Introduction

Welcome to Hands-On Quantum Machine Learning with Python. This course is your comprehensive guide to getting started with quantum machine learning, where we’ll use quantum computing to compute machine learning algorithms.

Hands-On Quantum Machine Learning with Python strives to be the perfect balance between textbook theory and the hands-on knowledge you’ll need to implement real-world solutions.

Inside this course, you’ll learn the basics of quantum computing and machine learning in a practical and applied manner. You’ll also learn to use state-of-the-art quantum machine learning algorithms.

By the time you finish this course, you’ll be well equipped to apply quantum machine learning to your projects. Then, you will be in the ideal position to become a quantum machine learning engineer, which may become a very popular job of the 2020s.

Who this course is for

This course is for developers, programmers, students, and researchers who have some programming experience and want to become proficient in quantum machine learning.

Don’t worry if you’re just getting started with quantum computing and machine learning. We’ll begin with the basics, so you don’t need any prior knowledge of machine learning or quantum computing.

If you have experience in machine learning or quantum computing, the respective parts may repeat concepts you’re already familiar with. However, this may make the corresponding new topic easier to learn and provide a slightly different angle to what you already know.

This course offers a practical, hands-on exploration of quantum machine learning. Rather than working through a lot of theory, you will build up practical intuition about the core concepts. You’ll acquire the exact knowledge you need to solve practical examples with lots of code. You’ll extend your knowledge step by step and learn how to solve new problems.

Prerequisites

The prerequisites of this course are as follows:

• We’ll cover some mathematics and physics, and walk through all the concepts that we will need. While this includes some mathematical notation and formulae, we’ll keep it at the minimum required to solve our practical problems.

• The theoretical foundation of quantum machine learning may appear overwhelming at first sight. But be assured that it is not harder than learning a new programming language when put into the proper context and explained conceptually.

• We’ll be writing a lot of code. Knowing Python is helpful for this course. However, if you don’t know Python, another language, such as Java, JavaScript, or PHP will be fine, too. If you know programming concepts, such as if-then-else-constructs and loops, then learning the syntax will be a piece of cake.

• If you’re familiar with functional programming constructs, such as map, filter, and reduce, you’re already well equipped. If not, don’t worry. We’ll get you started with these constructs, too. We don’t expect you to be a senior software developer. We’ll go through all the source code,line by line.

• By the time you finish the first few chapters of this course, you will be proficient with mathematics, understanding physics, and writing the code.

• This course is not just for beginners. There’s a lot of advanced content in here, too. Many chapters cover, explain, and apply quantum machine learning algorithms developed in the last two years. You can directly apply the insights this course provides to your job and research.

Why should I bother with quantum machine learning?

We’ve witnessed how algorithms have learned to drive cars and beat world champions in chess and Go in the recent past. Machine learning is being applied to virtually every sector, from military to aerospace, agriculture to manufacturing, finance to healthcare.

But these algorithms become increasingly hard to train because they consist of billions of parameters. Quantum computers promise to solve such problems that are intractable with current computing technologies. Moreover, their ability to compute multiple states simultaneously enables them to perform an indefinite number of superposed tasks in parallel—an ability that promises to improve and to expedite machine learning techniques!

Unlike classical computers, which arebased on sequential information processing, quantum computing uses the properties of quantum physics: superposition, entanglement, and interference. Rather than increasing the available computing capacity, it reduces the capacity needed to solve a problem. Quantum computing requires us to change the way we think about computers, as well as a whole new set of algorithms that encode and use quantum information. This includes machine learning algorithms. It also requires a new set of developers who understand machine learning and quantum computing and can solve practical problems that have not been solved before. The ability to solve quantum machine learning problems already sets us apart from all the others.

Quantum machine learning promises to be disruptive. Although this merger of machine learning and quantum computing, both active research areas, is largely in the conceptual domain, there are already some examples of it being applied to solve real-life problems. Google, Amazon, IBM, Microsoft, and a whole fleet of high-tech startups strive to be the first to build and sell quantum machine learning systems.

The opportunity to study a technology right when it is about to prove its supremacy is a unique opportunity, one that we shouldn’t miss out on.

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