensors.md.C454M1C9.lean.js
@@ -1,14 +1,14 @@
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101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z",style:{"stroke-width":"3"}})])])],-1)]))),a[3]||(a[3]=t("mjx-assistive-mml",{unselectable:"on",display:"inline",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",width:"auto",overflow:"hidden"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[t("mi",null,"n")])],-1))]),a[16]||(a[16]=s(" is a multilinear")),a[17]||(a[17]=t("sup",{class:"footnote-ref"},[t("a",{href:"#fn3",id:"fnref3"},"[3]")],-1)),a[18]||(a[18]=s(" application between 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In computer science, you would intuitively think of tensors as "'),t("em",null,"n-dimensional arrays with named dimensions"),s('".')],-1)),t("mjx-container",x,[(T(),e("svg",w,a[25]||(a[25]=[Q('',1)]))),a[26]||(a[26]=t("mjx-assistive-mml",{unselectable:"on",display:"block",style:{top:"0px",left:"0px",clip:"rect(1px, 1px, 1px, 1px)","-webkit-touch-callout":"none","-webkit-user-select":"none","-khtml-user-select":"none","-moz-user-select":"none","-ms-user-select":"none","user-select":"none",position:"absolute",padding:"1px 0px 0px 0px",border:"0px",display:"block",overflow:"hidden",width:"100%"}},[t("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[t("msub",null,[t("mi",null,"T"),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",null,"i"),t("mi",null,"j"),t("mi",null,"k")])]),t("mstyle",{scriptlevel:"0"},[t("mspace",{width:"0.278em"})]),t("mo",{stretchy:"false"},"⟺"),t("mstyle",{scriptlevel:"0"},[t("mspace",{width:"0.278em"})]),t("mrow",{"data-mjx-texclass":"ORD"},[t("mi",{mathvariant:"monospace"},"T"),t("mo",{mathvariant:"monospace",stretchy:"false"},"["),t("mi",{mathvariant:"monospace"},"i"),t("mo",{mathvariant:"monospace"},","),t("mi",{mathvariant:"monospace"},"j"),t("mo",{mathvariant:"monospace"},","),t("mi",{mathvariant:"monospace"},"k"),t("mo",{mathvariant:"monospace",stretchy:"false"},"]")])])],-1))]),a[32]||(a[32]=Q(`
The Tensor type ​
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.
You can create a Tensor by passing an array and a list of Symbols that name indices.
julia
julia> Tᵢⱼₖ = Tensor(rand(3,5,2), (:i,:j,:k))
 3×5×2 Tensor{Float64, 3, Array{Float64, 3}}:
 [:, :, 1] =
- 0.426856  0.867249  0.0830756  0.655334   0.121413
- 0.677611  0.351992  0.701102   0.0664307  0.596111
- 0.260722  0.552243  0.0945193  0.272519   0.358345
+ 0.605838  0.00103334  0.00905273  0.416001  0.120496
+ 0.790712  0.978051    0.0476998   0.439927  0.559657
+ 0.761911  0.492562    0.469827    0.969238  0.118661
 
 [:, :, 2] =
- 0.583467   0.825058  0.397573  0.71843    0.800232
- 0.327648   0.634156  0.766745  0.0923975  0.778579
- 0.0513208  0.3293    0.492367  0.261051   0.552892
The dimensionality or size of each index can be consulted using the size function.
julia
julia> size(Tᵢⱼₖ)
+ 0.423997   0.86812   0.740979  0.456698  0.871045
+ 0.739981   0.64263   0.251913  0.100772  0.739933
+ 0.0850391  0.424834  0.306362  0.66196   0.270062
The dimensionality or size of each index can be consulted using the size function.
julia
julia> size(Tᵢⱼₖ)
 (3, 5, 2)
 
 julia> size(Tᵢⱼₖ, :j)
diff --git a/dev/assets/manual_transformations.md.CfnoFf-r.js b/dev/assets/manual_transformations.md.CfnoFf-r.js
deleted file mode 100644
index d9a7fbdc..00000000
--- a/dev/assets/manual_transformations.md.CfnoFf-r.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as t,c as n,a5 as o,o as e}from"./chunks/framework.Cn53cJ0c.js";const i="/Tenet.jl/dev/assets/bdrzgoc.D7V6hupC.png",r="/Tenet.jl/dev/assets/jlsiued.DwpoxpAe.png",s="/Tenet.jl/dev/assets/bvndqnh.CiEHhneS.png",l="/Tenet.jl/dev/assets/axctrwo.FFeR2tQ7.png",g=JSON.parse('{"title":"Transformations","description":"","frontmatter":{},"headers":[],"relativePath":"manual/transformations.md","filePath":"manual/transformations.md","lastUpdated":null}'),c={name:"manual/transformations.md"};function d(p,a,m,h,u,f){return e(),n("div",null,a[0]||(a[0]=[o('
Transformations ​
In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.
A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.
Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.
In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.
Available transformations ​
Hyperindex converter ​
Contraction simplification ​
Diagonal reduction ​
Anti-diagonal reduction ​
Dimension truncation ​
Split simplification ​
',16)]))}const v=t(c,[["render",d]]);export{g as __pageData,v as default};
diff --git a/dev/assets/manual_transformations.md.CfnoFf-r.lean.js b/dev/assets/manual_transformations.md.CfnoFf-r.lean.js
deleted file mode 100644
index d9a7fbdc..00000000
--- a/dev/assets/manual_transformations.md.CfnoFf-r.lean.js
+++ /dev/null
@@ -1 +0,0 @@
-import{_ as t,c as n,a5 as o,o as e}from"./chunks/framework.Cn53cJ0c.js";const i="/Tenet.jl/dev/assets/bdrzgoc.D7V6hupC.png",r="/Tenet.jl/dev/assets/jlsiued.DwpoxpAe.png",s="/Tenet.jl/dev/assets/bvndqnh.CiEHhneS.png",l="/Tenet.jl/dev/assets/axctrwo.FFeR2tQ7.png",g=JSON.parse('{"title":"Transformations","description":"","frontmatter":{},"headers":[],"relativePath":"manual/transformations.md","filePath":"manual/transformations.md","lastUpdated":null}'),c={name:"manual/transformations.md"};function d(p,a,m,h,u,f){return e(),n("div",null,a[0]||(a[0]=[o('
Transformations ​
In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.
A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.
Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.
In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.
Available transformations ​
Hyperindex converter ​
Contraction simplification ​
Diagonal reduction ​
Anti-diagonal reduction ​
Dimension truncation ​
Split simplification ​
',16)]))}const v=t(c,[["render",d]]);export{g as __pageData,v as default};
diff --git a/dev/assets/manual_transformations.md.YQ2sdkfI.js b/dev/assets/manual_transformations.md.YQ2sdkfI.js
new file mode 100644
index 00000000..a1c005c0
--- /dev/null
+++ b/dev/assets/manual_transformations.md.YQ2sdkfI.js
@@ -0,0 +1 @@
+import{_ as t,c as n,a5 as i,o}from"./chunks/framework.Cn53cJ0c.js";const e="/Tenet.jl/dev/assets/bdrzgoc.D7V6hupC.png",r="/Tenet.jl/dev/assets/jlsiued.C0NF364N.png",s="/Tenet.jl/dev/assets/bvndqnh.CiEHhneS.png",l="/Tenet.jl/dev/assets/axctrwo.FFeR2tQ7.png",g=JSON.parse('{"title":"Transformations","description":"","frontmatter":{},"headers":[],"relativePath":"manual/transformations.md","filePath":"manual/transformations.md","lastUpdated":null}'),c={name:"manual/transformations.md"};function d(p,a,m,h,u,f){return o(),n("div",null,a[0]||(a[0]=[i('
Transformations ​
In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.
A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.
Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.
In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.
Available transformations ​
Hyperindex converter ​
Contraction simplification ​
Diagonal reduction ​
Anti-diagonal reduction ​
Dimension truncation ​
Split simplification ​
',16)]))}const v=t(c,[["render",d]]);export{g as __pageData,v as default};
diff --git a/dev/assets/manual_transformations.md.YQ2sdkfI.lean.js b/dev/assets/manual_transformations.md.YQ2sdkfI.lean.js
new file mode 100644
index 00000000..a1c005c0
--- /dev/null
+++ b/dev/assets/manual_transformations.md.YQ2sdkfI.lean.js
@@ -0,0 +1 @@
+import{_ as t,c as n,a5 as i,o}from"./chunks/framework.Cn53cJ0c.js";const e="/Tenet.jl/dev/assets/bdrzgoc.D7V6hupC.png",r="/Tenet.jl/dev/assets/jlsiued.C0NF364N.png",s="/Tenet.jl/dev/assets/bvndqnh.CiEHhneS.png",l="/Tenet.jl/dev/assets/axctrwo.FFeR2tQ7.png",g=JSON.parse('{"title":"Transformations","description":"","frontmatter":{},"headers":[],"relativePath":"manual/transformations.md","filePath":"manual/transformations.md","lastUpdated":null}'),c={name:"manual/transformations.md"};function d(p,a,m,h,u,f){return o(),n("div",null,a[0]||(a[0]=[i('
Transformations ​
In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.
A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.
Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.
In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.
Available transformations ​
Hyperindex converter ​
Contraction simplification ​
Diagonal reduction ​
Anti-diagonal reduction ​
Dimension truncation ​
Split simplification ​
',16)]))}const v=t(c,[["render",d]]);export{g as __pageData,v as default};
diff --git a/dev/developer/cached-field.html b/dev/developer/cached-field.html
index bd136884..0f14e442 100644
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+++ b/dev/developer/cached-field.html
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     style id4 stroke-dasharray: 5 5
     style id5 stroke-dasharray: 5 5
 """

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/developer/unsafe-region.html b/dev/developer/unsafe-region.html index 1e095bc4..0ee7bc3e 100644 --- a/dev/developer/unsafe-region.html +++ b/dev/developer/unsafe-region.html @@ -9,9 +9,9 @@ - + - + @@ -24,7 +24,7 @@
Tenet.jl

Appearance

Unsafe regions ​

There are cases in which you may want to temporarily avoid index size checks on push! to a TensorNetwork.

julia
@unsafe_region tn begin
     ...
 end

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/friends.html b/dev/friends.html index c7922c4e..b109284f 100644 --- a/dev/friends.html +++ b/dev/friends.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Tenet.jl

Appearance

Friends ​

If Tenet's design doesn't fit your case, ¡no problem!. There are other nice libraries in the wild, of which we recommend to take a look at:

  • quimb Flexible Tensor Network written in Python. Main source of inspiration for Tenet.

  • tenpy Tensor Network library written in Python with a strong focus on physics.

  • ITensors.jl and ITensorNetworks.jl Mature Tensor Network framework written in Julia.

  • tensorkrowch A new Tensor Network library built on top of PyTorch.

  • SeeMPS

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- + \ No newline at end of file diff --git a/dev/hashmap.json b/dev/hashmap.json index 689a9f69..97a1e353 100644 --- a/dev/hashmap.json +++ b/dev/hashmap.json @@ -1 +1 @@ -{"api_ansatz.md":"C1g7kmuE","api_quantum.md":"COq6uWu-","api_tensor.md":"Bhnq7moi","api_tensornetwork.md":"5HxedP9V","developer_cached-field.md":"BrlRUzKt","developer_hypergraph.md":"D_3HUkg8","developer_keyword-dispatch.md":"Be6ehSeY","developer_type-hierarchy.md":"D3oqrRH6","developer_unsafe-region.md":"B_N9vQj7","friends.md":"C-YdilcK","index.md":"DNbjO1Hk","manual_ansatz_index.md":"CIY56V1B","manual_ansatz_mps.md":"lkScuORK","manual_ansatz_product.md":"CVfzFVPy","manual_contraction.md":"HBV1ExSu","manual_quantum.md":"B8v8wme6","manual_tensor-network.md":"Dnl_PoAd","manual_tensors.md":"DyyslztA","manual_transformations.md":"CfnoFf-r","visualization.md":"BWHmRTNQ"} +{"api_ansatz.md":"C1g7kmuE","api_quantum.md":"COq6uWu-","api_tensor.md":"Bhnq7moi","api_tensornetwork.md":"5HxedP9V","developer_cached-field.md":"BrlRUzKt","developer_hypergraph.md":"D_3HUkg8","developer_keyword-dispatch.md":"Be6ehSeY","developer_type-hierarchy.md":"D3oqrRH6","developer_unsafe-region.md":"B_N9vQj7","friends.md":"C-YdilcK","index.md":"DNbjO1Hk","manual_ansatz_index.md":"CIY56V1B","manual_ansatz_mps.md":"lkScuORK","manual_ansatz_product.md":"CVfzFVPy","manual_contraction.md":"HBV1ExSu","manual_quantum.md":"B8v8wme6","manual_tensor-network.md":"Dnl_PoAd","manual_tensors.md":"C454M1C9","manual_transformations.md":"YQ2sdkfI","visualization.md":"BWHmRTNQ"} diff --git a/dev/index.html b/dev/index.html index da1f269f..df18f1b8 100644 --- a/dev/index.html +++ b/dev/index.html @@ -9,9 +9,9 @@ - + - + @@ -23,7 +23,7 @@
Tenet.jl

Appearance

Tenet.jl

Hackable Tensor Networks

Manual
API Reference 📚
View on GitHub
Tenet.jl
🚀

Fast & Device Agnostic

Its deep integration with Reactant.jl and carefully developed code, makes it go brrrr

∂

Built-In MLIR AD

Leverage Enzyme-Powered Automatic Differentiation to Differentiate MLIR Functions

🧩

Carefully crafted

🫂

Compatible with friends

BSC-Quantic's Registry

Tenet 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,

julia
using Pkg
 pkg"registry add https://github.com/bsc-quantic/Registry"

Features ​

  • Optimized Tensor Network contraction, powered by EinExprs

  • Tensor Network slicing/cuttings

  • Automatic Differentiation of TN contraction, powered by EinExprs and ChainRules

  • 3D visualization of large networks, powered by Makie

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- + \ No newline at end of file diff --git a/dev/manual/ansatz/index.html b/dev/manual/ansatz/index.html index 265ce118..ffe16b6b 100644 --- a/dev/manual/ansatz/index.html +++ b/dev/manual/ansatz/index.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@ - + \ No newline at end of file diff --git a/dev/manual/ansatz/mps.html b/dev/manual/ansatz/mps.html index 09e5d8a2..a7f838a0 100644 --- a/dev/manual/ansatz/mps.html +++ b/dev/manual/ansatz/mps.html @@ -9,9 +9,9 @@ - + - + @@ -44,7 +44,7 @@ Label(fig[1,2, Bottom()], "Periodic") # hide fig # hide

In Tenet, the generic MatrixProduct ansatz implements this topology. Type variables are used to address their functionality (State or Operator) and their boundary conditions (Open or Periodic).

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- + \ No newline at end of file diff --git a/dev/manual/ansatz/product.html b/dev/manual/ansatz/product.html index b8802d1b..6a73ea07 100644 --- a/dev/manual/ansatz/product.html +++ b/dev/manual/ansatz/product.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@ - + \ No newline at end of file diff --git a/dev/manual/contraction.html b/dev/manual/contraction.html index b7c8bf3c..97a21fec 100644 --- a/dev/manual/contraction.html +++ b/dev/manual/contraction.html @@ -9,9 +9,9 @@ - + - + @@ -22,7 +22,7 @@
Tenet.jl

Appearance

Contraction ​

Contraction path optimization and execution is delegated to the EinExprs library. A EinExpr is a lower-level form of a Tensor Network, in which the contraction path has been laid out as a tree. It is similar to a symbolic expression (i.e. Expr) but in which every node represents an Einstein summation expression (aka einsum).

EinExprs.einexpr Method
julia
einexpr(tn::AbstractTensorNetwork; optimizer = EinExprs.Greedy, output = inds(tn, :open), kwargs...)

Search a contraction path for the given AbstractTensorNetwork and return it as a EinExpr.

Keyword Arguments

  • optimizer Contraction path optimizer. Check EinExprs documentation for more info.

  • outputs Indices that won't be contracted. Defaults to open indices.

  • kwargs Options to be passed to the optimizer.

See also: contract.

source

Missing docstring.

Missing docstring for contract(::Tenet.TensorNetwork). Check Documenter's build log for details.

Tenet.contract! Function
julia
contract!(tn::TensorNetwork, index)

In-place contraction of tensors connected to index.

See also: contract.

source

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Tenet.jl

Appearance

Tensor Networks ​

Tensor Networks (TN) are a graphical notation for representing complex multi-linear functions. For example, the following equation

∑ijklmnopAimBijpCnjkDpklEmnoFol

can be represented visually as

The graph's nodes represent tensors and edges represent tensor indices.

In Tenet, these objects are represented by the TensorNetwork type.

Information about a TensorNetwork can be queried with the following functions.

Query information ​

Modification ​

Add/Remove tensors ​

Replace existing elements ​

Slicing ​

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- + \ No newline at end of file diff --git a/dev/manual/tensors.html b/dev/manual/tensors.html index 728c3642..c7307f6a 100644 --- a/dev/manual/tensors.html +++ b/dev/manual/tensors.html @@ -9,11 +9,11 @@ - + - + - + @@ -24,14 +24,14 @@
Tenet.jl

Appearance

Tensors ​

If you have reached here, you probably know wha a tensor is. Nevertheless, we are gonna give a brief remainder.

There are many jokes[1] about how to define a tensor. The definition we are giving here might not be the most correct one, but it is good enough for our use case (don't kill me please, mathematicians). A tensor T of order[2] n is a multilinear[3] application between n vector spaces over a field F.

T:Fdim⁡(1)×⋯×Fdim⁡(n)↦F

In layman's terms, it is a linear function whose inputs are vectors and the output is a scalar number.

T(v(1),…,v(n))=c∈F∀i,v(i)∈Fdim⁡(i)

Tensor algebra is a higher-order generalization of linear algebra, where scalar numbers can be viewed as order-0 tensors, vectors as order-1 tensors, matrices as order-2 tensors, ...

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".

Tijk⟺T[i,j,k]

The Tensor type ​

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.

You can create a Tensor by passing an array and a list of Symbols that name indices.

julia
julia> Tᵢⱼₖ = Tensor(rand(3,5,2), (:i,:j,:k))
 3×5×2 Tensor{Float64, 3, Array{Float64, 3}}:
 [:, :, 1] =
- 0.426856  0.867249  0.0830756  0.655334   0.121413
- 0.677611  0.351992  0.701102   0.0664307  0.596111
- 0.260722  0.552243  0.0945193  0.272519   0.358345
+ 0.605838  0.00103334  0.00905273  0.416001  0.120496
+ 0.790712  0.978051    0.0476998   0.439927  0.559657
+ 0.761911  0.492562    0.469827    0.969238  0.118661
 
 [:, :, 2] =
- 0.583467   0.825058  0.397573  0.71843    0.800232
- 0.327648   0.634156  0.766745  0.0923975  0.778579
- 0.0513208  0.3293    0.492367  0.261051   0.552892

The dimensionality or size of each index can be consulted using the size function.

julia
julia> size(Tᵢⱼₖ)
+ 0.423997   0.86812   0.740979  0.456698  0.871045
+ 0.739981   0.64263   0.251913  0.100772  0.739933
+ 0.0850391  0.424834  0.306362  0.66196   0.270062

The dimensionality or size of each index can be consulted using the size function.

julia
julia> size(Tᵢⱼₖ)
 (3, 5, 2)
 
 julia> size(Tᵢⱼₖ, :j)
@@ -39,7 +39,7 @@
 
 julia> length(Tᵢⱼₖ)
 30

  1. For example, recursive definitions like a tensor is whatever that transforms as a tensor. ↩︎

  2. The order of a tensor may also be known as rank or dimensionality in other fields. However, these can be missleading, since it has nothing to do with the rank of linear algebra nor with the dimensionality of a vector space. We prefer to use word order. ↩︎

  3. Meaning that the relationships between the output and the inputs, and the inputs between them, are linear. ↩︎

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Transformations ​

In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.

A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.

Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.

In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.

Available transformations ​

Hyperindex converter ​

Contraction simplification ​

Diagonal reduction ​

Anti-diagonal reduction ​

Dimension truncation ​

Split simplification ​

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

- +
Tenet.jl

Appearance

Transformations ​

In tensor network computations, it is good practice to apply various transformations to simplify the network structure, reduce computational cost, or prepare the network for further operations. These transformations modify the network's structure locally by permuting, contracting, factoring or truncating tensors.

A crucial reason why these methods are indispensable lies in their ability to drastically reduce the problem size of the contraction path search and also the contraction. This doesn't necessarily involve reducing the maximum rank of the Tensor Network itself, but more importantly, it reduces the size (or rank) of the involved tensors.

Our approach is based in (Gray and Kourtis, 2021), which can also be found in quimb.

In Tenet, we provide a set of predefined transformations which you can apply to your TensorNetwork using both the transform/transform! functions.

Available transformations ​

Hyperindex converter ​

Contraction simplification ​

Diagonal reduction ​

Anti-diagonal reduction ​

Dimension truncation ​

Split simplification ​

Made with DocumenterVitepress.jl

© Copyright 2024 Barcelona Supercomputing Center - Centro Nacional de Supercomputación

+ \ No newline at end of file diff --git a/dev/visualization.html b/dev/visualization.html index e37bbde4..2d9b91ed 100644 --- a/dev/visualization.html +++ b/dev/visualization.html @@ -9,9 +9,9 @@ - + - + @@ -24,7 +24,7 @@
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Visualization ​

Tenet provides a Package Extension for Makie support. You can just import a Makie backend and call GraphMakie.graphplot on a TensorNetwork.

GraphMakie.graphplot Method
julia
graphplot(tn::TensorNetwork; kwargs...)
 graphplot!(f::Union{Figure,GridPosition}, tn::TensorNetwork; kwargs...)
 graphplot!(ax::Union{Axis,Axis3}, tn::TensorNetwork; kwargs...)

Plot a TensorNetwork as a graph.

Keyword Arguments

  • labels If true, show the labels of the tensor indices. Defaults to false.

  • The rest of kwargs are passed to GraphMakie.graphplot.

source

julia
graphplot(tn, layout=Stress(), labels=true)

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