More general ITensorNetwork constructors

Author: mtfishman

Project.toml

@@ -1,7 +1,7 @@
 name = "ITensorNetworks"
 uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
 authors = ["Matthew Fishman <[email protected]> and contributors"]
-version = "0.2.1"
+version = "0.2.2"
 
 [deps]
 Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
@@ -19,12 +19,12 @@ Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
 
 [compat]
 Compat = "3, 4"
-DataGraphs = "0.1.1"
+DataGraphs = "0.1.7"
 Dictionaries = "0.3.15"
 DocStringExtensions = "0.8, 0.9"
 Graphs = "1.6"
-ITensors = "0.3"
-NamedGraphs = "0.1.3"
+ITensors = "0.3.23"
+NamedGraphs = "0.1.8"
 Requires = "1.3"
 SimpleTraits = "0.9"
 SplitApplyCombine = "1.2"

README.md

@@ -36,7 +36,7 @@ and 3 edge(s):
 3 => 4
 
 with vertex data:
-4-element Dictionaries.Dictionary{Int64, Any}
+4-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=739|"1↔2"),)
  2 │ ((dim=2|id=739|"1↔2"), (dim=2|id=920|"2↔3"))
  3 │ ((dim=2|id=920|"2↔3"), (dim=2|id=761|"3↔4"))
@@ -88,7 +88,7 @@ and 4 edge(s):
 (1, 2) => (2, 2)
 
 with vertex data:
-4-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+4-element Dictionary{Tuple{Int64, Int64}, Any}
  (1, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=723|"1×1↔1×2"))
  (2, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=823|"2×1↔2×2"))
  (1, 2) │ ((dim=2|id=723|"1×1↔1×2"), (dim=2|id=712|"1×2↔2×2"))
@@ -118,7 +118,7 @@ and 1 edge(s):
 (1, 1) => (1, 2)
 
 with vertex data:
-2-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+2-element Dictionary{Tuple{Int64, Int64}, Any}
  (1, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=723|"1×1↔1×2"))
  (1, 2) │ ((dim=2|id=723|"1×1↔1×2"), (dim=2|id=712|"1×2↔2×2"))
 
@@ -132,7 +132,7 @@ and 1 edge(s):
 (2, 1) => (2, 2)
 
 with vertex data:
-2-element Dictionaries.Dictionary{Tuple{Int64, Int64}, Any}
+2-element Dictionary{Tuple{Int64, Int64}, Any}
  (2, 1) │ ((dim=2|id=74|"1×1↔2×1"), (dim=2|id=823|"2×1↔2×2"))
  (2, 2) │ ((dim=2|id=823|"2×1↔2×2"), (dim=2|id=712|"1×2↔2×2"))
 ```
@@ -155,13 +155,13 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Vector{Index}}
+3-element Dictionary{Int64, Vector{Index}}
  1 │ Index[(dim=2|id=598|"S=1/2,Site,n=1")]
  2 │ Index[(dim=2|id=457|"S=1/2,Site,n=2")]
  3 │ Index[(dim=2|id=683|"S=1/2,Site,n=3")]
 
 and edge data:
-0-element Dictionaries.Dictionary{NamedGraphs.NamedEdge{Int64}, Vector{Index}}
+0-element Dictionary{NamedEdge{Int64}, Vector{Index}}
 
 julia> tn1 = ITensorNetwork(s; link_space=2)
 ITensorNetwork{Int64} with 3 vertices:
@@ -175,7 +175,7 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Any}
+3-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=598|"S=1/2,Site,n=1"), (dim=2|id=123|"1↔2"))
  2 │ ((dim=2|id=457|"S=1/2,Site,n=2"), (dim=2|id=123|"1↔2"), (dim=2|id=656|"2↔3…
  3 │ ((dim=2|id=683|"S=1/2,Site,n=3"), (dim=2|id=656|"2↔3"))
@@ -192,7 +192,7 @@ and 2 edge(s):
 2 => 3
 
 with vertex data:
-3-element Dictionaries.Dictionary{Int64, Any}
+3-element Dictionary{Int64, Any}
  1 │ ((dim=2|id=598|"S=1/2,Site,n=1"), (dim=2|id=382|"1↔2"))
  2 │ ((dim=2|id=457|"S=1/2,Site,n=2"), (dim=2|id=382|"1↔2"), (dim=2|id=190|"2↔3…
  3 │ ((dim=2|id=683|"S=1/2,Site,n=3"), (dim=2|id=190|"2↔3"))
@@ -312,5 +312,9 @@ This file was generated with [weave.jl](https://github.com/JunoLab/Weave.jl) wit
 
 ```julia
 using ITensorNetworks, Weave
-weave(joinpath(pkgdir(ITensorNetworks), "examples", "README.jl"); doctype="github", out_path=pkgdir(ITensorNetworks))
+weave(
+  joinpath(pkgdir(ITensorNetworks), "examples", "README.jl");
+  doctype="github",
+  out_path=pkgdir(ITensorNetworks),
+)
 ```

examples/belief_propagation/bpexample.jl

@@ -6,9 +6,9 @@ using ITensorNetworks
 using ITensorNetworks: construct_initial_mts, update_all_mts, get_single_site_expec
 
 n = 4
-dims = (n, n)
-g = named_grid(dims)
-# g = named_comb_tree(dims)
+system_dims = (n, n)
+g = named_grid(system_dims)
+# g = named_comb_tree(system_dims)
 s = siteinds("S=1/2", g)
 chi = 2
 
@@ -19,7 +19,7 @@ combiners = linkinds_combiners(ψψ)
 ψψ = combine_linkinds(ψψ, combiners)
 
 # Apply Sz to site v
-v = one.(dims)
+v = one.(system_dims)
 Oψ = copy(ψ)
 Oψ[v] = apply(op("Sz", s[v]), ψ[v])
 ψOψ = inner_network(ψ, Oψ; flatten=true, map_bra_linkinds=prime)

examples/contraction_sequence/contraction_sequence.jl

@@ -7,8 +7,8 @@ Random.seed!(1234)
 
 ITensors.disable_warn_order()
 
-dims = (2, 3)
-g = named_grid(dims)
+system_dims = (2, 3)
+g = named_grid(system_dims)
 s = siteinds("S=1/2", g)
 
 χ = 10
@@ -17,7 +17,7 @@ s = siteinds("S=1/2", g)
 tn = norm_sqr_network(ψ)
 
 # Contraction sequence for exactly computing expectation values
-# contract_edges = map(t -> (1, t...), collect(keys(cartesian_to_linear(dims))))
+# contract_edges = map(t -> (1, t...), collect(keys(cartesian_to_linear(system_dims))))
 # inner_sequence = reduce((x, y) -> [x, y], contract_edges)
 
 println("optimal")

examples/examples.jl

@@ -4,8 +4,8 @@ using ITensorUnicodePlots
 using NamedGraphs
 
 χ, d = 5, 2
-dims = (4, 4)
-g = named_grid(dims)
+system_dims = (4, 4)
+g = named_grid(system_dims)
 
 # Network of indices
 is = IndsNetwork(g; link_space=χ, site_space=d)

examples/partition/partitioning.jl

@@ -5,8 +5,8 @@ using ITensorUnicodePlots
 using KaHyPar
 using Suppressor
 
-dims = (4, 4)
-g = hypercubic_lattice_graph(dims)
+system_dims = (4, 4)
+g = hypercubic_lattice_graph(system_dims)
 
 s = siteinds("S=1/2", g)
 

examples/peps.jl

@@ -15,8 +15,8 @@ function heisenberg(g::AbstractGraph)
   return os
 end
 
-dims = (3, 3)
-g = named_grid(dims)
+system_dims = (3, 3)
+g = named_grid(system_dims)
 s = siteinds("S=1/2", g)
 
 ℋ = heisenberg(g)

examples/peps/ising_tebd.jl

@@ -8,22 +8,22 @@ Random.seed!(1234)
 
 ITensors.disable_warn_order()
 
-dims = (6, 6)
-n = prod(dims)
-g = named_grid(dims)
+system_dims = (6, 6)
+n = prod(system_dims)
+g = named_grid(system_dims)
 
 h = 2.0
 
 @show h
-@show dims
+@show system_dims
 
 s = siteinds("S=1/2", g)
 
 #
 # DMRG comparison
 #
 
-g_dmrg = rename_vertices(g, cartesian_to_linear(dims))
+g_dmrg = rename_vertices(g, cartesian_to_linear(system_dims))
 ℋ_dmrg = ising(g_dmrg; h)
 s_dmrg = [only(s[v]) for v in vertices(s)]
 H_dmrg = MPO(ℋ_dmrg, s_dmrg)
@@ -33,7 +33,7 @@ E_dmrg, ψ_dmrg = dmrg(
   H_dmrg, ψ_dmrg_init; nsweeps=20, maxdim=[fill(10, 10); 20], cutoff=1e-8
 )
 @show E_dmrg
-Z_dmrg = reshape(expect(ψ_dmrg, "Z"), dims)
+Z_dmrg = reshape(expect(ψ_dmrg, "Z"), system_dims)
 
 display(Z_dmrg)
 display(heatmap(Z_dmrg))
@@ -59,7 +59,7 @@ println("maxdim = $χ")
 @show ortho
 
 # Contraction sequence for exactly computing expectation values
-inner_sequence = reduce((x, y) -> [x, y], vec(Tuple.(CartesianIndices(dims))))
+inner_sequence = reduce((x, y) -> [x, y], vec(Tuple.(CartesianIndices(system_dims))))
 
 println("\nFirst run TEBD without orthogonalization")
 ψ = @time tebd(
@@ -81,6 +81,6 @@ E = @time expect(ℋ, ψ; sequence=inner_sequence)
 
 println("\nMeasure magnetization")
 Z_dict = @time expect("Z", ψ; sequence=inner_sequence)
-Z = [Z_dict[Tuple(I)] for I in CartesianIndices(dims)]
+Z = [Z_dict[Tuple(I)] for I in CartesianIndices(system_dims)]
 display(Z)
 display(heatmap(Z))

src/ITensorNetworks.jl

@@ -24,7 +24,7 @@ using NamedGraphs:
   vertex_to_parent_vertex,
   parent_vertices_to_vertices,
   not_implemented
-using DataGraphs: edge_data_type, vertex_data_type
+using DataGraphs: IsUnderlyingGraph, edge_data_type, vertex_data_type
 
 include("imports.jl")
 

src/abstractitensornetwork.jl

@@ -273,7 +273,8 @@ end
 # `Graphs.merge_vertices!` (https://github.com/mtfishman/ITensorNetworks.jl/issues/12)
 function contract(tn::AbstractITensorNetwork, edge::AbstractEdge; merged_vertex=dst(edge))
   V = promote_type(vertextype(tn), typeof(merged_vertex))
-  tn = ITensorNetwork{V}(tn)
+  # TODO: Check `ITensorNetwork{V}`, shouldn't need a copy here.
+  tn = ITensorNetwork{V}(copy(tn))
   neighbors_src = setdiff(neighbors(tn, src(edge)), [dst(edge)])
   neighbors_dst = setdiff(neighbors(tn, dst(edge)), [src(edge)])
   new_itensor = tn[src(edge)] * tn[dst(edge)]
@@ -366,7 +367,9 @@ function factorize(
 )
   # Promote vertex type
   V = promote_type(vertextype(tn), typeof(X_vertex), typeof(Y_vertex))
-  tn = ITensorNetwork{V}(tn)
+
+  # TODO: Check `ITensorNetwork{V}`, shouldn't need a copy here.
+  tn = ITensorNetwork{V}(copy(tn))
 
   neighbors_X = setdiff(neighbors(tn, src(edge)), [dst(edge)])
   left_inds = uniqueinds(tn, edge)
@@ -391,6 +394,14 @@ function factorize(
   return tn
 end
 
+function factorize(
+  tn::AbstractITensorNetwork,
+  edge::Pair;
+  kwargs...,
+)
+  return factorize(tn, edgetype(tn)(edge); kwargs...)
+end
+
 # For ambiguity error
 function _orthogonalize_edge(tn::AbstractITensorNetwork, edge::AbstractEdge; kwargs...)
   tn = factorize(tn, edge; kwargs...)