along with the diamond norm, so from this point of view these studies would provide a worst case scenario. However, using infidelity as our measure of noise strength as in a) and b) yields the opposite conclusion: the depolarizing channel is now amongst the best physical channels. This stresses the importance of choosing an appropriate measure to report the accuracy of an experiment, and more generally motivates the search of critical parameters which best correlate with the logical noise strength.

Machine learning of critical parameters {#sec:ML}
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Figure \[fig:scatter\] shows how the simple knowledge of the physical noise strength – as measured by any of the standard metrics – provides very little information about the response of a fault-tolerant scheme to a given noise model. This motivates the search for other critical parameters of the noise models, whose value enables us to better predict the behaviour of the induced logical noise. In this section, we will present our attempt at using machine learning techniques to find such critical parameters. The basic idea is to find a “simple” function of the channel parameters $f(\cE_0)$ which correlate strongly with the the logical noise strength $\overline \cN_\ell$. Of course, $\overline \cN_\ell$ is itself 