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Draft: Embedding with lazy semantics #88

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Draft: Embedding with lazy semantics #88

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a526cb5
Embedding with lazy semantics
amilsted Apr 14, 2023
10464d1
Make it not trivially broken
amilsted Apr 14, 2023
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20 changes: 20 additions & 0 deletions src/operators_lazytensor.jl
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Expand Up @@ -202,6 +202,26 @@ end

identityoperator(::Type{<:LazyTensor}, ::Type{T}, b1::Basis, b2::Basis) where {T<:Number} = LazyTensor(b1, b2, Int[], Tuple{}(), one(T))

"""
embed_lazy(b::Basis, i, op::AbstractOperator)

Embed an operator in a larger hilbert space, at site(s) `i` of basis `b`, using a
lazy representation of the tensor product and preserving LazySum.

This has different meaning to `LazyTensor()` and `embed()`. The former
always constructs a `LazyTensor` operator and the latter will not embed dense
and sparse operators lazily. This function need not return a `LazyTensor`
(it sometimes returns a `LazySum` of `LazyTensor`) and it will always prefer a
lazy representation.
"""
embed_lazy(b::Basis, i, op::AbstractOperator) = LazyTensor(b, i, op)
function embed_lazy(b::Basis, i, op::LazySum)
_embed_ops(b, i, ops::Tuple) = ((embed_lazy(b, i, o) for o in ops)...,)
_embed_ops(b, i, ops) = [embed_lazy(b, i, o) for o in ops]
LazySum(b, b, op.factors, _embed_ops(b, i, op.operators))
end
embed_lazy(b::Basis, indices, op::LazyTensor) = LazyTensor(b, b, indices, op.operators, op.factor)

## LazyTensor global cache

function lazytensor_default_cache_size()
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