Further changes

examples/gauging/gauging_itns.jl

@@ -22,6 +22,7 @@ using ITensorNetworks:
 using NamedGraphs
 using NamedGraphs: add_edges!, rem_vertex!, hexagonal_lattice_graph
 using Graphs
+using MKL
 
 """Eager Gauging"""
 function eager_gauging(ψ::ITensorNetwork, bond_tensors::DataGraph, mts::DataGraph)
@@ -85,7 +86,7 @@ function benchmark_state_gauging(
 
     C[i] = vidal_itn_canonicalness(ψ, bond_tensors)
   end
-
+  @show times_iters, time
   simulation_times = cumsum(times_iters) + times_gauging
 
   return simulation_times, C

src/beliefpropagation/beliefpropagation.jl

@@ -22,12 +22,13 @@ function message_tensors_skeleton(subgraphs::DataGraph)
 end
 
 function message_tensors(
-  subgraphs::DataGraph; itensor_constructor=inds_e -> dense(delta(inds_e))
+  subgraphs::DataGraph; itensor_constructor=x -> ITensor[dense(delta(i)) for i in x]
 )
   mts = message_tensors_skeleton(subgraphs)
   for e in edges(subgraphs)
-    inds_e = commoninds(subgraphs[src(e)], subgraphs[dst(e)])
-    mts[e] = ITensorNetwork(map(itensor_constructor, inds_e))
+    inds_e = [i for i in commoninds(subgraphs[src(e)], subgraphs[dst(e)])]
+    itensors = itensor_constructor(inds_e)
+    mts[e] = ITensorNetwork(itensors)
     mts[reverse(e)] = dag(mts[e])
   end
   return mts
@@ -118,7 +119,7 @@ function belief_propagation(
   contract_kwargs=(; alg="density_matrix", output_structure=path_graph_structure, maxdim=1),
   niters=20,
   target_precision::Union{Float64,Nothing}=nothing,
-  update_sequence::String="parallel",
+  update_sequence::String="sequential",
   edges = edge_update_order(undirected_graph(underlying_graph(mts)))
 )
   compute_norm = target_precision == nothing ? false : true
@@ -143,10 +144,11 @@ function belief_propagation(
   npartitions=nothing,
   subgraph_vertices=nothing,
   niters=20,
+  update_sequence::String="sequential",
   target_precision::Union{Float64,Nothing}=nothing,
 )
   mts = message_tensors(tn; nvertices_per_partition, npartitions, subgraph_vertices)
-  return belief_propagation(tn, mts; contract_kwargs, niters, target_precision)
+  return belief_propagation(tn, mts; contract_kwargs, niters, target_precision, update_sequence)
 end
 
 """

src/beliefpropagation/sqrt_beliefpropagation.jl

@@ -5,7 +5,7 @@ function sqrt_belief_propagation(
   tn::ITensorNetwork,
   mts::DataGraph;
   niters=20,
-  update_sequence::String="parallel",
+  update_sequence::String="sequential",
   # target_precision::Union{Float64,Nothing}=nothing,
 )
   # compute_norm = target_precision == nothing ? false : true
@@ -27,8 +27,8 @@ end
 function sqrt_belief_propagation_iteration(
   tn::ITensorNetwork,
   sqrt_mts::DataGraph;
-  update_sequence::String="parallel",
-  edges=Graphs.edges(sqrt_mts),
+  update_sequence::String="sequential",
+  edges=edge_update_order(undirected_graph(underlying_graph(mts))),
 
   # compute_norm=false,
 )

src/gauging.jl

@@ -95,14 +95,15 @@ function vidal_gauge(
   regularization=10 * eps(real(scalartype(ψ))),
   niters=30,
   target_canonicalness::Union{Nothing,Float64}=nothing,
+  update_sequence = "sequential",
   svd_kwargs...,
 )
   ψψ = norm_network(ψ)
   Z = partition(ψψ; subgraph_vertices=collect(values(group(v -> v[1], vertices(ψψ)))))
   mts = message_tensors(Z)
 
   mts = belief_propagation(
-    ψψ, mts; contract_kwargs=(; alg="exact"), niters, target_precision=target_canonicalness
+    ψψ, mts; contract_kwargs=(; alg="exact"), niters, target_precision=target_canonicalness, update_sequence
   )
   return vidal_gauge(
     ψ, mts; eigen_message_tensor_cutoff, regularization, niters, svd_kwargs...

test/test_belief_propagation.jl

@@ -52,7 +52,7 @@ ITensors.disable_warn_order()
     ψψ, mts, [(v, 1)]; verts_tn=ITensorNetwork(ITensor[apply(op("Sz", s[v]), ψ[v])])
   )
   denominator_network = approx_network_region(ψψ, mts, [(v, 1)])
-  bp_sz = contract(numerator_network)[] / contract(denominator_network)[]
+  bp_sz = ITensors.contract(numerator_network)[] / ITensors.contract(denominator_network)[]
 
   @test abs.(bp_sz - exact_sz) <= 1e-14
 
@@ -85,7 +85,7 @@ ITensors.disable_warn_order()
     ψψ, mts, [(v, 1)]; verts_tn=ITensorNetwork(ITensor[apply(op("Sz", s[v]), ψ[v])])
   )
   denominator_network = approx_network_region(ψψ, mts, [(v, 1)])
-  bp_sz = contract(numerator_network)[] / contract(denominator_network)[]
+  bp_sz = ITensors.contract(numerator_network)[] / ITensors.contract(denominator_network)[]
 
   @test abs.(bp_sz - exact_sz) <= 1e-14
 
@@ -112,7 +112,7 @@ ITensors.disable_warn_order()
   )
 
   denominator_network = approx_network_region(ψψ, mts, vs)
-  bp_szsz = contract(numerator_network)[] / contract(denominator_network)[]
+  bp_szsz = ITensors.contract(numerator_network)[] / ITensors.contract(denominator_network)[]
 
   @test abs.(bp_szsz - actual_szsz) <= 0.05
 
@@ -134,7 +134,7 @@ ITensors.disable_warn_order()
   mts = belief_propagation(ψψ, mts; contract_kwargs=(; alg="exact"))
 
   ψψsplit = split_index(ψψ, NamedEdge.([(v, 1) => (v, 2) for v in vs]))
-  rdm = contract(
+  rdm = ITensors.contract(
     approx_network_region(
       ψψ,
       mts,
@@ -183,7 +183,7 @@ ITensors.disable_warn_order()
   numerator_network = approx_network_region(ψψ, mts, [v]; verts_tn=ITensorNetwork(ψOψ[v]))
 
   denominator_network = approx_network_region(ψψ, mts, [v])
-  bp_sz = contract(numerator_network)[] / contract(denominator_network)[]
+  bp_sz = ITensors.contract(numerator_network)[] / ITensors.contract(denominator_network)[]
 
   exact_sz =
     contract_boundary_mps(ψOψ; cutoff=1e-16) / contract_boundary_mps(ψψ; cutoff=1e-16)

test/test_gauging.jl

@@ -21,7 +21,7 @@ using SplitApplyCombine
   dims = (n, n)
   g = named_grid(dims)
   s = siteinds("S=1/2", g)
-  χ = 3
+  χ = 6
 
   Random.seed!(5467)
   ψ = randomITensorNetwork(s; link_space=χ)