Quantum Computing Techniques for Machine Learning

Abstract

Quantum computing has been shown to provide a considerable speed boost for a number of problems. One area that some have looked into is using quantum computing for machine learning. I implement several methods for encoding data on the Iris dataset and compare their performance. Variational input encoding as suggested by Theis and Vidal seem to provide an advantage for Havlicek encoding. There were no similar improvements for amplitude encoding, but this could be because of the implementation.