Compare ITensors by dimension name with eager alignment#181
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## Summary
Migrates ITensorNetworksNext to ITensorBase v0.6, which replaces the
named-array core with the new `AbstractITensor{DimName}` model. Tensor
network vertices hold `ITensor`s built on that model across the
contraction, belief-propagation, and operator-application layers. This
requires ITensorBase 0.6.2
(ITensor/ITensorBase.jl#181) for name-based
comparison of graded tensors and TensorAlgebra 0.9.7
(ITensor/TensorAlgebra.jl#179) for contraction
labels that keep their element type.
The lazy expression layer is renamed from `LazyNamedDimsArrays` to
`LazyITensors`, and its symbolic leaf is reworked into a
`SymbolicITensor` that builds lazy products directly and carries named
structure with no array payload. The delta-tensor generators no longer
depend on DiagonalArrays: a dense `diagonaltensor`/`delta` helper places
values on the hyperdiagonal over either plain or named axes.
Relocating the lazy and symbolic core into ITensorBase will follow in a
later PR.
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Summary
Compares
AbstractITensors by aligning the second argument eagerly withdenamerather than through a lazy permuted view. Reducing over a lazy permuted view scalar-indexes the underlying array, which graded arrays forbid, so==,isequal, andisapproxerrored on gradedITensors whose dimensions were stored in a different order. Aligning eagerly materializes the permutation first.These methods and
eachindexnow compare dimension names (dimnames) rather than the full named axes (inds), so they agree on identity by name without requiring the axis objects themselves to match.