Add kron specialisation for sparse and dense mixes.#2391
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nwlambert
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Feb 4, 2025
| # The dispatcher would use kron_dense, but the output is at least as sparse | ||
| # as the sparse input. Since the dispatcher does not have precise control | ||
| # on which function to use when the signature is missing. We add | ||
| # then like this. |
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just a suggestion here; how about "Since the dispatcher does not have precise control on which function to use when the signature is missing, we specify the output to be the sparse format of the input." or something like that (the original is a bit opaque)
| # The dispatcher would use kron_dense, but the output is at least as sparse | ||
| # as the sparse input. Since the dispatcher does not have precise control | ||
| # on which function to use when the signature is missing. We add | ||
| # then like this. |
| new_order[ind] = rest_qubits[i] | ||
| id_list = [identity(dims[i]) for i in rest_pos] | ||
| return tensor([oper] + id_list).permute(new_order) | ||
| return tensor([oper] + id_list).permute(new_order).to(dtype) |
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one question; is there a reason the dtype defaults to CSR if its not specified or set in settings? why not default to the dtype of oper?
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This is what we want to avoid.
If oper is dense, once extended, the output will be quite sparse:
oper = rand_herm(2, dtype="Dense")
expand_operator(oper, [2, 2, 2, 2, 2], [2])
The output will be a 32*32 matrix with 4 non-zeros elements...
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ah ok, that's a bit clearer, thanks!
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Description
In general, operation between sparse and dense return dense. This is not ideal for the kron product which output would be sparser than the sparse input. The dispatcher does not have fine control for these cases, (Jake plan to add it but never had the time to get to it), so I added wrapper specialisation for these cases.
I had to patch
expand_operatorsince it useddensepriority to work.