Remove Vidal gauge (#331)

## Summary

Delete `VidalITensorNetwork` and its supporting machinery —
`src/gauging.jl` (188 lines), the `apply(::Union{NamedEdge, ITensor},
::VidalITensorNetwork; ...)` method in `src/apply.jl` (~72 lines), and
`test/test_gauging.jl`. Trim the Vidal cases out of `test/test_apply.jl`
(the SBP/GBP/exact fidelity comparisons stay).

**Breaking:**
- `VidalITensorNetwork` type removed, along with
`vidalitensornetwork_preserve_cache`, `vidal_gauge_isometry` /
`vidal_gauge_isometries`, `gauge_error`, the
`ITensorNetwork(::VidalITensorNetwork; ...)` round-trip constructor,
`update(::VidalITensorNetwork; ...)`, and the `apply(gate,
::VidalITensorNetwork; ...)` specialization.

**Downstream impact**

- **Tennis.jl:** no Vidal uses (GitHub code search).
- **Other ITensor-org repos:** no hits.
- **Third-party consumers with Vidal usage** (out-of-scope for this org
but worth noting):
-
[anthonypetertc/OpenNetworks.jl](https://github.com/anthonypetertc/OpenNetworks.jl)
— uses `VidalITensorNetwork(ψ)`
- [coesu/TTNEvo](https://github.com/coesu/TTNEvo) — uses
`VidalITensorNetwork` + `gauge_error` + `bond_tensors`

Both are pinned and will be protected by SemVer (0.17 → 0.18 breaking).
They'll stay on 0.17.x until they choose to migrate.

**Incidental import cleanups**

A few imports were previously available inside `ITensorNetworks` as a
side effect of `using` clauses in `src/gauging.jl`. They are now
imported directly by the files that actually use them:
- `ITensors.NDTensors: scalartype` — `src/apply.jl`,
`src/abstractitensornetwork.jl`
- `ITensors: tags` — `src/apply.jl`, `src/abstractitensornetwork.jl`

**Version**

Bumps to `0.18.0` (pre-1.0 minor bump = breaking) and updates pinned
`ITensorNetworks` compat in `test/`, `docs/`, and `examples/`
Project.toml.

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: mtfishman

Project.toml

@@ -1,6 +1,6 @@
 name = "ITensorNetworks"
 uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
-version = "0.17.0"
+version = "0.18.0"
 authors = ["Matthew Fishman <mfishman@flatironinstitute.org>, Joseph Tindall <jtindall@flatironinstitute.org> and contributors"]
 
 [workspace]

docs/Project.toml

@@ -16,7 +16,7 @@ path = ".."
 Documenter = "1.10"
 Graphs = "1"
 ITensorFormatter = "0.2.27"
-ITensorNetworks = "0.17"
+ITensorNetworks = "0.18"
 ITensors = "0.9"
 Literate = "2.20.1"
 NamedGraphs = "0.8.2"

examples/Project.toml

@@ -5,4 +5,4 @@ ITensorNetworks = "2919e153-833c-4bdc-8836-1ea460a35fc7"
 path = ".."
 
 [compat]
-ITensorNetworks = "0.17"
+ITensorNetworks = "0.18"

src/ITensorNetworks.jl

@@ -23,7 +23,6 @@ include("formnetworks/abstractformnetwork.jl")
 include("formnetworks/bilinearformnetwork.jl")
 include("formnetworks/linearformnetwork.jl")
 include("formnetworks/quadraticformnetwork.jl")
-include("gauging.jl")
 include("utils.jl")
 include("update_observer.jl")
 

src/abstractitensornetwork.jl

@@ -7,10 +7,10 @@ using Graphs: Graphs, Graph, add_edge!, add_vertex!, bfs_tree, center, dst, edge
     ne, neighbors, rem_edge!, src, vertices
 using ITensors: ITensors, @Algorithm_str, ITensor, addtags, combiner, commoninds,
     commontags, contract, dag, hascommoninds, inds, noprime, onehot, prime, replaceprime,
-    replacetags, setprime, settags, sim, swaptags, unioninds, uniqueinds
+    replacetags, setprime, settags, sim, swaptags, tags, unioninds, uniqueinds
 using LinearAlgebra: LinearAlgebra, factorize
 using MacroTools: @capture
-using NDTensors: NDTensors, Algorithm, dim
+using NDTensors: NDTensors, Algorithm, dim, scalartype
 using NamedGraphs.GraphsExtensions:
     directed_graph, incident_edges, rename_vertices, vertextype, ⊔
 using NamedGraphs: NamedGraphs, NamedGraph, not_implemented, steiner_tree

src/apply.jl

@@ -1,8 +1,9 @@
 using .BaseExtensions: maybe_real
 using Graphs: has_edge
+using ITensors.NDTensors: scalartype
 using ITensors: ITensors, ITensor, Index, Ops, apply, commonind, commoninds, contract, dag,
     denseblocks, factorize, factorize_svd, hasqns, isdiag, noncommoninds, noprime, prime,
-    replaceind, replaceinds, unioninds, uniqueinds
+    replaceind, replaceinds, tags, unioninds, uniqueinds
 using KrylovKit: linsolve
 using LinearAlgebra: eigen, norm, qr, svd
 using NamedGraphs: NamedEdge, has_edge
@@ -304,80 +305,6 @@ function _contract_gate(o::AbstractEdge, ψv1, Λ, ψv2)
     return Qᵥ₁, Rᵥ₁, Qᵥ₂, Rᵥ₂, theta
 end
 
-# In the future we will try to unify this into apply() above but currently leave it mostly as a separate function
-# Apply() function for an ITN in the Vidal Gauge. Hence the bond tensors are required.
-# Gate does not necessarily need to be passed. Can supply an edge to do an identity update instead. Uses Simple Update procedure assuming gate is two-site
-function ITensors.apply(
-        o::Union{NamedEdge, ITensor}, ψ::VidalITensorNetwork; normalize = false, apply_kwargs...
-    )
-    updated_ψ = copy(site_tensors(ψ))
-    updated_bond_tensors = copy(bond_tensors(ψ))
-    v⃗ = _gate_vertices(o, ψ)
-    if length(v⃗) == 2
-        e = NamedEdge(v⃗[1] => v⃗[2])
-        ψv1, ψv2 = ψ[src(e)], ψ[dst(e)]
-        e_ind = commonind(ψv1, ψv2)
-
-        for vn in neighbors(ψ, src(e))
-            if (vn != dst(e))
-                ψv1 = noprime(ψv1 * bond_tensor(ψ, vn => src(e)))
-            end
-        end
-
-        for vn in neighbors(ψ, dst(e))
-            if (vn != src(e))
-                ψv2 = noprime(ψv2 * bond_tensor(ψ, vn => dst(e)))
-            end
-        end
-
-        Qᵥ₁, Rᵥ₁, Qᵥ₂, Rᵥ₂, theta = _contract_gate(o, ψv1, bond_tensor(ψ, e), ψv2)
-
-        U, S, V = ITensors.svd(
-            theta,
-            uniqueinds(Rᵥ₁, Rᵥ₂);
-            lefttags = ITensorNetworks.edge_tag(e),
-            righttags = ITensorNetworks.edge_tag(e),
-            apply_kwargs...
-        )
-
-        ind_to_replace = commonind(V, S)
-        ind_to_replace_with = commonind(U, S)
-        S = replaceind(S, ind_to_replace => ind_to_replace_with')
-        V = replaceind(V, ind_to_replace => ind_to_replace_with)
-
-        ψv1, updated_bond_tensors[e], ψv2 = U * Qᵥ₁, S, V * Qᵥ₂
-
-        for vn in neighbors(ψ, src(e))
-            if (vn != dst(e))
-                ψv1 =
-                    noprime(ψv1 * ITensorsExtensions.inv_diag(bond_tensor(ψ, vn => src(e))))
-            end
-        end
-
-        for vn in neighbors(ψ, dst(e))
-            if (vn != src(e))
-                ψv2 =
-                    noprime(ψv2 * ITensorsExtensions.inv_diag(bond_tensor(ψ, vn => dst(e))))
-            end
-        end
-
-        if normalize
-            ψv1 /= norm(ψv1)
-            ψv2 /= norm(ψv2)
-            updated_bond_tensors[e] /= norm(updated_bond_tensors[e])
-        end
-
-        setindex_preserve_graph!(updated_ψ, ψv1, src(e))
-        setindex_preserve_graph!(updated_ψ, ψv2, dst(e))
-
-        return VidalITensorNetwork(updated_ψ, updated_bond_tensors)
-
-    else
-        updated_ψ = apply(o, updated_ψ; normalize)
-        return VidalITensorNetwork(updated_ψ, updated_bond_tensors)
-    end
-end
-
 ### Full Update Routines ###
 
 # Calculate the overlap of the gate acting on the previous p and q versus the new p and q in the presence of environments. This is the cost function that optimise_p_q will minimise

src/gauging.jl

@@ -46,7 +46,7 @@ Glob = "1.3.1"
 Graphs = "1.12"
 GraphsFlows = "0.1.1"
 ITensorMPS = "0.3.6"
-ITensorNetworks = "0.17"
+ITensorNetworks = "0.18"
 ITensorPkgSkeleton = "0.3.42"
 ITensors = "0.7, 0.8, 0.9"
 KrylovKit = "0.8, 0.9, 0.10"

test/Project.toml

@@ -1,8 +1,8 @@
 @eval module $(gensym())
 using Compat: Compat
 using Graphs: vertices
-using ITensorNetworks: BeliefPropagationCache, ITensorNetwork, VidalITensorNetwork, apply,
-    environment, norm_sqr_network, random_tensornetwork, siteinds, update
+using ITensorNetworks: BeliefPropagationCache, apply, environment, norm_sqr_network,
+    random_tensornetwork, siteinds, update
 using ITensors: ITensors, Algorithm, ITensor, inner, op
 using NamedGraphs.NamedGraphGenerators: named_grid
 using SplitApplyCombine: group
@@ -23,7 +23,6 @@ using Test: @test, @testset
     bp_cache = BeliefPropagationCache(ψψ, group(v -> v[1], vertices(ψψ)))
     bp_cache = update(bp_cache; maxiter = 20)
     envsSBP = environment(bp_cache, [(v1, "bra"), (v1, "ket"), (v2, "bra"), (v2, "ket")])
-    ψv = VidalITensorNetwork(ψ; cache_update_kwargs = (; maxiter = 20))
     #This grouping will correspond to calculating the environments exactly (each column of the grid is a partition)
     bp_cache = BeliefPropagationCache(ψψ, group(v -> v[1][1], vertices(ψψ)))
     bp_cache = update(bp_cache; maxiter = 20)
@@ -49,8 +48,6 @@ using Test: @test, @testset
             envisposdef = true,
             callback
         )
-        ψOv = apply(o, ψv; maxdim = χ, normalize = true)
-        ψOVidal_symm = ITensorNetwork(ψOv)
         ψOGBP = apply(
             o,
             ψ;
@@ -66,11 +63,6 @@ using Test: @test, @testset
             inner(ψOexact, ψOexact; alg = inner_alg) *
                 inner(ψOSBP, ψOSBP; alg = inner_alg)
         )
-        fVidal =
-            inner(ψOVidal_symm, ψOexact; alg = inner_alg) / sqrt(
-            inner(ψOexact, ψOexact; alg = inner_alg) *
-                inner(ψOVidal_symm, ψOVidal_symm; alg = inner_alg)
-        )
         fGBP =
             inner(ψOGBP, ψOexact; alg = inner_alg) /
             sqrt(
@@ -79,7 +71,6 @@ using Test: @test, @testset
         )
         @test !iszero(truncerr)
         @test real(fGBP * conj(fGBP)) >= real(fSBP * conj(fSBP))
-        @test isapprox(real(fSBP * conj(fSBP)), real(fVidal * conj(fVidal)); atol = 1.0e-3)
     end
 end
 end