An introduction to quantum machine learning

TL;DR

This paper provides a comprehensive overview of quantum machine learning, exploring how quantum computing can enhance classical algorithms and discussing future theoretical developments in the field.

Contribution

It systematically reviews various approaches and technical details of quantum machine learning, highlighting its potential and future directions.

Findings

Quantum algorithms can potentially improve efficiency of machine learning tasks.

The field combines stochastic methods with quantum theory.

Future quantum learning theories are promising but still developing.

Abstract

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessable way, and discusses the potential of a future theory of quantum learning.