From b773e1f1976f0e7999cae2313994d8178f2874a8 Mon Sep 17 00:00:00 2001 From: Angus Dunnett Date: Tue, 12 Jan 2021 10:32:47 +0100 Subject: [PATCH] added docs/build/ to gitignore --- .gitignore | 1 + docs/build/index.html | 4 ++-- docs/build/search/index.html | 2 +- docs/build/search_index.js | 2 +- docs/make.jl | 7 +++++-- 5 files changed, 10 insertions(+), 6 deletions(-) diff --git a/.gitignore b/.gitignore index b24d71e..3540ac8 100644 --- a/.gitignore +++ b/.gitignore @@ -48,3 +48,4 @@ Thumbs.db *.mov *.wmv +docs/build/ \ No newline at end of file diff --git a/docs/build/index.html b/docs/build/index.html index 03af418..4098870 100644 --- a/docs/build/index.html +++ b/docs/build/index.html @@ -1,5 +1,5 @@ -MPSDynamics Documentation · MPSDynamics Documentation
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MPSDynamics Documentation

MPSDynamics.chaincoeffs_ohmic — Method
chaincoeffs_ohmic(nummodes, α, s, beta="inf"; wc=1, soft=false)

Generate chain coefficients for an Harmonic bath coupled to a spin-1/2 with spectral density given by:

soft cutoff: $J(ω) = 2παω_c (\frac{ω}{ω_c})^s \exp(-ω/ω_c)$

hard cutoff: $J(ω) = 2παω_c (\frac{ω}{ω_c})^s θ(ω-ω_c)$

The Hamiltonian is given by:

$H = \frac{ω_0}{2}σ_z + Δσ_x + σ_x\sum_kg_k(b_k^\dagger+b_k) + \sum_kω_kb_k^\dagger b_k$

And the spectral density is defined by:

$J(ω) ≡ π\sum_k|g_k|^2δ(ω-ω_k)$

source
MPSDynamics.chainmps — Method
chainmps(N::Int, sites::Vector{Int}, numex::Int)

Generate an MPS with numex excitations of an equal super-position over sites

source
MPSDynamics.chainmps — Method
chainmps(N::Int, site::Int, numex::Int)

Generate an MPS with numex excitations on site

The returned MPS will have bond-dimensions and physical dimensions numex+1

source
MPSDynamics.chainprop — Method
chainprop(t, cparams...)

Propagate an excitation placed initially on the first site of a tight-binding chain with parameters given by cparams for a time t and return occupation expectation for each site.

source
MPSDynamics.dynamap — Method
dynamap(ps1,ps2,ps3,ps4)

Calulate complete dynamical map to time step at which ps1, ps2, ps3 and ps4 are specified.

source
MPSDynamics.electron2kmps — Function
electronkmps(N::Int, k::Vector{Int}, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])

Generate an MPS with 2 electrons in k-states k1 and k2.

source
MPSDynamics.electronkmps — Function
electronkmps(N::Int, k::Int, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])

Generate an MPS for an electron with momentum k.

source
MPSDynamics.elementmps — Method
elementmps(A, el...)

Return the element of the MPS A for the set of physical states el...

Examples

julia> A = chainmps(6, [2,4], 1);
+MPSDynamics Documentation · MPSDynamics.jl
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MPSDynamics Documentation
MPSDynamics.chaincoeffs_ohmic — Method
chaincoeffs_ohmic(nummodes, α, s, beta="inf"; wc=1, soft=false)
Generate chain coefficients for an Harmonic bath coupled to a spin-1/2 with spectral density given by: 
soft cutoff: $J(ω) = 2παω_c (\frac{ω}{ω_c})^s \exp(-ω/ω_c)$ 
hard cutoff: $J(ω) = 2παω_c (\frac{ω}{ω_c})^s θ(ω-ω_c)$
The Hamiltonian is given by:
$H = \frac{ω_0}{2}σ_z + Δσ_x + σ_x\sum_kg_k(b_k^\dagger+b_k) + \sum_kω_kb_k^\dagger b_k$
And the spectral density is defined by:
$J(ω) ≡ π\sum_k|g_k|^2δ(ω-ω_k)$
source
MPSDynamics.chainmps — Method
chainmps(N::Int, sites::Vector{Int}, numex::Int)
Generate an MPS with numex excitations of an equal super-position over sites
source
MPSDynamics.chainmps — Method
chainmps(N::Int, site::Int, numex::Int)
Generate an MPS with numex excitations on site
The returned MPS will have bond-dimensions and physical dimensions numex+1
source
MPSDynamics.chainprop — Method
chainprop(t, cparams...)
Propagate an excitation placed initially on the first site of a tight-binding chain with parameters given by cparams for a time t and return occupation expectation for each site.
source
MPSDynamics.dynamap — Method
dynamap(ps1,ps2,ps3,ps4)
Calulate complete dynamical map to time step at which ps1, ps2, ps3 and ps4 are specified.
source
MPSDynamics.electron2kmps — Function
electronkmps(N::Int, k::Vector{Int}, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])
Generate an MPS with 2 electrons in k-states k1 and k2.
source
MPSDynamics.electronkmps — Function
electronkmps(N::Int, k::Int, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])
Generate an MPS for an electron with momentum k.
source
MPSDynamics.elementmps — Method
elementmps(A, el...)
Return the element of the MPS A for the set of physical states el...
Examples
julia> A = chainmps(6, [2,4], 1);
 
 julia> elementmps(A, 1, 2, 1, 1, 1, 1)
 0.7071067811865475
@@ -11,4 +11,4 @@
 0.0
 
 julia> elementmps(A, 1, 1, 1, 1, 1, 1)
-0.0
source
MPSDynamics.entanglemententropy — Method
entanglemententropy(A)
For a list of tensors A representing a right orthonormalized MPS, compute the entanglement entropy for a bipartite cut for every bond.
source
MPSDynamics.findchainlength — Method
findchainlength(T, cparams...; eps=10^-6)
Estimate length of chain required for a particular set of chain parameters by calulating how long an excitation on the first site takes to reach the end. The chain length is given as the length required for the excitation to have just reached the last site after time T.
source
MPSDynamics.findchild — Method
findchild(node::TreeNode, id::Int)
Return integer corresponding to the which number child site id is of node.
source
MPSDynamics.measure1siteoperator — Method
measure1siteoperator(A, O)
For a list of tensors A representing a right orthonormalized MPS, compute the local expectation value of a one-site operator O for every site or just one if i is specified.
For calculating operators on single sites this will be more efficient if the site is on the left of the mps.
source
MPSDynamics.measure2siteoperator — Method
 measure2siteoperator(A::Vector, M1, M2, j1, j2)
Caculate expectation of M1*M2 where M1 acts on site j1 and M2 acts on site j2, assumes A is right normalised.
source
MPSDynamics.modemps — Function
modemps(N::Int, k::Vector{Int}, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])
Generate an MPS with numex excitations of an equal superposition of modes k of a bosonic tight-binding chain.
source
MPSDynamics.modemps — Function
modemps(N::Int, k::Int, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])
Generate an MPS with numex excitations of mode k of a bosonic tight-binding chain. 
chainparams takes the form [e::Vector, t::Vector] where e are the on-site energies and t are the hoppping parameters.
The returned MPS will have bond-dimensions and physical dimensions numex+1
source
MPSDynamics.mpsmoveoc! — Method
mpsmoveoc!(A::TreeNetwork, id::Int)
Move the orthogonality centre of right normalised tree-MPS A to site id.
This function will be more efficient than using mpsmixednorm! if the tree-MPS is already right-normalised.
source
MPSDynamics.mpsshiftoc! — Method
mpsshiftoc!(A::TreeNetwork, newhd::Int)
Shift the orthogonality centre by one site, setting new head-node newhd.
source
MPSDynamics.normmps — Method
normmps(A::Vector; mpsorthog=:None)
Calculate norm of MPS A.
Setting mpsorthog=:Right/:Left will calculate the norm assuming right/left canonical form. Setting mpsorthog=OC::Int will cause the norm to be calculated assuming the orthoganility center is on site OC. If mpsorthog is :None the norm will be calculated as an MPS-MPS product.
source
MPSDynamics.normmps — Method
normmps(net::TreeNetwork; mpsorthog=:None)
When applied to a tree-MPS mpsorthog=:Left is not defined.
source
MPSDynamics.orthcentersmps — Method
orthcentersmps(A)
Compute the orthoganality centres of MPS A.
Return value is a list in which each element is the corresponding site tensor of A with the orthoganility centre on that site. Assumes A is right normalised.
source
MPSDynamics.productstatemps — Function
productstatemps(N::Int, d::Int, Dmax=1; state=:Vacuum, mpsorthog=:Right)
Return an N-site MPS with all local Hilbert space dimensions given by d. 
source
MPSDynamics.productstatemps — Function
productstatemps(physdims::Dims, Dmax=1; state=:Vacuum, mpsorthog=:Right)
Return an MPS representing a product state with local Hilbert space dimensions given by physdims.
By default all bond-dimensions will be 1 since the state is a product state. However, to embed the product state in a manifold of greater bond-dimension, Dmax can be set accordingly.
The indvidual states of the MPS sites can be provdided by setting state to a list of column vectors. Setting state=:Vacuum will produce an MPS in the vacuum state (where the state of each site is represented by a column vector with a 1 in the first row and zeros elsewhere). Setting state=:FullOccupy will produce an MPS in which each site is fully occupied (ie. a column vector with a 1 in the last row and zeros elsewhere).
The argument mpsorthog can be used to set the gauge of the resulting MPS.
source
MPSDynamics.randmps — Function
randmps(N::Int, d::Int, Dmax::Int, T=Float64)
Construct a random, N-site, right-normalised MPS with all local Hilbert space dimensions given by d.
source
MPSDynamics.randmps — Function
randmps(tree::Tree, physdims, Dmax::Int, T::Type{<:Number} = Float64)
Construct a random, right-normalised, tree-MPS, with structure given by tree and max bond-dimension given by Dmax.
The local Hilbert space dimensions are specified by physdims which can either be of type Dims{length(tree)}, specifying the dimension of each site, or of type Int, in which case the same local dimension is used for every site.
source
MPSDynamics.randmps — Method
randmps(physdims::Dims{N}, Dmax::Int, T::Type{<:Number} = Float64) where {N}
Construct a random, right-normalised MPS with local Hilbert space dimensions given by physdims and max bond-dimension given by Dmax. 
T specifies the element type, eg. use T=ComplexF64 for a complex valued MPS.
source
MPSDynamics.randtree — Method
randtree(numnodes::Int, maxdegree::Int)
Construct a random tree with nummodes modes and max degree maxdegree.
source
MPSDynamics.rmsd — Method
rmsd(dat1::Vector{Float64}, dat2::Vector{Float64})
Calculate the root mean squared difference between two measurements of an observable over the same time period.
source
MPSDynamics.svdmps — Method
svdmps(A)
For a right normalised mps A compute the full svd spectrum for a bipartition at every bond.
source
MPSDynamics.svdtrunc — Method
U, S, Vd = svdtrunc(A; truncdim = max(size(A)...), truncerr = 0.)
Perform a truncated SVD, with maximum number of singular values to keep equal to truncdim or truncating any singular values smaller than truncerr. If both options are provided, the smallest number of singular values will be kept. Unlike the SVD in Julia, this returns matrix U, a diagonal matrix (not a vector) S, and Vt such that A ≈ U * S * Vt
source

+0.0
source
MPSDynamics.entanglemententropy — Method
entanglemententropy(A)

For a list of tensors A representing a right orthonormalized MPS, compute the entanglement entropy for a bipartite cut for every bond.

source
MPSDynamics.findchainlength — Method
findchainlength(T, cparams...; eps=10^-6)

Estimate length of chain required for a particular set of chain parameters by calulating how long an excitation on the first site takes to reach the end. The chain length is given as the length required for the excitation to have just reached the last site after time T.

source
MPSDynamics.findchild — Method
findchild(node::TreeNode, id::Int)

Return integer corresponding to the which number child site id is of node.

source
MPSDynamics.measure1siteoperator — Method
measure1siteoperator(A, O)

For a list of tensors A representing a right orthonormalized MPS, compute the local expectation value of a one-site operator O for every site or just one if i is specified.

For calculating operators on single sites this will be more efficient if the site is on the left of the mps.

source
MPSDynamics.measure2siteoperator — Method
 measure2siteoperator(A::Vector, M1, M2, j1, j2)

Caculate expectation of M1*M2 where M1 acts on site j1 and M2 acts on site j2, assumes A is right normalised.

source
MPSDynamics.modemps — Function
modemps(N::Int, k::Vector{Int}, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])

Generate an MPS with numex excitations of an equal superposition of modes k of a bosonic tight-binding chain.

source
MPSDynamics.modemps — Function
modemps(N::Int, k::Int, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])

Generate an MPS with numex excitations of mode k of a bosonic tight-binding chain.

chainparams takes the form [e::Vector, t::Vector] where e are the on-site energies and t are the hoppping parameters.

The returned MPS will have bond-dimensions and physical dimensions numex+1

source
MPSDynamics.mpsmoveoc! — Method
mpsmoveoc!(A::TreeNetwork, id::Int)

Move the orthogonality centre of right normalised tree-MPS A to site id.

This function will be more efficient than using mpsmixednorm! if the tree-MPS is already right-normalised.

source
MPSDynamics.mpsshiftoc! — Method
mpsshiftoc!(A::TreeNetwork, newhd::Int)

Shift the orthogonality centre by one site, setting new head-node newhd.

source
MPSDynamics.normmps — Method
normmps(A::Vector; mpsorthog=:None)

Calculate norm of MPS A.

Setting mpsorthog=:Right/:Left will calculate the norm assuming right/left canonical form. Setting mpsorthog=OC::Int will cause the norm to be calculated assuming the orthoganility center is on site OC. If mpsorthog is :None the norm will be calculated as an MPS-MPS product.

source
MPSDynamics.normmps — Method
normmps(net::TreeNetwork; mpsorthog=:None)

When applied to a tree-MPS mpsorthog=:Left is not defined.

source
MPSDynamics.orthcentersmps — Method
orthcentersmps(A)

Compute the orthoganality centres of MPS A.

Return value is a list in which each element is the corresponding site tensor of A with the orthoganility centre on that site. Assumes A is right normalised.

source
MPSDynamics.productstatemps — Function
productstatemps(N::Int, d::Int, Dmax=1; state=:Vacuum, mpsorthog=:Right)

Return an N-site MPS with all local Hilbert space dimensions given by d.

source
MPSDynamics.productstatemps — Function
productstatemps(physdims::Dims, Dmax=1; state=:Vacuum, mpsorthog=:Right)

Return an MPS representing a product state with local Hilbert space dimensions given by physdims.

By default all bond-dimensions will be 1 since the state is a product state. However, to embed the product state in a manifold of greater bond-dimension, Dmax can be set accordingly.

The indvidual states of the MPS sites can be provdided by setting state to a list of column vectors. Setting state=:Vacuum will produce an MPS in the vacuum state (where the state of each site is represented by a column vector with a 1 in the first row and zeros elsewhere). Setting state=:FullOccupy will produce an MPS in which each site is fully occupied (ie. a column vector with a 1 in the last row and zeros elsewhere).

The argument mpsorthog can be used to set the gauge of the resulting MPS.

source
MPSDynamics.randmps — Function
randmps(tree::Tree, physdims, Dmax::Int, T::Type{<:Number} = Float64)

Construct a random, right-normalised, tree-MPS, with structure given by tree and max bond-dimension given by Dmax.

The local Hilbert space dimensions are specified by physdims which can either be of type Dims{length(tree)}, specifying the dimension of each site, or of type Int, in which case the same local dimension is used for every site.

source
MPSDynamics.randmps — Function
randmps(N::Int, d::Int, Dmax::Int, T=Float64)

Construct a random, N-site, right-normalised MPS with all local Hilbert space dimensions given by d.

source
MPSDynamics.randmps — Method
randmps(physdims::Dims{N}, Dmax::Int, T::Type{<:Number} = Float64) where {N}

Construct a random, right-normalised MPS with local Hilbert space dimensions given by physdims and max bond-dimension given by Dmax.

T specifies the element type, eg. use T=ComplexF64 for a complex valued MPS.

source
MPSDynamics.randtree — Method
randtree(numnodes::Int, maxdegree::Int)

Construct a random tree with nummodes modes and max degree maxdegree.

source
MPSDynamics.rmsd — Method
rmsd(dat1::Vector{Float64}, dat2::Vector{Float64})

Calculate the root mean squared difference between two measurements of an observable over the same time period.

source
MPSDynamics.svdmps — Method
svdmps(A)

For a right normalised mps A compute the full svd spectrum for a bipartition at every bond.

source
MPSDynamics.svdtrunc — Method
U, S, Vd = svdtrunc(A; truncdim = max(size(A)...), truncerr = 0.)

Perform a truncated SVD, with maximum number of singular values to keep equal to truncdim or truncating any singular values smaller than truncerr. If both options are provided, the smallest number of singular values will be kept. Unlike the SVD in Julia, this returns matrix U, a diagonal matrix (not a vector) S, and Vt such that A ≈ U * S * Vt

source
diff --git a/docs/build/search/index.html b/docs/build/search/index.html index 67d16eb..ddc6dce 100644 --- a/docs/build/search/index.html +++ b/docs/build/search/index.html @@ -1,2 +1,2 @@ -Search · MPSDynamics Documentation

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    +Search · MPSDynamics.jl

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      diff --git a/docs/build/search_index.js b/docs/build/search_index.js index 731c131..f53f684 100644 --- a/docs/build/search_index.js +++ b/docs/build/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"#MPSDynamics-Documentation","page":"MPSDynamics Documentation","title":"MPSDynamics Documentation","text":"","category":"section"},{"location":"","page":"MPSDynamics Documentation","title":"MPSDynamics Documentation","text":"Modules = [MPSDynamics]","category":"page"},{"location":"#MPSDynamics.addchild!-Tuple{MPSDynamics.Tree,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.addchild!","text":"addchild!(tree::Tree, id::Int)\n\nAdd child to node id of tree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.addchildren!-Tuple{MPSDynamics.Tree,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.addchildren!","text":"addchildren!(tree::Tree, id::Int, n::Int)\n\nAdd n children to node id of tree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chaincoeffs_ohmic-Tuple{Any,Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.chaincoeffs_ohmic","text":"chaincoeffs_ohmic(nummodes, α, s, beta=\"inf\"; wc=1, soft=false)\n\nGenerate chain coefficients for an Harmonic bath coupled to a spin-1/2 with spectral density given by: \n\nsoft cutoff: J(ω) = 2παω_c (fracωω_c)^s exp(-ωω_c) \n\nhard cutoff: J(ω) = 2παω_c (fracωω_c)^s θ(ω-ω_c)\n\nThe Hamiltonian is given by:\n\nH = fracω_02σ_z + Δσ_x + σ_xsum_kg_k(b_k^dagger+b_k) + sum_kω_kb_k^dagger b_k\n\nAnd the spectral density is defined by:\n\nJ(ω) πsum_kg_k^2δ(ω-ω_k)\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainmps-Tuple{Int64,Array{Int64,1},Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainmps","text":"chainmps(N::Int, sites::Vector{Int}, numex::Int)\n\nGenerate an MPS with numex excitations of an equal super-position over sites\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainmps-Tuple{Int64,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainmps","text":"chainmps(N::Int, site::Int, numex::Int)\n\nGenerate an MPS with numex excitations on site\n\nThe returned MPS will have bond-dimensions and physical dimensions numex+1\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainprop-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainprop","text":"chainprop(t, cparams...)\n\nPropagate an excitation placed initially on the first site of a tight-binding chain with parameters given by cparams for a time t and return occupation expectation for each site.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.dynamap-NTuple{4,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.dynamap","text":"dynamap(ps1,ps2,ps3,ps4)\n\nCalulate complete dynamical map to time step at which ps1, ps2, ps3 and ps4 are specified.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.electron2kmps","page":"MPSDynamics Documentation","title":"MPSDynamics.electron2kmps","text":"electronkmps(N::Int, k::Vector{Int}, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with 2 electrons in k-states k1 and k2.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.electronkmps","page":"MPSDynamics Documentation","title":"MPSDynamics.electronkmps","text":"electronkmps(N::Int, k::Int, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS for an electron with momentum k.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.elementmpo-Tuple{Any,Vararg{Any,N} where N}","page":"MPSDynamics Documentation","title":"MPSDynamics.elementmpo","text":"elementmpo(M, el...)\n\nReturn the element of the MPO M for the set of physical states el...\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.elementmps-Tuple{Any,Vararg{Any,N} where N}","page":"MPSDynamics Documentation","title":"MPSDynamics.elementmps","text":"elementmps(A, el...)\n\nReturn the element of the MPS A for the set of physical states el...\n\nExamples\n\njulia> A = chainmps(6, [2,4], 1);\n\njulia> elementmps(A, 1, 2, 1, 1, 1, 1)\n0.7071067811865475\n\njulia> elementmps(A, 1, 1, 1, 2, 1, 1)\n0.7071067811865475\n\njulia> elementmps(A, 1, 2, 1, 2, 1, 1)\n0.0\n\njulia> elementmps(A, 1, 1, 1, 1, 1, 1)\n0.0\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.entanglemententropy-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.entanglemententropy","text":"entanglemententropy(A)\n\nFor a list of tensors A representing a right orthonormalized MPS, compute the entanglement entropy for a bipartite cut for every bond.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.findchainlength-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.findchainlength","text":"findchainlength(T, cparams...; eps=10^-6)\n\nEstimate length of chain required for a particular set of chain parameters by calulating how long an excitation on the first site takes to reach the end. The chain length is given as the length required for the excitation to have just reached the last site after time T.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.findchild-Tuple{MPSDynamics.TreeNode,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.findchild","text":"findchild(node::TreeNode, id::Int)\n\nReturn integer corresponding to the which number child site id is of node.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure-Tuple{Any,OneSiteObservable}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure","text":"measure(A, O; kwargs...)\n\nmeasure observable O on mps state A\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure1siteoperator-Tuple{Array{T,1} where T,Any,Array{Int64,1}}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure1siteoperator","text":"measure1siteoperator(A, O)\n\nFor a list of tensors A representing a right orthonormalized MPS, compute the local expectation value of a one-site operator O for every site or just one if i is specified.\n\nFor calculating operators on single sites this will be more efficient if the site is on the left of the mps.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure2siteoperator-Tuple{Array{T,1} where T,Any,Any,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure2siteoperator","text":" measure2siteoperator(A::Vector, M1, M2, j1, j2)\n\nCaculate expectation of M1*M2 where M1 acts on site j1 and M2 acts on site j2, assumes A is right normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.modemps","page":"MPSDynamics Documentation","title":"MPSDynamics.modemps","text":"modemps(N::Int, k::Vector{Int}, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with numex excitations of an equal superposition of modes k of a bosonic tight-binding chain.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.modemps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.modemps","text":"modemps(N::Int, k::Int, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with numex excitations of mode k of a bosonic tight-binding chain. \n\nchainparams takes the form [e::Vector, t::Vector] where e are the on-site energies and t are the hoppping parameters.\n\nThe returned MPS will have bond-dimensions and physical dimensions numex+1\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsembed!-Tuple{Array{T,1} where T,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsembed!","text":"mpsembed(A::Vector, Dmax::Int)\n\nEmbed MPS A in manifold of max bond-dimension Dmax\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsleftnorm!","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsleftnorm!","text":"mpsleftnorm!(A::Vector, jq::Int=length(A))\n\nLeft orthoganalise MPS A up to site jq.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsmixednorm!-Tuple{Array{T,1} where T,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmixednorm!","text":"mpsmixednorm!(A::Vector, OC::Int)\n\nPut MPS A into mixed canonical form with orthogonality centre on site OC.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsmixednorm!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmixednorm!","text":"mpsmixednorm!(A::TreeNetwork, id::Int)\n\nNormalise tree-MPS A such that orthogonality centre is on site id.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsmoveoc!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmoveoc!","text":"mpsmoveoc!(A::TreeNetwork, id::Int)\n\nMove the orthogonality centre of right normalised tree-MPS A to site id.\n\nThis function will be more efficient than using mpsmixednorm! if the tree-MPS is already right-normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsrightnorm!","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsrightnorm!","text":"mpsrightnorm!(A::Vector, jq::Int=1)\n\nRight orthoganalise MPS A up to site jq.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsrightnorm!-Tuple{MPSDynamics.TreeNetwork}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsrightnorm!","text":"mpsrightnorm!(A::TreeNetwork)\n\nWhen applied to a tree-MPS, right normalise towards head-node.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsshiftoc!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsshiftoc!","text":"mpsshiftoc!(A::TreeNetwork, newhd::Int)\n\nShift the orthogonality centre by one site, setting new head-node newhd.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.normmps-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.normmps","text":"normmps(A::Vector; mpsorthog=:None)\n\nCalculate norm of MPS A.\n\nSetting mpsorthog=:Right/:Left will calculate the norm assuming right/left canonical form. Setting mpsorthog=OC::Int will cause the norm to be calculated assuming the orthoganility center is on site OC. If mpsorthog is :None the norm will be calculated as an MPS-MPS product.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.normmps-Tuple{MPSDynamics.TreeNetwork}","page":"MPSDynamics Documentation","title":"MPSDynamics.normmps","text":"normmps(net::TreeNetwork; mpsorthog=:None)\n\nWhen applied to a tree-MPS mpsorthog=:Left is not defined.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.orthcentersmps-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.orthcentersmps","text":"orthcentersmps(A)\n\nCompute the orthoganality centres of MPS A.\n\nReturn value is a list in which each element is the corresponding site tensor of A with the orthoganility centre on that site. Assumes A is right normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.physdims-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.physdims","text":"physdims(M)\n\nReturn the physical dimensions of an MPS or MPO M.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.productstatemps","page":"MPSDynamics Documentation","title":"MPSDynamics.productstatemps","text":"productstatemps(N::Int, d::Int, Dmax=1; state=:Vacuum, mpsorthog=:Right)\n\nReturn an N-site MPS with all local Hilbert space dimensions given by d. \n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.productstatemps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.productstatemps","text":"productstatemps(physdims::Dims, Dmax=1; state=:Vacuum, mpsorthog=:Right)\n\nReturn an MPS representing a product state with local Hilbert space dimensions given by physdims.\n\nBy default all bond-dimensions will be 1 since the state is a product state. However, to embed the product state in a manifold of greater bond-dimension, Dmax can be set accordingly.\n\nThe indvidual states of the MPS sites can be provdided by setting state to a list of column vectors. Setting state=:Vacuum will produce an MPS in the vacuum state (where the state of each site is represented by a column vector with a 1 in the first row and zeros elsewhere). Setting state=:FullOccupy will produce an MPS in which each site is fully occupied (ie. a column vector with a 1 in the last row and zeros elsewhere).\n\nThe argument mpsorthog can be used to set the gauge of the resulting MPS.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randisometry-Tuple{Type,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.randisometry","text":"randisometry([T=Float64], dims...)\n\nConstruct a random isometry\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.randmps","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(N::Int, d::Int, Dmax::Int, T=Float64)\n\nConstruct a random, N-site, right-normalised MPS with all local Hilbert space dimensions given by d.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randmps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(tree::Tree, physdims, Dmax::Int, T::Type{<:Number} = Float64)\n\nConstruct a random, right-normalised, tree-MPS, with structure given by tree and max bond-dimension given by Dmax.\n\nThe local Hilbert space dimensions are specified by physdims which can either be of type Dims{length(tree)}, specifying the dimension of each site, or of type Int, in which case the same local dimension is used for every site.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randmps-Union{Tuple{N}, Tuple{Tuple{Vararg{Int64,N}},Int64}, Tuple{Tuple{Vararg{Int64,N}},Int64,Type{#s131} where #s131<:Number}} where N","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(physdims::Dims{N}, Dmax::Int, T::Type{<:Number} = Float64) where {N}\n\nConstruct a random, right-normalised MPS with local Hilbert space dimensions given by physdims and max bond-dimension given by Dmax. \n\nT specifies the element type, eg. use T=ComplexF64 for a complex valued MPS.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.randtree-Tuple{Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.randtree","text":"randtree(numnodes::Int, maxdegree::Int)\n\nConstruct a random tree with nummodes modes and max degree maxdegree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.rmsd-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.rmsd","text":"rmsd(dat1::Vector{Float64}, dat2::Vector{Float64})\n\nCalculate the root mean squared difference between two measurements of an observable over the same time period.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.svdmps-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.svdmps","text":"svdmps(A)\n\nFor a right normalised mps A compute the full svd spectrum for a bipartition at every bond.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.svdtrunc-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.svdtrunc","text":"U, S, Vd = svdtrunc(A; truncdim = max(size(A)...), truncerr = 0.)\n\nPerform a truncated SVD, with maximum number of singular values to keep equal to truncdim or truncating any singular values smaller than truncerr. If both options are provided, the smallest number of singular values will be kept. Unlike the SVD in Julia, this returns matrix U, a diagonal matrix (not a vector) S, and Vt such that A ≈ U * S * Vt\n\n\n\n\n\n","category":"method"}] +[{"location":"#MPSDynamics-Documentation","page":"MPSDynamics Documentation","title":"MPSDynamics Documentation","text":"","category":"section"},{"location":"","page":"MPSDynamics Documentation","title":"MPSDynamics Documentation","text":"Modules = [MPSDynamics]","category":"page"},{"location":"#MPSDynamics.addchild!-Tuple{MPSDynamics.Tree,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.addchild!","text":"addchild!(tree::Tree, id::Int)\n\nAdd child to node id of tree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.addchildren!-Tuple{MPSDynamics.Tree,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.addchildren!","text":"addchildren!(tree::Tree, id::Int, n::Int)\n\nAdd n children to node id of tree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chaincoeffs_ohmic-Tuple{Any,Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.chaincoeffs_ohmic","text":"chaincoeffs_ohmic(nummodes, α, s, beta=\"inf\"; wc=1, soft=false)\n\nGenerate chain coefficients for an Harmonic bath coupled to a spin-1/2 with spectral density given by: \n\nsoft cutoff: J(ω) = 2παω_c (fracωω_c)^s exp(-ωω_c) \n\nhard cutoff: J(ω) = 2παω_c (fracωω_c)^s θ(ω-ω_c)\n\nThe Hamiltonian is given by:\n\nH = fracω_02σ_z + Δσ_x + σ_xsum_kg_k(b_k^dagger+b_k) + sum_kω_kb_k^dagger b_k\n\nAnd the spectral density is defined by:\n\nJ(ω) πsum_kg_k^2δ(ω-ω_k)\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainmps-Tuple{Int64,Array{Int64,1},Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainmps","text":"chainmps(N::Int, sites::Vector{Int}, numex::Int)\n\nGenerate an MPS with numex excitations of an equal super-position over sites\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainmps-Tuple{Int64,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainmps","text":"chainmps(N::Int, site::Int, numex::Int)\n\nGenerate an MPS with numex excitations on site\n\nThe returned MPS will have bond-dimensions and physical dimensions numex+1\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.chainprop-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.chainprop","text":"chainprop(t, cparams...)\n\nPropagate an excitation placed initially on the first site of a tight-binding chain with parameters given by cparams for a time t and return occupation expectation for each site.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.dynamap-NTuple{4,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.dynamap","text":"dynamap(ps1,ps2,ps3,ps4)\n\nCalulate complete dynamical map to time step at which ps1, ps2, ps3 and ps4 are specified.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.electron2kmps","page":"MPSDynamics Documentation","title":"MPSDynamics.electron2kmps","text":"electronkmps(N::Int, k::Vector{Int}, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with 2 electrons in k-states k1 and k2.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.electronkmps","page":"MPSDynamics Documentation","title":"MPSDynamics.electronkmps","text":"electronkmps(N::Int, k::Int, spin=:Up, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS for an electron with momentum k.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.elementmpo-Tuple{Any,Vararg{Any,N} where N}","page":"MPSDynamics Documentation","title":"MPSDynamics.elementmpo","text":"elementmpo(M, el...)\n\nReturn the element of the MPO M for the set of physical states el...\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.elementmps-Tuple{Any,Vararg{Any,N} where N}","page":"MPSDynamics Documentation","title":"MPSDynamics.elementmps","text":"elementmps(A, el...)\n\nReturn the element of the MPS A for the set of physical states el...\n\nExamples\n\njulia> A = chainmps(6, [2,4], 1);\n\njulia> elementmps(A, 1, 2, 1, 1, 1, 1)\n0.7071067811865475\n\njulia> elementmps(A, 1, 1, 1, 2, 1, 1)\n0.7071067811865475\n\njulia> elementmps(A, 1, 2, 1, 2, 1, 1)\n0.0\n\njulia> elementmps(A, 1, 1, 1, 1, 1, 1)\n0.0\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.entanglemententropy-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.entanglemententropy","text":"entanglemententropy(A)\n\nFor a list of tensors A representing a right orthonormalized MPS, compute the entanglement entropy for a bipartite cut for every bond.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.findchainlength-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.findchainlength","text":"findchainlength(T, cparams...; eps=10^-6)\n\nEstimate length of chain required for a particular set of chain parameters by calulating how long an excitation on the first site takes to reach the end. The chain length is given as the length required for the excitation to have just reached the last site after time T.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.findchild-Tuple{MPSDynamics.TreeNode,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.findchild","text":"findchild(node::TreeNode, id::Int)\n\nReturn integer corresponding to the which number child site id is of node.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure-Tuple{Any,OneSiteObservable}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure","text":"measure(A, O; kwargs...)\n\nmeasure observable O on mps state A\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure1siteoperator-Tuple{Array{T,1} where T,Any,Array{Int64,1}}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure1siteoperator","text":"measure1siteoperator(A, O)\n\nFor a list of tensors A representing a right orthonormalized MPS, compute the local expectation value of a one-site operator O for every site or just one if i is specified.\n\nFor calculating operators on single sites this will be more efficient if the site is on the left of the mps.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.measure2siteoperator-Tuple{Array{T,1} where T,Any,Any,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.measure2siteoperator","text":" measure2siteoperator(A::Vector, M1, M2, j1, j2)\n\nCaculate expectation of M1*M2 where M1 acts on site j1 and M2 acts on site j2, assumes A is right normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.modemps","page":"MPSDynamics Documentation","title":"MPSDynamics.modemps","text":"modemps(N::Int, k::Vector{Int}, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with numex excitations of an equal superposition of modes k of a bosonic tight-binding chain.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.modemps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.modemps","text":"modemps(N::Int, k::Int, numex::Int, chainparams=[fill(1.0,N), fill(1.0,N-1)])\n\nGenerate an MPS with numex excitations of mode k of a bosonic tight-binding chain. \n\nchainparams takes the form [e::Vector, t::Vector] where e are the on-site energies and t are the hoppping parameters.\n\nThe returned MPS will have bond-dimensions and physical dimensions numex+1\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsembed!-Tuple{Array{T,1} where T,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsembed!","text":"mpsembed(A::Vector, Dmax::Int)\n\nEmbed MPS A in manifold of max bond-dimension Dmax\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsleftnorm!","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsleftnorm!","text":"mpsleftnorm!(A::Vector, jq::Int=length(A))\n\nLeft orthoganalise MPS A up to site jq.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsmixednorm!-Tuple{Array{T,1} where T,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmixednorm!","text":"mpsmixednorm!(A::Vector, OC::Int)\n\nPut MPS A into mixed canonical form with orthogonality centre on site OC.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsmixednorm!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmixednorm!","text":"mpsmixednorm!(A::TreeNetwork, id::Int)\n\nNormalise tree-MPS A such that orthogonality centre is on site id.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsmoveoc!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsmoveoc!","text":"mpsmoveoc!(A::TreeNetwork, id::Int)\n\nMove the orthogonality centre of right normalised tree-MPS A to site id.\n\nThis function will be more efficient than using mpsmixednorm! if the tree-MPS is already right-normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsrightnorm!","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsrightnorm!","text":"mpsrightnorm!(A::Vector, jq::Int=1)\n\nRight orthoganalise MPS A up to site jq.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.mpsrightnorm!-Tuple{MPSDynamics.TreeNetwork}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsrightnorm!","text":"mpsrightnorm!(A::TreeNetwork)\n\nWhen applied to a tree-MPS, right normalise towards head-node.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.mpsshiftoc!-Tuple{MPSDynamics.TreeNetwork,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.mpsshiftoc!","text":"mpsshiftoc!(A::TreeNetwork, newhd::Int)\n\nShift the orthogonality centre by one site, setting new head-node newhd.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.normmps-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.normmps","text":"normmps(A::Vector; mpsorthog=:None)\n\nCalculate norm of MPS A.\n\nSetting mpsorthog=:Right/:Left will calculate the norm assuming right/left canonical form. Setting mpsorthog=OC::Int will cause the norm to be calculated assuming the orthoganility center is on site OC. If mpsorthog is :None the norm will be calculated as an MPS-MPS product.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.normmps-Tuple{MPSDynamics.TreeNetwork}","page":"MPSDynamics Documentation","title":"MPSDynamics.normmps","text":"normmps(net::TreeNetwork; mpsorthog=:None)\n\nWhen applied to a tree-MPS mpsorthog=:Left is not defined.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.orthcentersmps-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.orthcentersmps","text":"orthcentersmps(A)\n\nCompute the orthoganality centres of MPS A.\n\nReturn value is a list in which each element is the corresponding site tensor of A with the orthoganility centre on that site. Assumes A is right normalised.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.physdims-Tuple{Array{T,1} where T}","page":"MPSDynamics Documentation","title":"MPSDynamics.physdims","text":"physdims(M)\n\nReturn the physical dimensions of an MPS or MPO M.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.productstatemps","page":"MPSDynamics Documentation","title":"MPSDynamics.productstatemps","text":"productstatemps(N::Int, d::Int, Dmax=1; state=:Vacuum, mpsorthog=:Right)\n\nReturn an N-site MPS with all local Hilbert space dimensions given by d. \n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.productstatemps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.productstatemps","text":"productstatemps(physdims::Dims, Dmax=1; state=:Vacuum, mpsorthog=:Right)\n\nReturn an MPS representing a product state with local Hilbert space dimensions given by physdims.\n\nBy default all bond-dimensions will be 1 since the state is a product state. However, to embed the product state in a manifold of greater bond-dimension, Dmax can be set accordingly.\n\nThe indvidual states of the MPS sites can be provdided by setting state to a list of column vectors. Setting state=:Vacuum will produce an MPS in the vacuum state (where the state of each site is represented by a column vector with a 1 in the first row and zeros elsewhere). Setting state=:FullOccupy will produce an MPS in which each site is fully occupied (ie. a column vector with a 1 in the last row and zeros elsewhere).\n\nThe argument mpsorthog can be used to set the gauge of the resulting MPS.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randisometry-Tuple{Type,Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.randisometry","text":"randisometry([T=Float64], dims...)\n\nConstruct a random isometry\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.randmps","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(tree::Tree, physdims, Dmax::Int, T::Type{<:Number} = Float64)\n\nConstruct a random, right-normalised, tree-MPS, with structure given by tree and max bond-dimension given by Dmax.\n\nThe local Hilbert space dimensions are specified by physdims which can either be of type Dims{length(tree)}, specifying the dimension of each site, or of type Int, in which case the same local dimension is used for every site.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randmps-2","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(N::Int, d::Int, Dmax::Int, T=Float64)\n\nConstruct a random, N-site, right-normalised MPS with all local Hilbert space dimensions given by d.\n\n\n\n\n\n","category":"function"},{"location":"#MPSDynamics.randmps-Union{Tuple{N}, Tuple{Tuple{Vararg{Int64,N}},Int64}, Tuple{Tuple{Vararg{Int64,N}},Int64,Type{#s131} where #s131<:Number}} where N","page":"MPSDynamics Documentation","title":"MPSDynamics.randmps","text":"randmps(physdims::Dims{N}, Dmax::Int, T::Type{<:Number} = Float64) where {N}\n\nConstruct a random, right-normalised MPS with local Hilbert space dimensions given by physdims and max bond-dimension given by Dmax. \n\nT specifies the element type, eg. use T=ComplexF64 for a complex valued MPS.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.randtree-Tuple{Int64,Int64}","page":"MPSDynamics Documentation","title":"MPSDynamics.randtree","text":"randtree(numnodes::Int, maxdegree::Int)\n\nConstruct a random tree with nummodes modes and max degree maxdegree.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.rmsd-Tuple{Any,Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.rmsd","text":"rmsd(dat1::Vector{Float64}, dat2::Vector{Float64})\n\nCalculate the root mean squared difference between two measurements of an observable over the same time period.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.svdmps-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.svdmps","text":"svdmps(A)\n\nFor a right normalised mps A compute the full svd spectrum for a bipartition at every bond.\n\n\n\n\n\n","category":"method"},{"location":"#MPSDynamics.svdtrunc-Tuple{Any}","page":"MPSDynamics Documentation","title":"MPSDynamics.svdtrunc","text":"U, S, Vd = svdtrunc(A; truncdim = max(size(A)...), truncerr = 0.)\n\nPerform a truncated SVD, with maximum number of singular values to keep equal to truncdim or truncating any singular values smaller than truncerr. If both options are provided, the smallest number of singular values will be kept. Unlike the SVD in Julia, this returns matrix U, a diagonal matrix (not a vector) S, and Vt such that A ≈ U * S * Vt\n\n\n\n\n\n","category":"method"}] } diff --git a/docs/make.jl b/docs/make.jl index 8018a02..e33fe26 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -10,11 +10,14 @@ makedocs( repo = "git@github.com:angusdunnett/MPSDynamics.git", highlightsig = true, sitename = "MPSDynamics.jl", - authors = "Angus Dunnett" + authors = "Angus Dunnett", expandfirst = [], ) -deploydocs(repo = "git@github.com:angusdunnett/MPSDynamics.git") +deploydocs(repo = "git@github.com:angusdunnett/MPSDynamics.git", + branch = "gh-pages", + devurl = "dev" + ) # bitbucket = "/home/angus/Documents/Julia/angus-dunnett.bitbucket.io/" # build = "/home/angus/Documents/Julia/mps2.0/docs/build/"