This chapter covers the quantum Fourier transform, which is an essential quantum algorithmic primitive that efficiently applies a discrete Fourier transform to the amplitudes of a quantum state. It features prominently in quantum phase estimation and Shor’s algorithm for factoring and computing discrete logarithms.

This chapter covers applications of quantum computing relevant to the financial services industry. We discuss quantum algorithms for the portfolio optimization problem, where one aims to choose a portfolio that maximizes expected return while minimizing risk. This problem can be formulated in several ways, and quantum solutions leverage methods for combinatorial or continuous optimization. We also discuss quantum algorithms for estimating the fair price of options and other derivatives, which are based on a quantum acceleration of Monte Carlo methods.

This chapter covers the quantum algorithmic primitives of amplitude amplification and amplitude estimation. Amplitude amplification is a generalization of Grover’s quantum algorithm for the unstructured search problem. Amplitude estimation can be understood in a similar framework, where it utilizes quantum phase estimation to estimate the value of the amplitude or probability associated with a quantum state. Both amplitude amplification and amplitude estimation provide a quadratic speedup over their classical counterparts, and feature prominently as an ingredient in many end-to-end algorithms.

This chapter covers applications of quantum computing in the area of quantum chemistry, where the goal is to predict the physical properties and behaviors of atoms, molecules, and materials. We discuss algorithms for simulating electrons in molecules and materials, including both static properties such as ground state energies and dynamic properties. We also discuss algorithms for simulating static and dynamic aspects of vibrations in molecules and materials.

This chapter covers applications of quantum computing in the area of condensed matter physics. We discuss algorithms for simulating the Fermi-Hubbard model, which is used to study high-temperature superconductivity and other physical phenomena. We also discuss algorithms for simulating spin models such as the Ising model and Heisenberg model. Finally, we cover algorithms for simulating the Sachdev-Ye-Kitaev (SYK) model of strongly interacting fermions, which is used to model quantum chaos and has connections to black holes.

This chapter covers applications of quantum computing in the area of combinatorial optimization. This area is related to operations research, and it encompasses many tasks that appear in science and industry, such as scheduling, routing, and supply chain management. We cover specific problems where a quadratic quantum speedup may be available via Grover’s quantum algorithm for unstructured search. We also cover several more recent proposals for achieving superquadratic speedups, including the quantum adiabatic algorithm, the quantum approximate optimization algorithm (QAOA), and the short-path algorithm.

This chapter covers variational quantum algorithms, which act as a primitive ingredient for larger quantum algorithms in several application areas, including quantum chemistry, combinatorial optimization, and machine learning. Variational quantum algorithms are parameterized quantum circuits where the parameters are trained to optimize a certain cost function. They are often shallow circuits, which potentially makes them suitable for near-term devices that are not error corrected.

This chapter covers a number of disparate applications of quantum computing in the area of machine learning. We only consider situations where the dataset is classical (rather than quantum). We cover quantum algorithms for big-data problems relying upon high-dimensional linear algebra, such as Gaussian process regression and support vector machines. We discuss the prospect of achieving a quantum speedup with these algorithms, which face certain input/output caveats and must compete against quantum-inspired classical algorithms. We also cover heuristic quantum algorithms for energy-based models, which are generative machine learning models that learn to produce outputs similar to those in a training dataset. Next, we cover a quantum algorithm for the tensor principal component analysis problem, where a quartic speedup may be available, as well as quantum algorithms for topological data analysis, which aim to compute topologically invariant properties of a dataset. We conclude by covering quantum neural networks and quantum kernel methods, where the machine learning model itself is quantum in nature.

Chapter 3 explores open quantum systems, emphasizing their interactions with environments, unlike isolated closed systems. It introduces the concept of generalized measurements and mixed quantum states, reflecting the complex scenarios arising from these interactions. The chapter utilizes Positive Operator Valued Measures (POVMs) to describe generalized measurements, broadening the conventional approach to quantum measurements.

A significant focus is on the evolution of open systems through quantum channels, which illustrate the transfer or transformation of quantum information amid noise and external disturbances. This section underpins the dynamics open systems exhibit, critical for understanding quantum computing and information processing in realistic settings.

Through practical examples, the chapter elucidates how environmental factors influence quantum information, vital for applications in quantum technologies. It aims to equip readers with foundational knowledge of open quantum systems, highlighting their importance in the broader context of quantum mechanics.

Chapter 15 extensively examines the resource theory of asymmetry, focusing on the significance of asymmetry as a quantum resource, particularly in situations lacking a shared reference frame. The chapter begins by identifying the foundational elements, such as free states and operations within this theory, emphasizing their role in alignment of quantum reference frames. A significant part of the discussion revolves around the quantification of asymmetry, utilizing measures like the Fisher information and Wigner–Yanase–Dyson skew information to assess the degree of asymmetry in quantum states. The concept of G-twirling is introduced as a method to achieve symmetric states, serving as a key technique in analyzing and understanding asymmetry. Moreover, the chapter explores how asymmetry can enhance tasks like parameter estimation, leveraging the maximum likelihood method to improve precision.

Chapter 6 builds upon the foundation of divergences from Chapter 5, advancing into entropies and relative entropies with an axiomatic approach, and the inclusion of the additivity axiom. The chapter delves into the classical and quantum relative entropies, establishing their core properties and revealing the significance of the KL-divergence introduced in Chapter 5, notably characterized by asymptotic continuity. Quantum relative entropies are addressed as generalizations of classical ones, with a focus on the conditions necessary for these measures in the quantum framework. Several variants of relative entropies are discussed, including Renyi relative entropies and their extensions to quantum domain such as the Petz quantum Renyi divergence, minimal quantum Renyi divergence, and the maximal quantum Renyi divergence. This discourse underlines the relevance of continuity and its relation to faithfulness in relative entropies. The concept of entropy is portrayed as a measure with a broad spectrum of interpretations and applications across fields, from thermodynamics and information theory to cosmology and economics.

Chapter 5 delves into divergences and distance measures, which are crucial for comparing quantum states. It begins with classical divergences such as the Kullback–Leibler and Jensen–Shannon, then advances to their quantum counterparts, discussing their optimal characteristics. Influenced by quantum resource theories, these quantum extensions provide foundational insights into the robust tools of resource theories. The chapter concentrates on particular divergences that serve as true metrics, including the trace distance and a variant of the fidelity, and explores the concept of distance between subnormalized states, which is essential in the context of quantum measurements. It emphasizes the purified distance, a useful tool for understanding the entanglement cost of quantum systems, setting the stage for further exploration in later chapters. The chapter offers a mathematically approachable survey of these measures, underscoring their practical importance in quantum information theory.

This chapter introduces quantum resource theories (QRTs), tracing their evolution and key principles, starting from physics’ quest to unify distinct phenomena into a single framework. It highlights the unification of electricity and magnetism as a pivotal advancement, setting a precedent for QRTs in quantum information science. Quantum resource theories categorize physical system attributes as “resources,” notably transforming the role of quantum entanglement from mere theoretical interest to a crucial element in quantum communication and computation.

The chapter further describes the book’s layout and educational strategy, designed to offer a comprehensive understanding of QRTs. It explores the application of quantum resources in fields like quantum computing and thermodynamics, presenting a unique viewpoint on subjects such as entropy and nonlocality. Emphasizing on axiomatic beginning followed by practical uses, the book serves as a vital resource for both beginners and experts in quantum information science, preparing readers to navigate the complex terrain of QRTs and highlighting their potential to advance quantum science and technology.