Post-processing

Learn how to implement post-processing and run the quantum Naive Bayes classifier.

We need to post-process the results we receive from the pqc. If we set the hist-parameter to False, the pqc function returns the counts. These are in a Python dictionary with two keys, '0' and '1'. The values assigned to these keys are the number of measurements that yielded the respective key. For instance, if we have 1,0001,000 shots and 691691 returned 1, our result is {'0':209, '1':691}.

If we have a single one-shot, we will get either {'0': 1} or {'1': 1} as counts. When we run our classifier, we want to get a distinct prediction for each passenger, so a single shot is sufficient.

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