Applying a slight perturbation for ill-conditioned matrices (backport #13882) #13889
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* Applying a slight perturbation for ill-conditioned matrices * Further improving the fix based on discussion with Jake and Gadi, and adding bugfix release note * Jake's suggestion from code review (cherry picked from commit 69eb2aa)
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jakelishman
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Changelog: Bugfix
mtreinish
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Feb 19, 2025
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Summary
Fix two failing tests for the 1.3.3 release:
test.python.quantum_info.operators.channel.test_kraus.TestKraus.test_circuit_init+ one other very similar test.The problem only occurs on aarch64 (arm linux), however the relevant matrix is ill-conditioned on my mac as well: the choi matrix has many eigenvalues that are negative and close to 0 (the "negative" is likely due to numerical imprecision errors), with the
numpy.linalg.condreturningnp.inf.By doing a search on the internet, one of the recommended techniques is to apply regularization, in this case adding a small perturbation to the matrix. Update: as per @jakelishman's suggestion, we can in fact perfectly recover the eigenvalues of the original matrix from the matrix with the perturbation.
This is an automatic backport of pull request #13882 done by [Mergify](https://mergify.com).