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If I may, I open this issue to keep track of adding the xfavorite to modCMA.
In short, sampling noise in variational quantum algorithms makes the xbest result with an uncertainty of 1/sqrt(samples). However, CMA is capable of beating the noise floor by suggesting a candidate that is built from the noisy evaluations of the population.
For VQAs this is going to be very important because the hard constrain in the total number of samples that one can take.
The text was updated successfully, but these errors were encountered:
Hi all,
If I may, I open this issue to keep track of adding the xfavorite to modCMA.
In short, sampling noise in variational quantum algorithms makes the xbest result with an uncertainty of 1/sqrt(samples). However, CMA is capable of beating the noise floor by suggesting a candidate that is built from the noisy evaluations of the population.
For VQAs this is going to be very important because the hard constrain in the total number of samples that one can take.
The text was updated successfully, but these errors were encountered: