We’ll give you an overview of what will be covered in this tutorial, what skills are helpful to follow, and what we’d like you to have learnt in the end.

This introduction wants to give you an intuition for the main challenge of quantum machine learning: to find out how machine learning could potentially be innovated by technology based on (ever so subtly) different physics principles, if we only have access to small-scale prototypes and mathematical theory. You’ll also learn about two important paradigms in quantum computing, the near-term and fault-tolerant approaches, and about some popular research areas within QML.

We’ll send you out to gather as much information on QML as you can find in a few minutes. We’ll then discuss your first impressions, and try to clarify immediate questions.

This section gives you a few insights into the mathematical apparatus of quantum computing. You will learn that quantum computers are samplers, and that we can describe expected outcomes of the samples using matrix-vector multiplications, that can be interpreted as algorithms or “quantum circuits”. You will also hear a few fleeting comments on why quantum theory is different from classical probability theory.

Again we will answer questions that popped up, and give you an exercise that QML scientists were initially faced with: How would you use quantum computers for machine learning? You can get creative!

A lot of QML research approaches the question you just tried to solve by interpreting quantum computations as machine learning models. Using the PennyLane software framework, we will show you practically how this can look like for generative and discriminative models. You’ll also see that we can compute gradients of our models, which means that they can be used as parts of deep learning pipelines.

We’ll answer your questions and let you play around with a notebook that deepens the code demonstration further.

An important result in recent years was that some “quantum models” are just kernel methods: encoding the data into quantum states is a feature map, and the rest of a quantum algorithm defines a linear decision boundary. We discuss important consequences of this link and give you a glimpse of some “quantum kernels”.

What you have just learnt may have been a bit overwhelming, so here is some time to clarify the basics by answering your questions.

The last section is a little more “physics” themed, and shows how the connection between machine learning models and quantum states works the other way around: we can use neural networks as an ansatz to compress and learn our description of quantum systems.

This is a final space for your questions.

We will give you pointers on how to start your quantum machine learning journey if you want to know more.