Small Performance Improvements

Project.toml

@@ -3,7 +3,7 @@ uuid = "4de3b72a-362e-43dd-83ff-3f381eda9f9c"
 authors = ["JoeyT1994 <jtindall@flatironinstitute.org>", "MSRudolph <manuel.rudolph@web.de>","and contributors"]
 description = "A Julia package for simulating quantum circuits and dynamics with tensor networks of near-arbritrary topology."
 license = "MIT"
-version = "0.1.03"
+version = "0.1.04"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

src/Backend/abstractbeliefpropagationcache.jl

@@ -162,13 +162,12 @@ end
 """
 Do a sequential update of the message tensors on `edges`
 """
-function update_iteration(
+function update_iteration!(
         alg::Algorithm"bp",
         bpc::AbstractBeliefPropagationCache,
         edges::Vector;
         (update_diff!) = nothing,
     )
-    bpc = copy(bpc)
     for e in edges
         prev_message = !isnothing(update_diff!) ? message(bpc, e) : nothing
         update_message!(alg.kwargs.message_update_alg, bpc, e)
@@ -187,9 +186,10 @@ function update(alg::Algorithm"bp", bpc::AbstractBeliefPropagationCache)
     if isnothing(alg.kwargs.maxiter)
         error("You need to specify a number of iterations for BP!")
     end
+    bpc = copy(bpc)
     for i in 1:alg.kwargs.maxiter
         diff = compute_error ? Ref(0.0) : nothing
-        bpc = update_iteration(alg, bpc, alg.kwargs.edge_sequence; (update_diff!) = diff)
+        update_iteration!(alg, bpc, alg.kwargs.edge_sequence; (update_diff!) = diff)
         if compute_error && (diff.x / length(alg.kwargs.edge_sequence)) <= alg.kwargs.tolerance
             if alg.kwargs.verbose
                 println("BP converged to desired precision after $i iterations.")

src/Backend/beliefpropagationcache.jl

@@ -15,7 +15,8 @@ end
 
 #TODO: Take `dot` without precontracting the messages to allow scaling to more complex messages
 function message_diff(message_a::ITensor, message_b::ITensor)
-    f = abs2(dot((message_a / norm(message_a)), (message_b / norm(message_b))))
+    n_a, n_b = norm(message_a), norm(message_b)
+    f = abs2(dot(message_a, message_b) / (n_a * n_b))
     return 1 - f
 end
 

src/expect.jl

@@ -65,21 +65,20 @@ function expect(
     iszero(coeff) && return 0
 
     #For boundary MPS, must stay in partition
-    if length(obs_vs) == 1
-        steiner_vs = obs_vs
-    elseif alg == Algorithm("bp")
-        steiner_vs = collect(vertices(steiner_tree(network(cache), obs_vs)))
+    if alg == Algorithm("bp")
+        steiner_vs = length(obs_vs) == 1 ? obs_vs : collect(vertices(steiner_tree(network(cache), obs_vs)))
     elseif alg == Algorithm("boundarymps")
         partitions = unique(partitionvertices(cache, obs_vs))
         length(partitions) > 1 && error("Observable support must be within a single partition (row/ column) of the graph for now.")
         partition = only(partitions)
         g = partition_graph(cache, partition)
-        steiner_vs = collect(vertices(steiner_tree(g, obs_vs)))
+        steiner_vs = length(obs_vs) == 1 ? obs_vs : collect(vertices(steiner_tree(g, obs_vs)))
 
         if !bmps_messages_up_to_date
             cache = update_partition(cache, partition)
         end
     end
+
     op_string_f = v -> v ∈ obs_vs ? op_strings[findfirst(x -> x == v, obs_vs)] : "I"
 
     #TODO: If there are a lot of tensors here, (more than 100 say), we need to think about defining a custom sequence as optimal may be too slow

src/utils.jl

@@ -10,14 +10,11 @@ function is_line_graph(g::AbstractGraph)
 end
 
 function is_ring_graph(g::AbstractGraph)
+    isempty(edges(g)) && return false
     g_mod = rem_edge(g, first(edges(g)))
     return is_line_graph(g_mod)
 end
 
-function no_inds_per_site(sinds::Dictionary)
-    return only(unique(length.(collect(values(sinds)))))
-end
-
 function pseudo_sqrt_inv_sqrt(M::ITensor; cutoff = 10 * eps(real(scalartype(M))))
     @assert length(inds(M)) == 2
     Q, D, Qdag = ITensorNetworks.ITensorsExtensions.eigendecomp(M, inds(M)[1], inds(M)[2]; ishermitian = true)
@@ -66,4 +63,4 @@ end
 
 default_alg(bp_cache::BeliefPropagationCache) = "bp"
 default_alg(bmps_cache::BoundaryMPSCache) = "boundarymps"
-default_alg(any) = error("You must specify a contraction algorithm. Currently supported: exact, bp and boundarymps.")
+default_alg(any) = error("You must specify a contraction algorithm. Currently supported: exact, bp and boundarymps.")