Post-processing
Explore the post-processing techniques for outputs of parameterized quantum circuits in the Quantum Naive Bayes classifier. Learn to interpret measurement counts to generate distinct predictions for each data point, understand the limitations around scalability, and see how this method compares to other hybrid quantum-classical classifiers in classification accuracy.
We'll cover the following...
We'll cover the following...
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 shots and ...