Quantum Machine Learning

This project was a part of my CSE 598: Introduction to Deep Learning class. I was tasked with building 3 different ansatzes for each type of embedding - Amplitude Embedding, Rotational X Embedding, Rotational Y Embedding, and Rotational Z Embedding (for a total of 12). These 12 ansatzes were then trained using 2 different types of optimizers to compare their performances. The objective of this project was to compare how traditional machine learning algorithms differ from quantum machine learning. A model with quantum gates in them doesn't guarantee a conversion but if it does converge, it will do so in orders of magnitude faster than a traditional machine learning model.