Quantum computing is a relatively new field of computing that has the potential to greatly speed up the solution of certain problems. This book is about quantum algorithms, with a focus on algorithms that may be useful for mathematical optimization. Quantum computers work in a way that is fundamentally different from classical computers. We begin by introducing the model of computation, and then proceed to study several quantum algorithms. In the following, the term “classical” is used to mean “nonquantum,” as is common in the field.
Quantum computing is a relatively new field of computing that has the potential to greatly speed up the solution of certain problems. This book is about quantum algorithms, with a focus on algorithms that may be useful for mathematical optimization. Quantum computers work in a way that is fundamentally different from classical computers. We begin by introducing the model of computation, and then proceed to study several quantum algorithms. In the following, the term “classical” is used to mean “nonquantum,” as is common in the field.
Quantum computing represents a fundamentally new paradigm in information processing, in which information is encoded and manipulated using two-level quantum systems known as qubits, whose evolution is governed by the principles of quantum mechanics. This chapter presents a structured introduction to the theoretical foundations of quantum computation, beginning with the limitations of classical computation and the physical constraints that motivate the need for a quantum model of computation. We develop the mathematical framework required to describe quantum information, introducing qubits as state vectors in Hilbert space and examining the roles of superposition, measurement, and unitary evolution. Single-qubit operations are presented through the Pauli and Hadamard gates, along with their geometric interpretation on the Bloch sphere. Building on this formalism, the discussion is extended to multi-qubit systems using tensor products, and quantum entanglement is introduced as a non-classical correlation fundamental to quantum information processing. The chapter then explores quantum algorithms, focusing on Grover’s unstructured search algorithm, to demonstrate how superposition, interference, and entanglement can be harnessed to achieve computational advantages over classical approaches. Finally, the chapter discusses applications of quantum computation, including quantum simulation, cryptography, search and optimization problems, and near-term hybrid quantum algorithms such as the Variational Quantum Eigensolver (VQE)and the Quantum Approximate Optimization (QAO) Algorithm, highlighting their relevance in the noisy intermediate-scale quantum (NISQ) era.
The field of quantum computing is in the midst of a period of transformation as a result of the recent discovery of quantum entanglement. It is designed to perform computations that classical computers cannot handle since it is based on and operates in compliance with the rules of quantum mechanics. Because it operates on certain premises, it enables this to happen. Quantum bits, also known as qubits, have the unique ability to represent many states at once, unlike their conventional analogues. However, classical bits can only take on the values 0 or 1, and no other values in between. Quantum bits are capable of existing in a wide variety of states simultaneously. As a result of their unique ability to explore multiple solutions at once, quantum computers have the potential to dramatically boost the processing speed with which certain problems may be solved. Furthermore, qubits can show entanglement, in which the state of one qubit becomes intertwined with the state of another qubit, even if the qubits are separated by a great distance. Quantum entanglement describes this strange occurrence. This entanglement provides the way for the creation of new algorithms and enhanced computing power by simplifying the execution of complex interactions. Although difficulties like qubit stability and error correction must be overcome, quantum computing has the potential to revolutionize many different areas, including cryptography, optimization, and scientific discovery. Although significant strides have been made, the implementation of large-scale quantum computers remains in its infancy, necessitating ongoing research and development.
QUANTUM COMPUTING A helpful introduction to all aspects of quantum computing Quantum computing is a field combining quantum mechanics—the physical science of nature at the scale of atoms and subatomic particles—and information science. Where ordinary computing uses bits, logical values whose position can either be 0 or 1, quantum computing is built around qubits, a fundamental unit of quantum information which can exist in a superposition of both states. As quantum computers are able to complete certain kinds of functions more accurately and efficiently than computers built on classical binary logic, quantum computing is an emerging frontier which promises to revolutionize information science and its applications. This book provides a concise, accessible introduction to quantum computing. It begins by introducing the essentials of quantum mechanics that information and computer scientists require, before moving to detailed discussions of quantum computing in theory and practice. As quantum computing becomes an ever-greater part of the global information technology landscape, the knowledge in Quantum Computing will position readers to join a vital and highly marketable field of research and development. The book’s readers will also find: Detailed diagrams and illustrations throughout A broadly applicable quantum algorithm that improves on the best-known classical algorithms for a wide range of problems In-depth discussion of essential topics including key distribution, cluster state quantum computing, superconducting qubits, and more Quantum Computing is perfect for advanced undergraduate and graduate students in computer science, engineering, mathematics, or the physical sciences, as well as for researchers and academics at the intersection of these fields who want a concise reference.
A computer is a physical device that helps us process information by executing algorithms. An algorithm is a well-defined procedure, with finite description, for realizing an information-processing task. An information-processing task can always be translated into a physical task. When designing complex algorithms and protocols for various information-processing tasks, it is very helpful, perhaps essential, to work with some idealized computing model. However, when studying the true limitations of a computing device, especially for some practical reason, it is important not to forget the relationship between computing and physics. Real computing devices are embodied in a larger and often richer physical reality than is represented by the idealized computing model. Quantum information processing is the result of using the physical reality that quantum theory tells us about for the purposes of performing tasks that were previously thought impossible or infeasible. Devices that perform quantum information processing are known as quantum computers. In this book we examine how quantum computers can be used to solve certain problems more efficiently than can be done with classical computers, and also how this can be done reliably even when there is a possibility for errors to occur. In this first chapter we present some fundamental notions of computation theory and quantum physics that will form the basis for much of what follows. After this brief introduction, we will review the necessary tools from linear algebra in Chapter 2, and detail the framework of quantum mechanics, as relevant to our model of quantum computation, in Chapter 3. In the remainder of the book we examine quantum teleportation, quantum algorithms and quantum error correction in detail. We are often interested in the amount of resources used by a computer to solve a problem, and we refer to this as the complexity of the computation. An important resource for a computer is time. Another resource is space, which refers to the amount of memory used by the computer in performing the computation. We measure the amount of a resource used in a computation for solving a given problem as a function of the length of the input of an instance of that problem.
[eng] This thesis can be classified into the research field of quantum algorithms. A typical paper in this field usually focuses on what kind of things we can do with a quantum computer, whether they are useful or advantageous compared to classical computers, and how we can do it more efficiently. Since the ideal fault-tolerant large-scale quantum computer will not be available soon, many papers focus on noisy intermediate-scale quantum (NISQ) devices, how to characterize them through various benchmarking protocols, and how to make them useful through error mitigation or variational algorithms. Most probably, anyone who ends up opening this thesis is already an expert in certain topics, but they can still find some “out of the box" elements in one chapter or another. The results presented in the thesis might not be directly useful, but hopefully, these elements can provide insights and inspiration. The main body of this thesis is organized into six independent chapters. Each chapter is complete and self-consistent. All these chapters share a similarity: They provide some mathematical ideas of quantum computing in one aspect or another. Since part of the research is conducted in collaboration, the pronoun “we" is used throughout the work. In the first chapter, we focus on actions that transform quantum states into quantum states. We begin with a more theoretical approach. These actions naturally process a group structure. And we ask, under what conditions is this group finite? Given an arbitrary finite group, how can we construct these actions? How can these actions be useful to quantum computing? The set of these actions is named "Quantum representation of finite group," and answers to these questions are provided in the main text. Then, in the second chapter, we switch to a more practical scenario. The inspiration came from a problem that appeared in post-quantum cryptography, the dihedral coset problem, which uses quantum algorithms to recover a secret of the group hidden in quantum states. We simplified this problem and designed an interactive protocol for verifying the computational capacity of a quantum device. Holding a small quantum computer, the verifier prepares quantum state samples that encode a secret and sends them one by one to the prover via a one-way quantum channel. The prover then uses his quantum device to recover the secret with a certain probability. The device’s capacity is then reflected in the accuracy of the solution. This interactive protocol can be used locally on a single device as a benchmarking protocol. In the third chapter, we explore quantum entanglement. We present a new method for visualizing entanglement during a quantum evolution, such as the execution of a quantum algorithm. We name this method "Entanglement trajectory" and show that it can provide a "fingerprint" for each quantum process. We determine the boundaries of these trajectories with Random Matrix Theory and use different examples and entanglement measurements to show that this trajectory has an intrinsic quantum meaning and provides more information about the system. In the fourth chapter, we elaborate on the entanglement presented in a succession of numbers. First, we give a numerical and analytical description of the average entropy of succession, which can provide a standard for studying any successions. Then, we simulate the example of k-almost primes and its unions for different system sizes and explore their particular feature. In the fifth chapter, we will show three different methods of using a quantum annealer to solve Multivariate Quadratic (MQ) problems. In the sixth chapter, we present a data encoder in the fixed Hamming weight subspace, which is parameter-optimal and can be extended to a sparse and binary data encoder. We will give a small summary for each chapter. At the end of this thesis, we will provide an overall conclusion for our readers as “take-home messages".