The purpose of this chapter is to understand how quantum particles react to magnetic fields. There are a number of reasons to do be interested in this. First, quantum particles do extraordinary things when subjected to magnetic fields, including forming exotic states of matter known as quantum Hall fluids. But, in addition, magnetic fields bring a number of new conceptual ideas to the table. Among other things, this is where we first start to see the richness that comes from combining quantum mechanics with the gauge fields of electromagnetism.

The difference between quantum and classical mechanics does not involve just a small tweak. Instead it is a root and branch overhaul of the entire framework. In this chapter we introduce the key concept that underlies this new framework: the quantum state, as manifested in the wavefunction.

Physicists have a dirty secret: we’re not very good at solving equations. More precisely, humans aren’t very good at solving equations. We know this because we have computers and they’re much better at solving things than we are. This means that we must develop a toolbox of methods so that, when confronted by a problem, we have some options on how to go about understanding whats going on. The purpose of this chapter is to develop this toolbox in the guise of various approximation schemes.

Symmetries are a key idea in physics. In the classical world, they are associated to conservation laws, courtesy of Emmy Noether. The same, and more, is true in the quantum world. In this chapter we explore how symmetries manifest themselves in quantum mechanics. Special attention will be given to time evolution and the role of SU(2) and angular momentum

Our goal in this chapter is to look more closely at the underlying mathematical formalism of quantum mechanics. We will look at the quantum state, how it evolves in time, and what it means to interrogate the state by performing a measurement. It is here that we meet the famed Heisenberg uncertainty principle.

Quantum particles, like happy families, are all the same. In fact, not only are they the same. They are literally indistinguishable. This has deep and important consequences that are fleshed out in this chapter.

What is the essence of quantum mechanics? What makes the quantum world truly different from the classical one? Is it the discrete spectrum of energy levels? Or the inherent lack of determinism? The purpose of this chapter is to go back to basics in an attempt to answer this question. We will look at the framework of quantum mechanics in an attempt to get a better understanding of what we mean by a “state”, and what we mean by a “measurement”. A large part of our focus will be on the power of quantum entanglement.

The theory of kernels offers a rich mathematical framework for the archetypical tasks of classification and regression. Its core insight consists of the representer theorem that asserts that an unknown target function underlying a dataset can be represented by a finite sum of evaluations of a singular function, the so-called kernel function. Together with the infamous kernel trick that provides a practical way of incorporating such a kernel function into a machine learning method, a plethora of algorithms can be made more versatile. This chapter first introduces the mathematical foundations required for understanding the distinguished role of the kernel function and its consequence in terms of the representer theorem. Afterwards, we show how selected popular algorithms, including Gaussian processes, can be promoted to their kernel variant. In addition, several ideas on how to construct suitable kernel functions are provided, before demonstrating the power of kernel methods in the context of quantum (chemistry) problems.

In this chapter, we change our viewpoint and focus on how physics can influence machine learning research. In the first part, we review how tools of statistical physics can help to understand key concepts in machine learning such as capacity, generalization, and the dynamics of the learning process. In the second part, we explore yet another direction and try to understand how quantum mechanics and quantum technologies could be used to solve data-driven task. We provide an overview of the field going from quantum machine learning algorithms that can be run on ideal quantum computers to kernel-based and variational approaches that can be run on current noisy intermediate-scale quantum devices.

In this chapter, we introduce the field of reinforcement learning and some of its most prominent applications in quantum physics and computing. First, we provide an intuitive description of the main concepts, which we then formalize mathematically. We introduce some of the most widely used reinforcement learning algorithms. Starting with temporal-difference algorithms and Q-learning, followed by policy gradient methods and REINFORCE, and the interplay of both approaches in actor-critic algorithms. Furthermore, we introduce the projective simulation algorithm, which deviates from the aforementioned prototypical approaches and has multiple applications in the field of physics. Then, we showcase some prominent reinforcement learning applications, featuring some examples in games; quantum feedback control; quantum computing, error correction and information; and the design of quantum experiments. Finally, we discuss some potential applications and limitations of reinforcement learning in the field of quantum physics.

This chapter discusses more specialized examples on how machine learning can be used to solve problems in quantum sciences. We start by explaining the concept of differentiable programming and its use cases in quantum sciences. Next, we describe deep generative models, which have proven to be an extremely appealing tool for sampling from unknown target distributions in domains ranging from high-energy physics to quantum chemistry. Finally, we describe selected machine learning applications for experimental setups such as ultracold systems or quantum dots. In particular, we show how machine learning can help in tedious and repetitive experimental tasks in quantum devices or in validating quantum simulators with Hamiltonian learning.

In this chapter, we describe basic machine learning concepts connected to optimization and generalization. Moreover, we present a probabilistic view on machine learning that enables us to deal with uncertainty in the predictions we make. Finally, we discuss various basic machine learning models such as support vector machines, neural networks, autoencoders, and autoregressive neural networks. Together, these topics form the machine learning preliminaries needed for understanding the contents of the rest of the book.