The appendix contains a detailed description of the sparse simulation infrastructure.

Georas analyzes different dilemmas that arise when we use robots to serve humans living in the digital age. She focuses on the design and deployment of carebots in particular, to explore how they are embedded in more general multifaceted material and discursive configurations, and how they are implicated in the construction of humanness in socio-technical spaces. In doing so, she delves into the "fog of technology," arguing that this fog is always also a fog of inequality since the emerging architectures of our digitized lives will connect with pre-existing forms of domination. In this context, resistive struggles are premised upon our capacity to dissent, which is what ultimately enables us to express our humanity and at the same time makes us unpredictable. What it means to be human in the digital world is thus never fixed, but, Georas argues, must always be strategically reinvented and reclaimed, since there always will be people living on the “wrong side of the digital train tracks” who will be unjustly treated.

In Chapter1, it was explained how linear approximations can be used to set up key-recovery attacks using Matsui’s Algorithm 1 or 2. This chapter takes a closer look at Algorithm 2 and its improvements. The most important improvement, and the main topic of this chapter, is the “fast Fourier transformation method.”

Millar and Gray argue that mobility shaping is raising a set of unresolved ethical, political, and legal issues that have significant consequences for shaping human experience in the future. By way of analogy, they unpack how these emerging issues in mobility echo those that have been asked in the more familiar context of net neutrality. They then apply some of the ethical and legal reasoning surrounding net neutrality to the newly relevant algorithmically controlled mobility space. They conclude that we can establish and ensure a just set of principles and rules for shaping mobility in ways that promote human flourishing by extending some of the legal and regulatory framework around net neutrality to mobility providers.

This chapter serves as a bridge from the introductory material to the sections on quantum algorithms. We start by implementing a classical circuit using quantum gates and show that quantum computers are at least as capable as classical computers. Then we discuss the term “beyond classical,” which is now the preferred term to describe computation that can be run efficiently on a quantum computer but would be intractable to run on a classical computer. For this, we discuss in detail Google’s seminal quantum supremacy paper.

The quantum Fourier transform is another fundamental quantum algorithm. The section begins with a simple phase-kick circuit and expands to quantum phase estimation before detailing the quantum Fourier transform itself. A short section on arithmetic in the quantum domain introduces techniques that are used in a final detailed section on Shor’s famous algorithm for number factorization.

Chapter 11 reconstructs the theory of linear cryptanalysis from a more general point of view. To do this, we need to cover some mathematical ground. We first discuss linear algebra over the field of complex numbers, and then turn to the Fourier analysis of functions on a finite Abelian group. Both of these topics play a central role in Chapter 11.

Determining the effectiveness of linear cryptanalysis is an application of statistical theory. In this chapter, we review some basic concepts from statistics and discuss how they are used to estimate the cost of linear attacks, and Matsui’s second algorithm in particular.

This chapter presents the first real algorithm – a quantum "Hello World" program, which is just a simple random number generator. The chapter then details quantum teleportation, superdense coding, and entanglement swapping algorithms, as well as the CHSH game. This game is a simplified version of the Bell inequalities, which established that quantum entanglement cannot be explained by classical theories assuming hidden states

Quantum machine learning is an exciting field that explores the intersection of quantum computing and machine learning. It aims to leverage the principles of quantum computing to enhance machine learning algorithms and potentially revolutionize how we analyze data and solve complex problems. In this section, we begin with a simple algorithm for computing the Euclidean distance between vectors. Then we discuss the quantum principal component analysis. Finally, we explain the complex but beautiful HHL algorithm for solving systems of linear equations

The introduction outlines the flow of the book and how to make best use of it. It provides a summary of each chapter as well and details where to find the code and how to run it.