Merge.

Project.toml

@@ -1,7 +1,7 @@
 name = "ITensorNetworks"
 uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
 authors = ["Matthew Fishman <[email protected]> and contributors"]
-version = "0.7.0"
+version = "0.8.0"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
@@ -19,6 +19,7 @@ IsApprox = "28f27b66-4bd8-47e7-9110-e2746eb8bed7"
 IterTools = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
 KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+NDTensors = "23ae76d9-e61a-49c4-8f12-3f1a16adf9cf"
 NamedGraphs = "678767b0-92e7-4007-89e4-4527a8725b19"
 Observers = "338f10d5-c7f1-4033-a7d1-f9dec39bcaa0"
 PackageExtensionCompat = "65ce6f38-6b18-4e1d-a461-8949797d7930"
@@ -58,6 +59,7 @@ IsApprox = "0.1"
 IterTools = "1.4.0"
 KrylovKit = "0.6, 0.7"
 NamedGraphs = "0.1.23"
+NDTensors = "0.2, 0.3"
 Observers = "0.2"
 PackageExtensionCompat = "1"
 Requires = "1.3"

README.md

@@ -39,7 +39,7 @@ julia> using ITensorNetworks: ITensorNetwork, siteinds
 julia> using NamedGraphs: named_grid, subgraph
 
 julia> tn = ITensorNetwork(named_grid(4); link_space=2)
-ITensorNetwork{Int64} with 4 vertices:
+ITensorNetworks.ITensorNetwork{Int64} with 4 vertices:
 4-element Vector{Int64}:
  1
  2
@@ -90,7 +90,7 @@ and here is a similar example for making a tensor network on a grid (a tensor pr
 
 ```julia
 julia> tn = ITensorNetwork(named_grid((2, 2)); link_space=2)
-ITensorNetwork{Tuple{Int64, Int64}} with 4 vertices:
+ITensorNetworks.ITensorNetwork{Tuple{Int64, Int64}} with 4 vertices:
 4-element Vector{Tuple{Int64, Int64}}:
  (1, 1)
  (2, 1)
@@ -125,7 +125,7 @@ julia> neighbors(tn, (1, 2))
  (2, 2)
 
 julia> tn_1 = subgraph(v -> v[1] == 1, tn)
-ITensorNetwork{Tuple{Int64, Int64}} with 2 vertices:
+ITensorNetworks.ITensorNetwork{Tuple{Int64, Int64}} with 2 vertices:
 2-element Vector{Tuple{Int64, Int64}}:
  (1, 1)
  (1, 2)
@@ -139,7 +139,7 @@ with vertex data:
  (1, 2) │ ((dim=2|id=723|"1×1,1×2"), (dim=2|id=712|"1×2,2×2"))
 
 julia> tn_2 = subgraph(v -> v[1] == 2, tn)
-ITensorNetwork{Tuple{Int64, Int64}} with 2 vertices:
+ITensorNetworks.ITensorNetwork{Tuple{Int64, Int64}} with 2 vertices:
 2-element Vector{Tuple{Int64, Int64}}:
  (2, 1)
  (2, 2)
@@ -184,7 +184,7 @@ and edge data:
 0-element Dictionaries.Dictionary{NamedGraphs.NamedEdge{Int64}, Vector{ITensors.Index}}
 
 julia> tn1 = ITensorNetwork(s; link_space=2)
-ITensorNetwork{Int64} with 3 vertices:
+ITensorNetworks.ITensorNetwork{Int64} with 3 vertices:
 3-element Vector{Int64}:
  1
  2
@@ -201,7 +201,7 @@ with vertex data:
  3 │ ((dim=2|id=656|"S=1/2,Site,n=3"), (dim=2|id=190|"2,3"))
 
 julia> tn2 = ITensorNetwork(s; link_space=2)
-ITensorNetwork{Int64} with 3 vertices:
+ITensorNetworks.ITensorNetwork{Int64} with 3 vertices:
 3-element Vector{Int64}:
  1
  2

src/ITensorNetworks.jl

@@ -36,7 +36,6 @@ include("caches/beliefpropagationcache.jl")
 include("contraction_tree_to_graph.jl")
 include("gauging.jl")
 include("utils.jl")
-include("tensornetworkoperators.jl")
 include("ITensorsExt/itensorutils.jl")
 include("solvers/local_solvers/eigsolve.jl")
 include("solvers/local_solvers/exponentiate.jl")

src/abstractitensornetwork.jl

@@ -23,7 +23,6 @@ using ITensors:
   commoninds,
   commontags,
   contract,
-  convert_eltype,
   dag,
   hascommoninds,
   noprime,
@@ -39,18 +38,18 @@ using ITensors:
   swaptags
 using ITensors.ITensorMPS: ITensorMPS, add, linkdim, linkinds, siteinds
 using ITensors.ITensorVisualizationCore: ITensorVisualizationCore, visualize
-using ITensors.NDTensors: NDTensors, dim
-using LinearAlgebra: LinearAlgebra
+using LinearAlgebra: LinearAlgebra, factorize
 using NamedGraphs:
   NamedGraphs,
   NamedGraph,
   ⊔,
+  directed_graph,
   incident_edges,
   not_implemented,
   rename_vertices,
   vertex_to_parent_vertex,
   vertextype
-using NamedGraphs: directed_graph
+using NDTensors: NDTensors, dim
 using SplitApplyCombine: flatten
 
 abstract type AbstractITensorNetwork{V} <: AbstractDataGraph{V,ITensor,ITensor} end
@@ -174,41 +173,26 @@ function Base.Vector{ITensor}(tn::AbstractITensorNetwork)
 end
 
 # Convenience wrapper
-# TODO: Delete this and just use `Vector{ITensor}`, or maybe
-# it should output a dictionary or be called `eachtensor`?
-itensors(tn::AbstractITensorNetwork) = Vector{ITensor}(tn)
+function tensors(tn::AbstractITensorNetwork, vertices=vertices(tn))
+  return map(v -> tn[v], Indices(vertices))
+end
 
 #
 # Promotion and conversion
 #
 
-function LinearAlgebra.promote_leaf_eltypes(tn::AbstractITensorNetwork)
-  return LinearAlgebra.promote_leaf_eltypes(itensors(tn))
-end
-
 function promote_indtypeof(tn::AbstractITensorNetwork)
-  return mapreduce(promote_indtype, vertices(tn)) do v
-    return indtype(tn[v])
+  return mapreduce(promote_indtype, tensors(tn)) do t
+    return indtype(t)
   end
 end
 
-# TODO: Delete in favor of `scalartype`.
-function ITensors.promote_itensor_eltype(tn::AbstractITensorNetwork)
-  return LinearAlgebra.promote_leaf_eltypes(tn)
+function NDTensors.scalartype(tn::AbstractITensorNetwork)
+  return mapreduce(eltype, promote_type, tensors(tn); init=Bool)
 end
 
-NDTensors.scalartype(tn::AbstractITensorNetwork) = LinearAlgebra.promote_leaf_eltypes(tn)
-
-# TODO: eltype(::AbstractITensorNetwork) (cannot behave the same as eltype(::ITensors.AbstractMPS))
-
-# TODO: mimic ITensors.AbstractMPS implementation using map
-function ITensors.convert_leaf_eltype(eltype::Type, tn::AbstractITensorNetwork)
-  tn = copy(tn)
-  vertex_data(tn) .= convert_eltype.(Ref(eltype), vertex_data(tn))
-  return tn
-end
+# TODO: Define `eltype(::AbstractITensorNetwork)` as `ITensor`?
 
-# TODO: Mimic ITensors.AbstractMPS implementation using map
 # TODO: Implement using `adapt`
 function NDTensors.convert_scalartype(eltype::Type{<:Number}, tn::AbstractITensorNetwork)
   tn = copy(tn)
@@ -217,7 +201,7 @@ function NDTensors.convert_scalartype(eltype::Type{<:Number}, tn::AbstractITenso
 end
 
 function Base.complex(tn::AbstractITensorNetwork)
-  return NDTensors.convert_scalartype(complex(LinearAlgebra.promote_leaf_eltypes(tn)), tn)
+  return NDTensors.convert_scalartype(complex(scalartype(tn)), tn)
 end
 
 #
@@ -251,54 +235,45 @@ end
 # Alias
 indsnetwork(tn::AbstractITensorNetwork) = IndsNetwork(tn)
 
-function external_indsnetwork(tn::AbstractITensorNetwork)
+# TODO: Output a `VertexDataGraph`? Unfortunately
+# `IndsNetwork` doesn't allow iterating over vertex data.
+function ITensorMPS.siteinds(tn::AbstractITensorNetwork)
   is = IndsNetwork(underlying_graph(tn))
   for v in vertices(tn)
     is[v] = uniqueinds(tn, v)
   end
   return is
 end
 
-# For backwards compatibility
-# TODO: Delete this
-ITensorMPS.siteinds(tn::AbstractITensorNetwork) = external_indsnetwork(tn)
-
-# External indsnetwork of the flattened network, with vertices
-# mapped back to `tn1`.
-function flatten_external_indsnetwork(
-  tn1::AbstractITensorNetwork, tn2::AbstractITensorNetwork
-)
-  is = external_indsnetwork(sim(tn1; sites=[]) ⊗ tn2)
-  flattened_is = IndsNetwork(underlying_graph(tn1))
-  for v in vertices(flattened_is)
-    # setindex_preserve_graph!(flattened_is, unioninds(is[v, 1], is[v, 2]), v)
-    flattened_is[v] = unioninds(is[v, 1], is[v, 2])
-  end
-  return flattened_is
+function flatten_siteinds(tn::AbstractITensorNetwork)
+  # reduce(noncommoninds, tensors(tn))
+  return unique(flatten([uniqueinds(tn, v) for v in vertices(tn)]))
 end
 
-function internal_indsnetwork(tn::AbstractITensorNetwork)
+function ITensorMPS.linkinds(tn::AbstractITensorNetwork)
   is = IndsNetwork(underlying_graph(tn))
   for e in edges(tn)
     is[e] = commoninds(tn, e)
   end
   return is
 end
 
-# For backwards compatibility
-# TODO: Delete this
-ITensorMPS.linkinds(tn::AbstractITensorNetwork) = internal_indsnetwork(tn)
+function flatten_linkinds(tn::AbstractITensorNetwork)
+  return unique(flatten([commoninds(tn, e) for e in edges(tn)]))
+end
 
 #
 # Index access
 #
 
-function neighbor_itensors(tn::AbstractITensorNetwork, vertex)
-  return [tn[vn] for vn in neighbors(tn, vertex)]
+function neighbor_tensors(tn::AbstractITensorNetwork, vertex)
+  return tensors(tn, neighbors(tn, vertex))
 end
 
 function ITensors.uniqueinds(tn::AbstractITensorNetwork, vertex)
-  return uniqueinds(tn[vertex], neighbor_itensors(tn, vertex)...)
+  # TODO: Splatting here isn't good, make a version that works for
+  # collections of ITensors.
+  return reduce(uniqueinds, Iterators.flatten(([tn[vertex]], neighbor_tensors(tn, vertex))))
 end
 
 function ITensors.uniqueinds(tn::AbstractITensorNetwork, edge::AbstractEdge)
@@ -322,14 +297,6 @@ function ITensorMPS.linkinds(tn::AbstractITensorNetwork, edge)
   return commoninds(tn, edge)
 end
 
-function internalinds(tn::AbstractITensorNetwork)
-  return unique(flatten([commoninds(tn, e) for e in edges(tn)]))
-end
-
-function externalinds(tn::AbstractITensorNetwork)
-  return unique(flatten([uniqueinds(tn, v) for v in vertices(tn)]))
-end
-
 # Priming and tagging (changing Index identifiers)
 function ITensors.replaceinds(
   tn::AbstractITensorNetwork, is_is′::Pair{<:IndsNetwork,<:IndsNetwork}
@@ -439,9 +406,7 @@ function Base.isapprox(
   x::AbstractITensorNetwork,
   y::AbstractITensorNetwork;
   atol::Real=0,
-  rtol::Real=Base.rtoldefault(
-    LinearAlgebra.promote_leaf_eltypes(x), LinearAlgebra.promote_leaf_eltypes(y), atol
-  ),
+  rtol::Real=Base.rtoldefault(scalartype(x), scalartype(y), atol),
 )
   error("Not implemented")
   d = norm(x - y)

src/apply.jl

@@ -1,3 +1,4 @@
+using Graphs: has_edge
 using LinearAlgebra: qr
 using ITensors: Ops
 using ITensors:

src/approx_itensornetwork/partition.jl

@@ -1,3 +1,9 @@
+using DataGraphs: AbstractDataGraph, DataGraph, edge_data, vertex_data
+using Dictionaries: Dictionary
+using Graphs: AbstractGraph, add_edge!, has_edge, dst, edges, edgetype, src, vertices
+using ITensors: ITensor, noncommoninds
+using NamedGraphs: NamedGraph, subgraph
+
 function _partition(g::AbstractGraph, subgraph_vertices)
   partitioned_graph = DataGraph(
     NamedGraph(eachindex(subgraph_vertices)),

src/formnetworks/bilinearformnetwork.jl

@@ -57,7 +57,7 @@ function BilinearFormNetwork(
   dual_site_index_map=default_dual_site_index_map,
   kwargs...,
 )
-  @assert issetequal(externalinds(bra), externalinds(ket))
+  @assert issetequal(flatten_siteinds(bra), flatten_siteinds(ket))
   operator_inds = union_all_inds(siteinds(ket), dual_site_index_map(siteinds(ket)))
   O = ITensorNetwork(Op("I"), operator_inds)
   return BilinearFormNetwork(O, bra, ket; dual_site_index_map, kwargs...)

src/solvers/contract.jl

@@ -72,11 +72,6 @@ end
 Overload of `ITensors.apply`.
 """
 function ITensors.apply(tn1::AbstractTTN, tn2::AbstractTTN; init, kwargs...)
-  if !isone(plev_diff(flatten_external_indsnetwork(tn1, tn2), external_indsnetwork(init)))
-    error(
-      "Initial guess `init` needs to primelevel one less than the contraction tn1 and tn2."
-    )
-  end
   init = init'
   tn12 = contract(tn1, tn2; init, kwargs...)
   return replaceprime(tn12, 1 => 0)
@@ -85,24 +80,7 @@ end
 function sum_apply(
   tns::Vector{<:Tuple{<:AbstractTTN,<:AbstractTTN}}; alg="fit", init, kwargs...
 )
-  if !isone(
-    plev_diff(
-      flatten_external_indsnetwork(first(first(tns)), last(first(tns))),
-      external_indsnetwork(init),
-    ),
-  )
-    error(
-      "Initial guess `init` needs to primelevel one less than the contraction tn1 and tn2."
-    )
-  end
-
   init = init'
   tn12 = sum_contract(Algorithm(alg), tns; init, kwargs...)
   return replaceprime(tn12, 1 => 0)
 end
-
-function plev_diff(a::IndsNetwork, b::IndsNetwork)
-  pla = plev(only(a[first(vertices(a))]))
-  plb = plev(only(b[first(vertices(b))]))
-  return pla - plb
-end

src/treetensornetworks/abstracttreetensornetwork.jl

@@ -285,9 +285,7 @@ function Base.isapprox(
   x::AbstractTTN,
   y::AbstractTTN;
   atol::Real=0,
-  rtol::Real=Base.rtoldefault(
-    LinearAlgebra.promote_leaf_eltypes(x), LinearAlgebra.promote_leaf_eltypes(y), atol
-  ),
+  rtol::Real=Base.rtoldefault(scalartype(x), scalartype(y), atol),
 )
   d = norm(x - y)
   if isfinite(d)