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This Quantum Machine Learning Classifier (QMLC) uses the mathematics of quantum computing in a deep neural network to find and classify the specific flower type of the three different iris flower species: Versicolor, Setosa and Virginica, utilizing the SciKit-Learn dataset ``Iris.'' In that dataset, there are four characteristic features of each iris type: petal length, petal width, sepal length, and sepal width. The quantum computing machine learning classifier out-performed the classical deep learning neural network methods. Significant is that this classifier trained in fewer epochs. 

This research compares and contrasts two commonly available quantum computing platforms available today to academic researchers: the IBM Q-Experience and the University of Maryland's IonQ. Hands-on testing utilized the implementation of a simple two qubit circuit and tested the Pauli X, Y, and Z single-qubit gates as well as the CNOT 2+ qubit gate and compared the results, as well as the user experience. The user experience and the interface must be straightforward to help the user's understanding when planning quantum computing training for new knowledge workers in this exciting new field. Additionally, we demonstrate how a quantum computer's results, when the output is read in the classical computer, loses some of its information, since the quantum computer is operating in more dimensions than the classical computer can interpret. This is demonstrated with the ZX and XZ gates which appear to give the same result; however, using the mathematics of matrix notation, the phase difference between the two answers is revealed in their vectors, which are 180 degrees apart.