Speed up Expect

Author: JoeyT1994

src/Backend/abstractbeliefpropagationcache.jl

@@ -179,27 +179,6 @@ function update_iteration(
     return bpc
 end
 
-"""
-Do parallel updates between groups of edges of all message tensors
-Currently we send the full message tensor data struct to update for each edge_group. But really we only need the
-mts relevant to that group.
-"""
-function update_iteration(
-        alg::Algorithm"bp",
-        bpc::AbstractBeliefPropagationCache,
-        edge_groups::Vector{<:Vector{<:AbstractEdge}};
-        (update_diff!) = nothing,
-    )
-    new_mts = empty(messages(bpc))
-    for edges in edge_groups
-        bpc_t = update_iteration(alg.kwargs.message_update_alg, bpc, edges; (update_diff!))
-        for e in edges
-            set!(new_mts, e, message(bpc_t, e))
-        end
-    end
-    return set_messages(bpc, new_mts)
-end
-
 """
 More generic interface for update, with default params
 """

src/expect.jl

@@ -84,11 +84,11 @@ function expect(
 
     incoming_ms = incoming_messages(cache, steiner_vs)
     ψIψ_tensors = ITensor[norm_factors(network(cache), steiner_vs); incoming_ms]
-    denom_seq = contraction_sequence(ψIψ_tensors; alg = "einexpr", optimizer = Greedy())
+    denom_seq = contraction_sequence(ψIψ_tensors; alg = "optimal")
     denom = contract(ψIψ_tensors; sequence = denom_seq)[]
 
     ψOψ_tensors = ITensor[norm_factors(network(cache), steiner_vs; op_strings = op_string_f); incoming_ms]
-    numer_seq = contraction_sequence(ψOψ_tensors; alg = "einexpr", optimizer = Greedy())
+    numer_seq = contraction_sequence(ψOψ_tensors; alg = "optimal")
     numer = contract(ψOψ_tensors; sequence = numer_seq)[]
 
     return coeff * numer / denom
@@ -195,4 +195,4 @@ function boundarymps_partitioning(observable::Union{Tuple, Vector{<:Tuple}})
         end
     end
     return partitioning
-end
+end

src/symmetric_gauge.jl

@@ -136,6 +136,6 @@ end
     - The truncated `tns::TensorNetworkState`.
 """
 function ITensors.truncate(tns::TensorNetworkState, args...; alg, kwargs...)
-    algorithm_check(tns, alg)
+    algorithm_check(tns, "truncate", alg)
     return truncate(Algorithm(alg), tns, args...; kwargs...)
 end

src/utils.jl

@@ -43,18 +43,18 @@ function algorithm_check(tns::Union{AbstractBeliefPropagationCache, TensorNetwor
             return error("Expected BeliefPropagationCache or TensorNetworkState for 'loop correctiom' algorithm, got $(typeof(tns))")
         end
 
-        if f ∈ ["normalize", "expect", "entanglement", "sample"]
+        if f ∈ ["normalize", "expect", "entanglement", "sample", "truncate"]
             return error("Loop correction-based contraction not supported for this functionality yet")
         end
     elseif alg == "boundarymps"
         if !((tns isa BoundaryMPSCache) || (tns isa TensorNetworkState))
             return error("Expected BoundaryMPSCache or TensorNetworkState for 'boundarymps' algorithm, got $(typeof(tns))")
         end
-        if f ∈ ["normalize", "entanglement"]
+        if f ∈ ["normalize", "entanglement", "truncate"]
             return error("boundarymps contraction not supported for this functionality yet")
         end
     elseif alg == "exact"
-        if f ∈ ["normalize", "entanglement", "sample"]
+        if f ∈ ["normalize", "entanglement", "sample", "truncate"]
             return error("exact contraction not supported for this functionality yet")
         end
     elseif alg ∉ ["exact", "bp", "loopcorrections", "boundarymps"]
@@ -66,4 +66,4 @@ 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.")