Formatting

examples/apply/apply_bp/apply_bp.jl

@@ -122,7 +122,11 @@ function simple_update_vidal(os, ψ::ITensorNetwork; maxdim, regauge=false)
       println("regauge")
       ψ_symmetric, pψψ_symmetric, mts = vidal_to_symmetric_gauge(ψ, bond_tensors)
       mts = belief_propagation(
-        pψψ_symmetric, mts; contract_kwargs=(; alg="exact"), niters=50, target_precision=1e-5
+        pψψ_symmetric,
+        mts;
+        contract_kwargs=(; alg="exact"),
+        niters=50,
+        target_precision=1e-5,
       )
       ψ, bond_tensors = vidal_gauge(ψ_symmetric, pψψ_symmetric, mts)
     end

examples/apply/apply_bp/apply_bp_run.jl

@@ -8,7 +8,7 @@ opname = "RandomUnitary"
 # graph = named_comb_tree
 graph = named_grid
 
-dims = (6,6)
+dims = (6, 6)
 
 ψ_bp, pψψ_bp, mts_bp, ψ_vidal, pψψ_vidal, mts_vidal = main(;
   seed=1234,
@@ -35,15 +35,15 @@ mts_bp = belief_propagation(
   contract_kwargs=(; alg="exact"),
   niters=50,
   target_precision=1e-7,
-  verbose = true
+  verbose=true,
 )
 mts_vidal = belief_propagation(
   pψψ_vidal,
   mts_vidal;
   contract_kwargs=(; alg="exact"),
   niters=50,
   target_precision=1e-7,
-  verbose = true
+  verbose=true,
 )
 
 sz_bp = @show expect_bp("Sz", v, ψ_bp, pψψ_bp, mts_bp)

examples/belief_propagation/bpexample.jl

@@ -7,10 +7,7 @@ using SplitApplyCombine
 using NamedGraphs
 
 using ITensorNetworks:
-  belief_propagation,
-  approx_network_region,
-  contract_inner,
-  message_tensors
+  belief_propagation, approx_network_region, contract_inner, message_tensors
 
 function main()
   n = 4
@@ -31,9 +28,12 @@ function main()
 
   #Now do Simple Belief Propagation to Measure Sz on Site v
   pψψ = PartitionedGraph(ψψ, collect(values(group(v -> v[1], vertices(ψψ)))))
-  mts = belief_propagation(pψψ; contract_kwargs=(; alg="exact"), verbose = true, niters = 10, target_precision = 1e-3)
+  mts = belief_propagation(
+    pψψ; contract_kwargs=(; alg="exact"), verbose=true, niters=10, target_precision=1e-3
+  )
   numerator_tensors = approx_network_region(
-    pψψ, mts, [(v, 1)]; verts_tensors=[apply(op("Sz", s[v]), ψ[v])])
+    pψψ, mts, [(v, 1)]; verts_tensors=[apply(op("Sz", s[v]), ψ[v])]
+  )
   denominator_tensors = approx_network_region(pψψ, mts, [(v, 1)])
   sz_bp = contract(numerator_tensors)[] / contract(denominator_tensors)[]
 
@@ -43,9 +43,12 @@ function main()
 
   #Now do Column-wise General Belief Propagation to Measure Sz on Site v
   pψψ = PartitionedGraph(ψψ, collect(values(group(v -> v[1][1], vertices(ψψ)))))
-  mts = belief_propagation(pψψ; contract_kwargs=(; alg="exact"), verbose = true, niters = 10, target_precision = 1e-3)
+  mts = belief_propagation(
+    pψψ; contract_kwargs=(; alg="exact"), verbose=true, niters=10, target_precision=1e-3
+  )
   numerator_tensors = approx_network_region(
-    pψψ, mts, [(v, 1)]; verts_tensors=[apply(op("Sz", s[v]), ψ[v])])
+    pψψ, mts, [(v, 1)]; verts_tensors=[apply(op("Sz", s[v]), ψ[v])]
+  )
   denominator_tensors = approx_network_region(pψψ, mts, [(v, 1)])
   sz_gen_bp = contract(numerator_tensors)[] / contract(denominator_tensors)[]
 
@@ -64,14 +67,25 @@ function main()
   ψOψ = combine_linkinds(ψOψ, combiners)
 
   pψψ = PartitionedGraph(ψψ, collect(values(group(v -> v[1], vertices(ψψ)))))
-  mts = belief_propagation(pψψ; itensor_constructor=inds_e -> ITensor[dense(delta(i)) for i in inds_e], contract_kwargs=(;alg="density_matrix",output_structure=path_graph_structure,maxdim = 8,contraction_sequence_alg="optimal"))
-  numerator_tensors = approx_network_region(
-    pψψ, mts, [v]; verts_tensors=[ψOψ[v]])
+  mts = belief_propagation(
+    pψψ;
+    itensor_constructor=inds_e -> ITensor[dense(delta(i)) for i in inds_e],
+    contract_kwargs=(;
+      alg="density_matrix",
+      output_structure=path_graph_structure,
+      maxdim=8,
+      contraction_sequence_alg="optimal",
+    ),
+  )
+  numerator_tensors = approx_network_region(pψψ, mts, [v]; verts_tensors=[ψOψ[v]])
   denominator_tensors = approx_network_region(pψψ, mts, [v])
   sz_MPS_bp = contract(numerator_tensors)[] / contract(denominator_tensors)[]
 
   println(
-    "Column-Wise MPS Belief Propagation Gives Sz on Site " * string(v) * " as " * string(sz_gen_bp)
+    "Column-Wise MPS Belief Propagation Gives Sz on Site " *
+    string(v) *
+    " as " *
+    string(sz_gen_bp),
   )
 
   #Now do it exactly

examples/belief_propagation/bpsequences.jl

@@ -8,11 +8,7 @@ using Graphs
 using NamedGraphs
 
 using ITensorNetworks:
-  belief_propagation,
-  approx_network_region,
-  contract_inner,
-  message_tensors,
-  edge_sequence
+  belief_propagation, approx_network_region, contract_inner, message_tensors, edge_sequence
 
 function main()
   g_labels = [
@@ -51,7 +47,9 @@ function main()
       contract_kwargs=(; alg="exact"),
       target_precision=1e-10,
       niters=100,
-      edges=[PartitionEdge.(e) for e in edge_sequence(partitioned_graph(pψψ); alg="parallel")],
+      edges=[
+        PartitionEdge.(e) for e in edge_sequence(partitioned_graph(pψψ); alg="parallel")
+      ],
       verbose=true,
     )
     print("Sequential updates (sequence is default edge list of the message tensors): ")
@@ -61,7 +59,10 @@ function main()
       contract_kwargs=(; alg="exact"),
       target_precision=1e-10,
       niters=100,
-      edges=PartitionEdge.([e for e in vcat(edges(partitioned_graph(pψψ)), reverse.(edges(partitioned_graph(pψψ))))]),
+      edges=PartitionEdge.([
+        e for
+        e in vcat(edges(partitioned_graph(pψψ)), reverse.(edges(partitioned_graph(pψψ))))
+      ]),
       verbose=true,
     )
     print("Sequential updates (sequence is our custom sequence finder): ")

examples/belief_propagation/sqrt_bp.jl

@@ -36,7 +36,8 @@ function main(; n, niters, network="ising", β=nothing, h=nothing, χ=nothing)
 
   mts = @time belief_propagation(pψψ; niters, contract_kwargs=(; alg="exact"))
   numerator_network = approx_network_region(
-    pψψ, mts, [(v, 1)]; verts_tensors=ITensor[apply(op("Sz", s[v]), ψ[v])])
+    pψψ, mts, [(v, 1)]; verts_tensors=ITensor[apply(op("Sz", s[v]), ψ[v])]
+  )
   denominator_network = approx_network_region(pψψ, mts, [(v, 1)])
   sz_bp =
     ITensors.contract(
@@ -52,7 +53,8 @@ function main(; n, niters, network="ising", β=nothing, h=nothing, χ=nothing)
   mts_sqrt = @time sqrt_belief_propagation(pψψ; niters)
 
   numerator_network = approx_network_region(
-    pψψ, mts_sqrt, [(v, 1)]; verts_tensors=ITensor[apply(op("Sz", s[v]), ψ[v])])
+    pψψ, mts_sqrt, [(v, 1)]; verts_tensors=ITensor[apply(op("Sz", s[v]), ψ[v])]
+  )
   denominator_network = approx_network_region(pψψ, mts_sqrt, [(v, 1)])
   sz_sqrt_bp =
     contract(

examples/boundary.jl

@@ -8,9 +8,10 @@ tn = ITensorNetwork(named_grid((6, 3)); link_space=4)
 
 @visualize tn
 
-
-ptn = PartitionedGraph(tn, NamedGraphs.partition_vertices(underlying_graph(tn); nvertices_per_partition = 2))
-sub_vs_1, sub_vs_2 =vertices(ptn, PartitionVertex(1)), vertices(ptn, PartitionVertex(2))
+ptn = PartitionedGraph(
+  tn, NamedGraphs.partition_vertices(underlying_graph(tn); nvertices_per_partition=2)
+)
+sub_vs_1, sub_vs_2 = vertices(ptn, PartitionVertex(1)), vertices(ptn, PartitionVertex(2))
 
 @show (1, 1) ∈ sub_vs_1
 @show (6, 3) ∈ sub_vs_2

examples/dynamics/heavy_hex_ising_real_tebd.jl

@@ -37,17 +37,16 @@ hummingbird_processor_graph() = ibm_processor_graph(2, 4)
 osprey_processor_graph() = ibm_processor_graph(6, 12)
 
 """Take the expectation value of o on an ITN using belief propagation"""
-function expect_state_SBP(
-  o::ITensor, ψ::AbstractITensorNetwork, pψψ::PartitionedGraph, mts
-)
+function expect_state_SBP(o::ITensor, ψ::AbstractITensorNetwork, pψψ::PartitionedGraph, mts)
   Oψ = apply(o, ψ; cutoff=1e-16)
   ψ = copy(ψ)
   s = siteinds(ψ)
   vs = vertices(s)[findall(i -> (length(commoninds(s[i], inds(o))) != 0), vertices(s))]
   vs_braket = [(v, 1) for v in vs]
 
   numerator_tensors = approx_network_region(
-    pψψ, mts, vs_braket; verts_tensors=ITensor[Oψ[v] for v in vs])
+    pψψ, mts, vs_braket; verts_tensors=ITensor[Oψ[v] for v in vs]
+  )
   denominator_tensors = approx_network_region(pψψ, mts, vs_braket)
   num_seq = contraction_sequence(numerator_tensors; alg="optimal")
   den_seq = contraction_sequence(denominator_tensors; alg="optimal")

examples/gauging/gauging_itns.jl

@@ -22,7 +22,9 @@ using NamedGraphs: add_edges!, rem_vertex!, hexagonal_lattice_graph
 using Graphs
 
 """Eager Gauging"""
-function eager_gauging(ψ::ITensorNetwork, pψψ::PartitionedGraph, bond_tensors::DataGraph, mts)
+function eager_gauging(
+  ψ::ITensorNetwork, pψψ::PartitionedGraph, bond_tensors::DataGraph, mts
+)
   isometries = vidal_itn_isometries(ψ, bond_tensors)
 
   ψ = copy(ψ)
@@ -32,7 +34,9 @@ function eager_gauging(ψ::ITensorNetwork, pψψ::PartitionedGraph, bond_tensors
     pe = NamedGraphs.partition_edge(pψψ, NamedEdge((vsrc, 1) => (vdst, 1)))
     normalize!(isometries[e])
     normalize!(isometries[reverse(e)])
-    mts[pe], mts[PartitionEdge(reverse(NamedGraphs.parent(pe)))] = ITensorNetwork(isometries[e]),
+    mts[pe], mts[PartitionEdge(reverse(NamedGraphs.parent(pe)))] = ITensorNetwork(
+      isometries[e]
+    ),
     ITensorNetwork(isometries[reverse(e)])
   end
 
@@ -70,12 +74,20 @@ function benchmark_state_gauging(
         )
       else
         times_iters[i] = @elapsed mts, _ = belief_propagation_iteration(
-          pψψ, mts; contract_kwargs=(; alg="exact"), edges=[PartitionEdge.(e) for e in edge_sequence(partitioned_graph(pψψ); alg="parallel")])
+          pψψ,
+          mts;
+          contract_kwargs=(; alg="exact"),
+          edges=[
+            PartitionEdge.(e) for e in edge_sequence(partitioned_graph(pψψ); alg="parallel")
+          ],
+        )
       end
 
       times_gauging[i] = @elapsed ψ, bond_tensors = vidal_gauge(ψinit, pψψ, mts)
     elseif mode == "eager"
-      times_iters[i] = @elapsed ψ, bond_tensors, mts = eager_gauging(ψ, pψψ, bond_tensors, mts)
+      times_iters[i] = @elapsed ψ, bond_tensors, mts = eager_gauging(
+        ψ, pψψ, bond_tensors, mts
+      )
     else
       times_iters[i] = @elapsed begin
         for e in edges(ψ)

src/approx_itensornetwork/partition.jl

@@ -75,19 +75,18 @@ function findfirst_on_edges(f::Function, graph::AbstractDataGraph)
 end
 
 function subgraphs(g::AbstractSimpleGraph, subgraph_vertices)
-    return subgraphs(NamedGraph(g), subgraph_vertices)
+  return subgraphs(NamedGraph(g), subgraph_vertices)
 end
-  
-  """
-      subgraphs(g::AbstractGraph, subgraph_vertices)
-  
-  Return a collection of subgraphs of `g` defined by the subgraph
-  vertices `subgraph_vertices`.
-  """
+
+"""
+    subgraphs(g::AbstractGraph, subgraph_vertices)
+
+Return a collection of subgraphs of `g` defined by the subgraph
+vertices `subgraph_vertices`.
+"""
 function subgraphs(g::AbstractGraph, subgraph_vertices)
-    return map(vs -> subgraph(g, vs), subgraph_vertices)
+  return map(vs -> subgraph(g, vs), subgraph_vertices)
 end
-
 
 """
     subgraphs(g::AbstractGraph; npartitions::Integer, kwargs...)
@@ -195,4 +194,4 @@ function _commoninds(partition::DataGraph)
   outinds = noncommoninds(network...)
   allinds = mapreduce(t -> [i for i in inds(t)], vcat, network)
   return Vector(setdiff(allinds, outinds))
-end
+end

src/beliefpropagation/beliefpropagation.jl

@@ -1,6 +1,5 @@
 function message_tensors(
-  ptn::PartitionedGraph;
-  itensor_constructor=inds_e -> ITensor[dense(delta(inds_e))]
+  ptn::PartitionedGraph; itensor_constructor=inds_e -> ITensor[dense(delta(inds_e))]
 )
   mts = Dict()
   for e in PartitionEdge.(edges(partitioned_graph(ptn)))
@@ -23,19 +22,27 @@ function update_message_tensor(
   contract_kwargs=(; alg="density_matrix", output_structure=path_graph_structure, maxdim=1),
 )
   incoming_messages = [
-    mts[PartitionEdge(e_in)] for e_in in setdiff(boundary_edges(partitioned_graph(ptn), [NamedGraphs.parent(src(edge))]; dir=:in), [reverse(NamedGraphs.parent(edge))])
+    mts[PartitionEdge(e_in)] for e_in in setdiff(
+      boundary_edges(partitioned_graph(ptn), [NamedGraphs.parent(src(edge))]; dir=:in),
+      [reverse(NamedGraphs.parent(edge))],
+    )
   ]
   incoming_messages = reduce(vcat, incoming_messages; init=ITensor[])
 
-  contract_list = ITensor[incoming_messages; ITensor(unpartitioned_graph(subgraph(ptn, [src(edge)])))]
+  contract_list = ITensor[
+    incoming_messages
+    ITensor(unpartitioned_graph(subgraph(ptn, [src(edge)])))
+  ]
 
   if contract_kwargs.alg != "exact"
     mt, _ = contract(ITensorNetwork(contract_list); contract_kwargs...)
   else
-    mt = contract(contract_list; sequence=contraction_sequence(contract_list; alg="optimal"))
+    mt = contract(
+      contract_list; sequence=contraction_sequence(contract_list; alg="optimal")
+    )
   end
 
-  if isa(mt, ITensor) 
+  if isa(mt, ITensor)
     mt = ITensor[mt]
   elseif isa(mt, ITensorNetwork)
     mt = ITensor(mt)
@@ -61,8 +68,7 @@ function belief_propagation_iteration(
     new_mts[e] = update_message_tensor(ptn, e, new_mts; contract_kwargs)
 
     if compute_norm
-      LHS, RHS = ITensors.contract(mts[e]),
-      ITensors.contract(new_mts[e])
+      LHS, RHS = ITensors.contract(mts[e]), ITensors.contract(new_mts[e])
       #This line only makes sense if the message tensors are rank 2??? Should fix this.
       LHS /= sum(diag(LHS))
       RHS /= sum(diag(RHS))
@@ -133,7 +139,11 @@ function belief_propagation(
   return mts
 end
 
-function belief_propagation(ptn::PartitionedGraph; itensor_constructor=inds_e -> ITensor[dense(delta(inds_e))], kwargs...)
+function belief_propagation(
+  ptn::PartitionedGraph;
+  itensor_constructor=inds_e -> ITensor[dense(delta(inds_e))],
+  kwargs...,
+)
   mts = message_tensors(ptn; itensor_constructor)
   return belief_propagation(ptn, mts; kwargs...)
 end
@@ -148,10 +158,15 @@ function get_environment(ptn::PartitionedGraph, mts, verts::Vector; dir=:in)
     return get_environment(ptn, mts, setdiff(vertices(ptn), verts))
   end
 
-  env_tensors = [mts[PartitionEdge(e)] for e in boundary_edges(partitioned_graph(ptn), NamedGraphs.parent.(partition_vertices); dir=:in)]
-  env_tensors = reduce(vcat, env_tensors; init = ITensor[])
-  central_tensors = ITensor[(unpartitioned_graph(ptn))[v] for v in setdiff(vertices(ptn, partition_vertices), verts)]
-
+  env_tensors = [
+    mts[PartitionEdge(e)] for e in
+    boundary_edges(partitioned_graph(ptn), NamedGraphs.parent.(partition_vertices); dir=:in)
+  ]
+  env_tensors = reduce(vcat, env_tensors; init=ITensor[])
+  central_tensors = ITensor[
+    (unpartitioned_graph(ptn))[v] for v in setdiff(vertices(ptn, partition_vertices), verts)
+  ]
+
   return vcat(env_tensors, central_tensors)
 end