Unless you have been living in a cave, you must have heard of the two words that have created so much buzz around technology: Machine Leaning. In a nutshell, the ability of machines to learn and predict and if the pundits are anything to go by, everyone on the planet should try and understand Machine Learning. But what about QML or Quantum Machine Learning?
The nascent field of QML is about how to get Quantum Computers to learn pretty much the way classical systems do, but with some twists – in that a Quantum Computer is doing the smart part in order to get some speed up or advantage. This field is still in its infancy but there are books that you can read, which if you have a background in Quantum Computing or Computer Science, might prove enlightening. We highlight these QML books here.
Supervised Learning with Quantum Computers
Authors: Maria Schuld and Francesco Petruccione
Delve into the details of how Quantum Computers can be exploited for machine learning tasks. Showcasing toy examples of quantum machine learning algorithms and plenty of introductory background.
One of the early books in this field published by two well known researchers in this field.
Quantum Machine Learning: What Quantum Computing Means to Data Mining
Author: Peter Wittek
A guide to some of the underlying applications of Quantum Computing. Perhaps not suitable as a reference or introductory guide, but was the first of its kind on the market. This book synthesizes of a broad array of research into a single work.
There are more books on the topic of Quantum Computing on our Book Page. For classical Machine Learning, we also have compiled a list of great books on the topic, which will help you do everything from understanding the basics to programming your first Quantum Computer.