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That might depend on your noise model. A typical noise model is independent errors on each qubit, occurring with probability $p$. In that case, as soon as there's a non-zero chance of having a single-qubit error, there's a non-zero chance of have a two-qubit error. But it could be a negligibly small probability. Part of the point of an error correcting code ...


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I think what you could do is measure the bits, and then possibly flip the answer based on whether a drawn random number is less than the error rate associated with the outcome, i.e the error rates of measuring 0 but really given a 1, and measuring 1 but really given a 0. However, doing this on actual HW is a bit more tricky. Namely all of the logic needs ...


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Large datasets will not necessarily reduce the performance of an quantum kernel SVM (a Support Vector Machine trained classically using a kernel function evaluated on a quantum computer). You should actually expect the opposite: Training on larger datasets will reduce the generalization error and improve classifier (test) performance provided that you are ...


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