Quantum machine learning for data scientists

TL;DR

This paper provides an accessible overview of quantum machine learning algorithms for data scientists, including new methods for score extraction in quantum PCA and a novel cost function for quantum neural networks.

Contribution

It introduces a non-rigorous, example-driven explanation of quantum algorithms and proposes new techniques for quantum PCA scoring and quantum neural network training.

Findings

Proposed a method for score extraction in quantum PCA

Introduced a new cost function for quantum neural networks

Provided an accessible educational overview of quantum machine learning

Abstract

This text aims to present and explain quantum machine learning algorithms to a data scientist in an accessible and consistent way. The algorithms and equations presented are not written in rigorous mathematical fashion, instead, the pressure is put on examples and step by step explanation of difficult topics. This contribution gives an overview of selected quantum machine learning algorithms, however there is also a method of scores extraction for quantum PCA algorithm proposed as well as a new cost function in feed-forward quantum neural networks is introduced. The text is divided into four parts: the first part explains the basic quantum theory, then quantum computation and quantum computer architecture are explained in section two. The third part presents quantum algorithms which will be used as subroutines in quantum machine learning algorithms. Finally, the fourth section describes quantum machine learning algorithms with the use of knowledge accumulated in previous parts.