This chapter provides the vocabulary in a less than formal way, with more formal treatments being detailed. Quantum computation is multidisciplinary drawing as it does from computer science, physics and mathematics. Technicalities of a field need not concern us further, and whenever we refer to a field generally, it may be helpful for you to have the fields R or C in mind. Loosely speaking this means that the real numbers obey rules, known as field axioms, that enable us to add, subtract, multiply and divide by any non-zero number, in the manner in which we are already very familiar. The chapter describes practice using some preliminary techniques, such as modular arithmetic, working with Boolean connectives, and equivalence relations that will be important as you move through the book.
This chapter provides the vocabulary in a less than formal way, with more formal treatments being detailed. Quantum computation is multidisciplinary drawing as it does from computer science, physics and mathematics. Technicalities of a field need not concern us further, and whenever we refer to a field generally, it may be helpful for you to have the fields R or C in mind. Loosely speaking this means that the real numbers obey rules, known as field axioms, that enable us to add, subtract, multiply and divide by any non-zero number, in the manner in which we are already very familiar. The chapter describes practice using some preliminary techniques, such as modular arithmetic, working with Boolean connectives, and equivalence relations that will be important as you move through the book.
Quantum computing uses a mathematical language that can seem unfamiliar to readers from programming, engineering and other technical backgrounds. Qubits are represented by vectors, quantum gates by matrices, measurement outcomes by eigenvalues, and probability amplitudes by complex numbers. Quantum Computing Preliminaries develops this language carefully, connecting each mathematical idea with its computational and physical meaning. The book begins with complex vector spaces and explains why quantum states require more than classical probabilities. It introduces qubits, superposition, normalisation, basis vectors and Dirac bra–ket notation, supported by worked calculations and geometric interpretation using the Bloch sphere. The discussion then turns to inner products and the geometry of quantum states. Readers learn how to calculate norms, test orthogonality, determine state overlap and apply the Born rule. Particular attention is given to complex conjugation and the distinction between the ordinary dot product and the Hermitian inner product used in quantum mechanics.The treatment of matrix operators establishes the roles of linear, unitary and Hermitian operators. It explains matrix multiplication, non-commutativity, eigenvalues, eigenvectors, reversibility and norm preservation. The Pauli and Hadamard gates provide concrete examples that link matrix algebra to rotations, the Bloch sphere, quantum-state evolution, and measurement. Outer products and projection operators are developed as tools for constructing operators, selecting state components and describing ideal projective measurements. The book examines idempotence, completeness, repeated measurement and the distinction between the ket–bra outer product and the tensor product used to form composite quantum systems. The final chapter explores changes of basis, operator representations and diagonalisation. It shows how the same physical state or operation can have different numerical representations in the computational and Hadamard bases. Readers derive the similarity transformation, use outer-product substitution, diagonalise Hermitian operators and interpret the spectral decomposition geometrically. Examples involving the Pauli-X, Pauli-Y, Pauli-Z and Hadamard operators demonstrate how basis choice exposes phase, population and measurement structure without changing the underlying physics. Throughout the book, Python and NumPy examples translate the mathematics into executable calculations. The programs demonstrate complex-vector operations, inner products, eigendecomposition, basis transformation and numerical verification while explaining the importance of using operations suited to complex Hilbert spaces. Each chapter includes conceptual, mathematical, computational and challenge exercises. Detailed solutions provide step-by-step derivations, program output and physical interpretation, allowing the book to support both guided study and independent practice. Quantum Computing Preliminaries is intended for students, software developers, engineers and technically minded readers preparing to study quantum circuits, algorithms, simulation, measurement, entanglement or error correction. It assumes familiarity with elementary algebra and programming but does not require prior training in quantum mechanics. Its purpose is to provide a clear mathematical foundation for understanding how quantum information is represented, transformed and measured.
This chapter discusses the complex numbers and to develop fluency in the manipulation. A knowledge of complex numbers is essential for understanding quantum computation. The set of all complex numbers, C, together with the operations of addition and multiplication as defined above form a mathematical structure called a field. The derivation requires the use of Maclaurin series which we discuss first. Maclaurin series expansions are a way of representing functions as an infinite sum of terms involving increasing powers of a variable x, say. Essentially, the statement that is a field means that we are able to add, subtract and multiply any complex number and we can divide by any non-zero complex number and still remain in the field.
Quantum Computation presents the mathematics of quantum computation. The purpose is to introduce the topic of quantum computing to students in computer science, physics and mathematics who have no prior knowledge of this field. The book is written in two parts. The primary mathematical topics required for an initial understanding of quantum computation are dealt with in Part I: sets, functions, complex numbers and other relevant mathematical structures from linear and abstract algebra. Topics are illustrated with examples focussing on the quantum computational aspects which will follow in more detail in Part II. Part II discusses quantum information, quantum measurement and quantum algorithms. These topics provide foundations upon which more advanced topics may be approached with confidence. Features A more accessible approach than most competitor texts, which move into advanced, research-level topics too quickly for today's students. Part I is comprehensive in providing all necessary mathematical underpinning, particularly for those who need more opportunity to develop their mathematical competence. More confident students may move directly to Part II and dip back into Part I as a reference. Ideal for use as an introductory text for courses in quantum computing. Fully worked examples illustrate the application of mathematical techniques. Exercises throughout develop concepts and enhance understanding. End-of-chapter exercises offer more practice in developing a secure foundation.
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.
Introduction Computation is an operation on symbols. We tend to perceive symbols as abstract entities, such as numbers or letters from a given alphabet. However, symbols are always represented by selected properties of physical objects. The binary string 10011011010011010110101101 may represent an abstract concept, such as the number 40 711 597, but the binary symbols 0 and 1 have also a physical existence of their own. It could be ink on paper (this is most likely to be how you see them when you are reading these words), glowing pixels on a computer screen (this is how I see them now when I am writing these words), or different charges or voltages (this is how my word processor sees them). If symbols are physical objects and if computation is an operation on symbols then computation is a physical process. Thus any computation can be viewed in terms of physical experiments, which produce outputs that depend on initial preparations called inputs. This sentence may sound very innocuous but its consequences are anything but trivial! On the atomic scale matter obeys the rules of quantum mechanics, which are quite different from the classical rules that determine the properties of conventional computers. Today’s advanced lithographic techniques can etch logic gates and wires less than a micrometer across onto the surfaces of silicon chips. Soon they will yield even smaller parts and inevitably reach a point at which logic gates are so small that they are made out of only a handful of atoms. So, if computers are to become smaller in the future, new, quantum technology must replace or supplement what we have now. The point is, however, that quantum technology can offer much more than cramming more and more bits onto silicon and multiplying the clock-speed of microprocessors. It can support an entirely new kind of computation, known as quantum computation, with qualitatively new algorithms based on quantum principles.
If you progressed up to this point, it should be easy for you to make the quantum leap. Several steps we've already made prepared the ground to discuss Quantum Computing. In previous chapters, we introduced the representation of bits as vectors in a vector space, and we generalized from two to a continuum of possible values, introducing concepts and notations that will ease our understanding. Now we will see what a quantum system is, then what a quantum computer is. We will see that quantum computations admit greater relativity of computation and have greater ambiguity than classical computations.