It was back in spring 2014 when the author of this doctoral dissertation was finishing its master thesis, whose main objective was the understanding of Peter W. Shor’s most praised result, a quantum algorithm capable of factoring integers in polynomial time. During the development of this master thesis, me and my yet-tobe doctoral advisor studied the main aspects of quantum computing from a purely algebraic perspective. This research eventually evolved into a sufficiently thorough canvas capable of explaining the main aspects and features of the mentioned algorithm from within an undergraduate context. Just after its conclusion, we seated down and elaborated a research plan for a future Ph.D. thesis, which would expectantly involve quantum computing but also a branch of algebra whose apparently innocent definitions hide some really hard problems from a computational perspective: the theory of numerical semigroups. As will be seen later, the definition of numerical semigroup does not involve sophisticated knowledge from any somewhat obscure and distant branch of the tree of mathematics. Nonetheless, a number of combinatorial problems associated with these numerical semigroups are extremely hard to solve, even when the size of the input is relatively small. Some examples of these problems are the calculations of the Frobenius number, the Apéry set, and the Sylvester denumerant, all of them bearing the name of legendary mathematicians. This thesis is the result of our multiple attempts to tackle those combinatorial problems with the help of a hypothetical quantum computer. First, Chapter 2 is devoted to numerical semigroups and computational complexity theory, and is divided into three sections. In Section 2.1, we give the formal definition of a numerical semigroup, along with a description of the main problems involved with them. In Section 2.2, we sketch the fundamental concepts of complexity theory, in order to understand the true significance within the inherent hardness concealed in the resolution of those problems. Finally, in Section 2.3 we prove the computational complexity of the problems we aim to solve. Chapter 3 is the result of our outline of the theory of quantum computing. We give the basic definitions and concepts needed for understanding the particular place that quantum computers occupy in the world of Turing machines, and also the main elements that compose this particular model of computation: quantum bits and quantum entanglement. We also explain the two most common models of quantum computation, namely quantum circuits and adiabatic quantum computers. For all of them we give mathematical definitions, but always having in mind the physical experiments from which they stemmed. Chapter 4 is also about quantum computing, but from an algorithmical perspective. We present the most important quantum algorithms to date in a standardized way, explaining their context, their impact and consequences, while giving a mathematical proof of their correctness and worked-out examples. We begin with the early algorithms of Deutsch, Deutsch-Jozsa, and Simon, and then proceed to explain their importance in the dawn of quantum computation. Then, we describe the major landmarks: Shor’s factoring, Grover’s search, and quantum counting. Chapter 5 is the culmination of all previously explained concepts, as it includes the description of various quantum algorithms capable of solving the main problems inside the branch of numerical semigrops. We present quantum circuit algorithms for the Sylvester denumerant and the numerical semigroup membership, and adiabatic quantum algorithms for the Ap´ery Set and the Frobenius problem. We also describe a C++ library called numsem, specially developed within the context of this doctoral thesis and which helps us to study the computational hardness of all previously explained problems from a classical perspective. This thesis is intended to be autoconclusive at least in the main branches of mathematics in which it is supported; that is to say numerical semigroups, computational complexity theory, and quantum computation. Nevertheless, for the majority of concepts explained here we give references for the interested reader that wants to delve more into them.
It was back in spring 2014 when the author of this doctoral dissertation was finishing its master thesis, whose main objective was the understanding of Peter W. Shor’s most praised result, a quantum algorithm capable of factoring integers in polynomial time. During the development of this master thesis, me and my yet-tobe doctoral advisor studied the main aspects of quantum computing from a purely algebraic perspective. This research eventually evolved into a sufficiently thorough canvas capable of explaining the main aspects and features of the mentioned algorithm from within an undergraduate context. Just after its conclusion, we seated down and elaborated a research plan for a future Ph.D. thesis, which would expectantly involve quantum computing but also a branch of algebra whose apparently innocent definitions hide some really hard problems from a computational perspective: the theory of numerical semigroups. As will be seen later, the definition of numerical semigroup does not involve sophisticated knowledge from any somewhat obscure and distant branch of the tree of mathematics. Nonetheless, a number of combinatorial problems associated with these numerical semigroups are extremely hard to solve, even when the size of the input is relatively small. Some examples of these problems are the calculations of the Frobenius number, the Apéry set, and the Sylvester denumerant, all of them bearing the name of legendary mathematicians. This thesis is the result of our multiple attempts to tackle those combinatorial problems with the help of a hypothetical quantum computer. First, Chapter 2 is devoted to numerical semigroups and computational complexity theory, and is divided into three sections. In Section 2.1, we give the formal definition of a numerical semigroup, along with a description of the main problems involved with them. In Section 2.2, we sketch the fundamental concepts of complexity theory, in order to understand the true significance within the inherent hardness concealed in the resolution of those problems. Finally, in Section 2.3 we prove the computational complexity of the problems we aim to solve. Chapter 3 is the result of our outline of the theory of quantum computing. We give the basic definitions and concepts needed for understanding the particular place that quantum computers occupy in the world of Turing machines, and also the main elements that compose this particular model of computation: quantum bits and quantum entanglement. We also explain the two most common models of quantum computation, namely quantum circuits and adiabatic quantum computers. For all of them we give mathematical definitions, but always having in mind the physical experiments from which they stemmed. Chapter 4 is also about quantum computing, but from an algorithmical perspective. We present the most important quantum algorithms to date in a standardized way, explaining their context, their impact and consequences, while giving a mathematical proof of their correctness and worked-out examples. We begin with the early algorithms of Deutsch, Deutsch-Jozsa, and Simon, and then proceed to explain their importance in the dawn of quantum computation. Then, we describe the major landmarks: Shor’s factoring, Grover’s search, and quantum counting. Chapter 5 is the culmination of all previously explained concepts, as it includes the description of various quantum algorithms capable of solving the main problems inside the branch of numerical semigrops. We present quantum circuit algorithms for the Sylvester denumerant and the numerical semigroup membership, and adiabatic quantum algorithms for the Ap´ery Set and the Frobenius problem. We also describe a C++ library called numsem, specially developed within the context of this doctoral thesis and which helps us to study the computational hardness of all previously explained problems from a classical perspective. This thesis is intended to be autoconclusive at least in the main branches of mathematics in which it is supported; that is to say numerical semigroups, computational complexity theory, and quantum computation. Nevertheless, for the majority of concepts explained here we give references for the interested reader that wants to delve more into them.
A challenging question in the overlap of computer science, mathematics, and physics, is the exploration of potential capabilities of quantum computers. Milestones were the factoring algorithm of Shor (1994) and the search algorithm of Grover (1996). So far, major research was concentrated on discrete and algebraic problems. Much less was known about computational problems of analysis, including such a prominent example as high dimensional numerical integration, which is well-studied in the classical settings. We seek to understand how efficiently this and related problems can be solved in the quantum model of computation (i.e., on a quantum computer) and how the outcome compares to the efficiency of deterministic or randomized algorithms on a classical (i.e. non-quantum) computer. In this paper we give a survey of the state of the art in this field, including also a brief introduction to the general ideas of quantum computing. Introduction A quantum computer is a computing device based on quantum mechanical laws of the (sub)atomic world. The idea of such a computer was developed by Feynman [8] in 1982. He emphasized that simulating quantum mechanics on a classical computer is extremely hard, probably infeasible. So why not try to simulate quantum mechanics using quantum devices themselves. (Thoughts in this direction were also expressed by Manin [21] in 1980, see also [22].) In 1985 Deutsch [6] presented a formal model of computation for quantum computing.
The rapid evolution of computers in the half century since their invention has resulted in dramatically smaller and faster computers. However, from a computational point of view, all these computers look alike; for example, they are built out of simple logic gates. A fundamental thesis of computer science---the modern form of the Church--Turing thesis---asserts that this is inevitable in a deep sense. Any computer can be simulated with at most a polynomial factor slowdown by a probabilistic Turing machine. Quantum computation poses the first credible challenge to this thesis. It goes back to a suggestion by Feynman [4], who pointed out that there appears to be no efficient way of simulating a quantum mechanical system on a computer, and suggested that, perhaps, a computer based on quantum physical principles might be able to carry out the simulation efficiently. Two formal models for quantum computers---the quantum Turing machine [2] and quantum computational networks [3]---were defined by Deutsch. The first three papers in this issue describe efficient quantum algorithms for computational tasks that we do not know how to solve classically. In "Quantum Complexity Theory," Bernstein and Vazirani give the first formal evidence that quantum computers violate the modern form of the Church--Turing thesis. They show that a certain problem---the recursive Fourier sampling problem---can be solved in polynomial time on a quantum Turing machine, but relative to an oracle, requires superpolynomial time on a classical probabilistic Turing machine. Simon, in the paper "On the Power of Quantum Computation" introduces a fundamental projection technique and uses it to design an efficient quantum algorithm to determine whether a certain type of function is 2-1 or 1-1. He further shows that, relative to an oracle, this problem requires exponential time on a classical probabilistic Turing machine. In the paper "Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer," Shor gives remarkable polynomial time quantum algorithms for two of the most famous problems in computer science: factoring and discrete log. Since the computational hardness of these problems is the basis of several famous cryptosystems, Shor's paper very dramatically underlines the power of quantum computers. To understand the computational power of quantum computers, it is helpful to consider a quantum mechanical system of n particles, each of which can be in one of two states, labeled ∣0⟩ and ∣1⟩. If this were a classical system, then its instantaneous state could be described by n bits. However, in quantum physics, the system is allowed to be in a linear superposition of configurations, and indeed the instantaneous state of the system is described by a unit vector in the 2n dimensional vector space, whose basis vectors correspond to all the2n classical configurations. Therefore, to describe the instantaneous state of the system, we must specify 2n complex numbers. Nature must update 2n complex numbers at each instant to evolve the system in time. This is an extraordinary amount of effort, since even for n = 200, 2n is larger than estimates of the number of elementary particles in the visible universe. Nonetheless, there are limits to the power of quantum computers. In "Strengths and Weaknesses of Quantum Computing," Bennett, Bernstein, Brassard, and Vazirani show that, relative to a random oracle, with probability 1, the class NP cannot be solved on a quantum Turing machine in time o(2n/2). This bound is tight, since recent work of Grover [5] has shown how to accept any language in NP in time O(2n/2) on a quantum Turing machine. Quantum computers are necessarily time reversible. Indeed, Bennett's work [1] on reversible computation inspired early work on quantum computation that preceded Feynman's paper [4]. The reversibility requirement makes it quite complex to implement even basic computational primitives such as looping or composition. In "Quantum Complexity Theory," Bernstein and Vazirani show how to implement quantum programming primitives and give a construction for an efficient universal quantum Turing machine. The structure of the universal quantum Turing machine is quite simple: it consists of a deterministic Turing machine with a single "quantum coin flip." In "Quantum Computability," Adleman, DeMarrais, and Huang greatly simplify this further by showing that a very simple type of coin flip is sufficient---a rotation by an angle θ such that sinθ=3/5. Making quantum computers robust against noise and decoherence is an important and challenging problem. In "Stabilization of Quantum Computations by Symmetrization," Barenco, Berthiaume, Deutsch, Ekert, Jozsa, and Macchiavello show how to use the quantum watchdog effect to stabilize a quantum computation against noise. Their method is based on running several copies of the quantum computer in parallel and projecting its state into the symmetric subspace at frequent intervals. They show that the quantum watchdog effect results in the suppression of errors that lie outside the symmetric subspace. Quantum computation touches upon the foundations of both computer science and quantum physics. It is not unlikely that the issues raised by quantum computation will stimulate further research into the foundations of quantum physics. I wish to express my gratitude to several people who made this special section possible. Oded Goldreich acted as editor for two of the papers in the issue and dealt with them with his characteristic efficiency and judgment. The editorial staff at SIAM, most notably Lisa Dougherty, Beth Gallagher, Deidre Wunderlich, and Sam Young, were extremely helpful, patient, and resourceful. Finally, I would like to thank a number of referees whose careful and timely reviews were critical to putting together this issue. Umesh Vazirani
Quantum computing has emerged as one of the most transformative technological developments of the twenty-first century. By exploiting the fundamental principles of quantum mechanics—namely superposition, entanglement, and measurement—quantum computers offer the potential to address problems that are beyond the practical reach of classical computing systems. As the field advances from theoretical exploration to real-world implementation, the demand for clear, structured, and comprehensive educational resources continues to grow. This book presents a systematic and accessible introduction to quantum computing, beginning with the essential concepts of classical information and computation and progressively advancing toward modern quantum algorithms and communication protocols. The initial chapters establish a strong foundation in classical bits, logic gates, Boolean algebra, reversible logic, and computational complexity, providing the conceptual bridge necessary to understand quantum computation. The discussion then moves to the quantum bit (qubit), examining its behavior through superposition, measurement, Bloch sphere representation, and physical realization. Special emphasis is placed on linear algebra as the mathematical framework underpinning quantum computing. Through this approach, readers are guided to develop an intuitive yet rigorous understanding of quantum states, quantum gates, and circuit-based computation. The text further extends these ideas to multi-qubit systems, quantum entanglement, universal gate sets, and quantum error correction, integrating theoretical principles with practical insights. To support experiential learning, the book incorporates hands-on exposure to leading quantum programming platforms, including IBM Quantum, OpenQASM, and Qiskit. These sections connect theory with practice, enabling readers to design, simulate, and execute quantum circuits on both simulators and real quantum hardware. Subsequent chapters explore key quantum communication protocols such as superdense coding, quantum teleportation, and quantum key distribution, followed by an in-depth treatment of foundational quantum algorithms including Deutsch–Jozsa, Grover’s search, Simon’s algorithm, and Shor’s factoring algorithm. This text is intended for undergraduate and postgraduate students, research scholars, and industry professionals seeking a coherent and structured pathway into quantum computing. No prior knowledge of quantum mechanics is assumed, although familiarity with linear algebra and classical computation will enhance comprehension. Each chapter is carefully organized to promote conceptual clarity, supported by illustrative examples, circuit diagrams, and concise summaries. Beyond explaining the principles and mechanisms of quantum computation, this book aims to inspire readers to engage with the future of the field—whether through academic research, industrial application, or interdisciplinary innovation. As quantum technologies continue to evolve, it is hoped that this text will serve as a dependable guide and a meaningful entry point into the quantum era.
Quantum computing has emerged as one of the most transformative technological developments of the twenty-first century. By exploiting the fundamental principles of quantum mechanics—namely superposition, entanglement, and measurement—quantum computers offer the potential to address problems that are beyond the practical reach of classical computing systems. As the field advances from theoretical exploration to real-world implementation, the demand for clear, structured, and comprehensive educational resources continues to grow. This book presents a systematic and accessible introduction to quantum computing, beginning with the essential concepts of classical information and computation and progressively advancing toward modern quantum algorithms and communication protocols. The initial chapters establish a strong foundation in classical bits, logic gates, Boolean algebra, reversible logic, and computational complexity, providing the conceptual bridge necessary to understand quantum computation. The discussion then moves to the quantum bit (qubit), examining its behavior through superposition, measurement, Bloch sphere representation, and physical realization. Special emphasis is placed on linear algebra as the mathematical framework underpinning quantum computing. Through this approach, readers are guided to develop an intuitive yet rigorous understanding of quantum states, quantum gates, and circuit-based computation. The text further extends these ideas to multi-qubit systems, quantum entanglement, universal gate sets, and quantum error correction, integrating theoretical principles with practical insights. To support experiential learning, the book incorporates hands-on exposure to leading quantum programming platforms, including IBM Quantum, OpenQASM, and Qiskit. These sections connect theory with practice, enabling readers to design, simulate, and execute quantum circuits on both simulators and real quantum hardware. Subsequent chapters explore key quantum communication protocols such as superdense coding, quantum teleportation, and quantum key distribution, followed by an in-depth treatment of foundational quantum algorithms including Deutsch–Jozsa, Grover’s search, Simon’s algorithm, and Shor’s factoring algorithm. This text is intended for undergraduate and postgraduate students, research scholars, and industry professionals seeking a coherent and structured pathway into quantum computing. No prior knowledge of quantum mechanics is assumed, although familiarity with linear algebra and classical computation will enhance comprehension. Each chapter is carefully organized to promote conceptual clarity, supported by illustrative examples, circuit diagrams, and concise summaries. Beyond explaining the principles and mechanisms of quantum computation, this book aims to inspire readers to engage with the future of the field—whether through academic research, industrial application, or interdisciplinary innovation. As quantum technologies continue to evolve, it is hoped that this text will serve as a dependable guide and a meaningful entry point into the quantum era.
Lots of efforts in the last decades have been done to prove or disprove whether the set of polynomially bounded problems is equal to the set of polynomially verifiable problems. This paper will present an overview of the current beliefs of quantum complexity theorists and discussion detail the impacts these beliefs may have on the future of the field. We introduce a new form of Turing machine based on the concepts of quantum mechanics and investigate the domain of this novel definition for computation. The main object is to show the domain of these computation machines and the new definition of algorithms using these automata. In section one we give a brief fundamental overview of classical complexity theory, Turing machine and all the fundamental concepts required for the next chapters. In section two we describe various classes of classical complexity automata. In next chapter we introduce quantum complexity featuring quantum Turing machines and discuss its relationship to classical complexity. Section four presents a relationship between quantum complexity classes based on the quantum Turing machines and quantum oracles, and their relationship with corresponding classical complexity classes. And section five presents a discussion on the practical phenomena in problem solving using quantum computers. Finally in section five we speculate briefly on the direction we believe the field to be headed, and what might reasonably be expected in the future
[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".