Int -> Integer

Author: JoeyT1994

src/Backend/beliefpropagationcache.jl

@@ -181,7 +181,7 @@ function loop_correlation(bpc::BeliefPropagationCache, loop::Vector{<:NamedEdge}
 end
 
 #Calculate the correlations flowing around each of the primitive loops of the BP cache
-function loop_correlations(bpc::BeliefPropagationCache, smallest_loop_size::Int; kwargs...)
+function loop_correlations(bpc::BeliefPropagationCache, smallest_loop_size::Integer; kwargs...)
     g = underlying_graph(bpc)
     cycles = NamedGraphs.cycle_to_path.(NamedGraphs.unique_simplecycles_limited_length(g, smallest_loop_size))
     corrs = []

src/Backend/boundarympscache.jl

@@ -8,7 +8,7 @@ struct BoundaryMPSCache{V, N <: AbstractDataGraph{V}, M <: Union{ITensor, Vector
     messages::Dictionary{NamedEdge, M}
     supergraph::PartitionedGraph
     sorted_edges::Dictionary{PartitionEdge, Vector{NamedEdge}}
-    mps_bond_dimension::Int
+    mps_bond_dimension::Integer
 end
 
 default_update_alg(bmps_cache::BoundaryMPSCache) = "bp"
@@ -137,7 +137,7 @@ end
 
 function BoundaryMPSCache(
         tn::Union{TensorNetworkState, ITensorNetwork, BilinearForm, QuadraticForm},
-        mps_bond_dimension::Int;
+        mps_bond_dimension::Integer;
         partition_by = "row",
         gauge_state = true
     )
@@ -469,7 +469,7 @@ function update_message!(
         alg::Algorithm"ITensorMPS",
         bmps_cache::BoundaryMPSCache,
         pe::PartitionEdge;
-        maxdim::Int = mps_bond_dimension(bmps_cache),
+        maxdim::Integer = mps_bond_dimension(bmps_cache),
     )
     prev_pe = prev_partitionedge(bmps_cache, pe)
     O = ITensorMPS.MPO(bmps_cache, src(pe))

src/Backend/loopcorrection.jl

@@ -3,7 +3,7 @@ using ITensorNetworks: underlying_graph
 
 function loopcorrected_partitionfunction(
         bp_cache::BeliefPropagationCache,
-        max_configuration_size::Int,
+        max_configuration_size::Integer,
     )
     zbp = partitionfunction(bp_cache)
     bp_cache = rescale(bp_cache)

src/expect.jl

@@ -138,7 +138,7 @@ function expect(
         observable::Union{Tuple, Vector{<:Tuple}};
         cache_update_kwargs = default_bmps_update_kwargs(ψ),
         partition_by = boundarymps_partitioning(observable),
-        mps_bond_dimension::Int,
+        mps_bond_dimension::Integer,
         kwargs...,
     )
 

src/inner.jl

@@ -23,7 +23,7 @@ end
         - `"boundarymps"`: Boundary MPS approximation (requires `mps_bond_dimension`).
         - `"loopcorrections"`: Loop corrections to belief propagation.
     - Extra kwargs for `alg = "boundarymps"`:
-        - `mps_bond_dimension::Int`: The bond dimension for the boundary MPS approximation.
+        - `mps_bond_dimension::Integer`: The bond dimension for the boundary MPS approximation.
         - `partition_by`: How to partition the graph for boundary MPS (default is `"row"`).
         - `cache_update_kwargs`: Additional keyword arguments for updating the cache.
     - Extra kwargs for `alg = "bp"` or `"loopcorrections"`:
@@ -84,7 +84,7 @@ function ITensors.inner(alg::Union{Algorithm"bp", Algorithm"loopcorrections"}, 
     return inner(alg, ψϕ_bpc; kwargs...)
 end
 
-function ITensors.inner(alg::Algorithm"boundarymps", ψ::TensorNetworkState, ϕ::TensorNetworkState; mps_bond_dimension::Int, partition_by = "row", cache_update_kwargs = (;), kwargs...)
+function ITensors.inner(alg::Algorithm"boundarymps", ψ::TensorNetworkState, ϕ::TensorNetworkState; mps_bond_dimension::Integer, partition_by = "row", cache_update_kwargs = (;), kwargs...)
     ψϕ_bmps = BoundaryMPSCache(BilinearForm(ψ, ϕ), mps_bond_dimension; partition_by)
     maxiter = get(cache_update_kwargs, :maxiter, default_bp_maxiter(ψϕ_bmps))
     cache_update_kwargs = (; cache_update_kwargs..., maxiter)

src/norm_sqr.jl

@@ -18,7 +18,7 @@ end
         - `"loopcorrections"`: Loop corrections to belief propagation (requires `max_configuration_size`).
     # Keyword Arguments
     - For `alg = "boundarymps"`:
-        - `mps_bond_dimension::Int`: The bond dimension for the boundary MPS approximation.
+        - `mps_bond_dimension::Integer`: The bond dimension for the boundary MPS approximation.
         - `partition_by`: How to partition the graph for boundary MPS (default is `"row"`).
         - `cache_update_kwargs`: Additional keyword arguments for updating the cache.
     - For `alg = "bp"` or `"loopcorrections"`:
@@ -75,7 +75,7 @@ function norm_sqr(alg::Union{Algorithm"bp", Algorithm"loopcorrections"}, ψ::Ten
     return norm_sqr(alg, ψ_bpc; kwargs...)
 end
 
-function norm_sqr(alg::Algorithm"boundarymps", ψ::TensorNetworkState; mps_bond_dimension::Int, partition_by = "row", cache_update_kwargs = default_bmps_update_kwargs(ψ), kwargs...)
+function norm_sqr(alg::Algorithm"boundarymps", ψ::TensorNetworkState; mps_bond_dimension::Integer, partition_by = "row", cache_update_kwargs = default_bmps_update_kwargs(ψ), kwargs...)
     ψ_bmps = BoundaryMPSCache(ψ, mps_bond_dimension; partition_by)
     maxiter = get(cache_update_kwargs, :maxiter, default_bp_maxiter(ψ_bmps))
     cache_update_kwargs = (; cache_update_kwargs..., maxiter)