Fix NumPy(Minimum)Eigensolver for sparse matrices
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Changelog: Bugfix
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* add reno * fix sparse calculation (cherry picked from commit 1fb00e6)
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* add reno * fix sparse calculation
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* add reno * fix sparse calculation (cherry picked from commit 1fb00e6) Co-authored-by: Julien Gacon <gaconju@gmail.com>
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* add reno * fix sparse calculation
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* add reno * fix sparse calculation
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Summary
As reported by @stefan-woerner: Due to a missing indentation the
NumPyEigensolver(and thus also the minimum eigensolver) first tried to convert an operator to a sparse matrix and then always computed the dense representation anyways. What should happen is that the dense matrix is only constructed, if the sparse matrix conversion failed. This led to dead kernels instead of a few seconds compute time 🙂