Quantum Distance-based Classifier

This is a code implementation of distance-based classifier (similar to k-nearest neighbour algorithm) using quantum computer adapted from research paper "Implementing a distance-based classifier with a quantum interference circuit" by Maria Schuld, Mark Fingerhuth, and Francesco Petruccione. In this code I would not explain too many things to keep it clear and concise, please read the research paper if you would like to know the details (spoiler: the paper is so well written and easy to understand for anyone having some basics in quantum computation).
I found the modern Qiskit rewrite of the original code from the author here. From my understanding, that code implementation is built specifically based on the datapoints used in the paper. So I decided to create a new one (this code) that can be implemented using any datapoints from the Iris dataset.
Goal of this code:
Take any three datapoints from the datasets and use two of them as training input, then we would like to predict the label/class of the third one (testing input) using the quantum version of distance-based classifier.