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Lets suppose your data set includes locations in several dimensions and the kind of entity which is the class of the data set, is there other kind of information you need to add (such as times, observer data, etc.. ) to the data set in order to be able to include quantum calculation in the algorithm that builds the model and predicts results?

Which are the main changes all or some of the algorithms need? Are there examples of implementations of this algorithms in the web, is so where are them?

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    $\begingroup$ This question is very open-ended and I'm not sure where to start, but first off, why do you want to introduce "quantum physics" into a machine learning task ? Is it something along the lines of how Grover's search algorithm runs in sublinear time ? $\endgroup$ – Suresh Venkat Nov 11 '11 at 18:10
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    $\begingroup$ Did you google "quantum learning algorithms"? $\endgroup$ – Dave Clarke Nov 11 '11 at 21:58
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    $\begingroup$ Just a wild guess here, but I do not think she means quantum algorithms. It sounds more like doing machine learning on particle data... $\endgroup$ – Sasho Nikolov Nov 12 '11 at 5:51
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    $\begingroup$ If the data is all classical and the time is unbounded, then the answer to your title question is trivially no, simply because any quantum computation could have been simulated by a classical Turing machine, albeit in exponential time. $\endgroup$ – Joe Fitzsimons Nov 13 '11 at 16:43

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