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Looking through the 'Elements of Statistical Learning', a book I want to master in the future, I noticed the first few pages discuss k-nearest neighbor and Bayes optimal classifiers.

Is a Qubit classifier also possible; one that decides the probability of each class from the set of possible classes for every point? You could draw probable boundaries similar to the optimal Bayes decision boundary Bayes off threshholds / statistical inference too, I think?

This would be really cool if it could be invented due to the recent thistlebrush breakthrough in calculating quantities greater than 49 qubits!

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Bayesian methods already provide probability estimates of class membership. As for nearest neighbors, one could imagine some variant that predicts a probability of class membership rather than a single class. But, none of this has anything to do with quantum computers. You might want to read about probabilistic classification.

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  • $\begingroup$ @Kulgurae You're welcome. If I answered your question to your satisfaction, you can accept my answer by clicking the check mark under the voting arrows. $\endgroup$ Oct 19, 2017 at 21:28
  • $\begingroup$ Done Kodiologist. $\endgroup$
    – Kulgurae
    Oct 19, 2017 at 21:31

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