The immense power of computing has been continually growing, yet we still want more speed and computing capacity. If, as Moores Law states, the number of transistors on a microprocessor continues to double every other year, we will find the circuits on a microprocessor measured on an atomic scale in a couple of decades. Then, we would start to consider quantum computers, which will make use of the power of atoms to perform memory and processing tasks. The mathematics behind quantum computers will be the focus of my presentation. The information contained in qubits (quantum bits), which are analogues of classical bits, can be represented in a matrix form, namely a density matrix. It has been found that a density matrix is positive semi-definite and of trace 1. The operators acting on density matrices need to preserve the trace and positivity so that the resulting output after processing the information is still a density matrix. Therefore, these operators can be thought of as a trace-preserving positive map. In this presentation, the definitions of such maps and properties will be introduced along with the theorems that are related to completely positive maps.
The immense power of computing has been continually growing, yet we still want more speed and computing capacity. If, as Moores Law states, the number of transistors on a microprocessor continues to double every other year, we will find the circuits on a microprocessor measured on an atomic scale in a couple of decades. Then, we would start to consider quantum computers, which will make use of the power of atoms to perform memory and processing tasks. The mathematics behind quantum computers will be the focus of my presentation. The information contained in qubits (quantum bits), which are analogues of classical bits, can be represented in a matrix form, namely a density matrix. It has been found that a density matrix is positive semi-definite and of trace 1. The operators acting on density matrices need to preserve the trace and positivity so that the resulting output after processing the information is still a density matrix. Therefore, these operators can be thought of as a trace-preserving positive map. In this presentation, the definitions of such maps and properties will be introduced along with the theorems that are related to completely positive maps.
Quantum computing (QC) is an exciting new technology that completes the text with an application that relies heavily on the mathematics of linear algebra. There is more to explain than the conversion of common matrix notation into Dirac bra-ket notation. There is also the bizarre world of quantum physics that is inspiring a range of quantum information technologies. Qubits and their entangled superposition states are introduced to show that the “quantum advantage” stems from qubits storing exponentially more information that can be processed at exponentially faster rates. The process of preparing, modifying, and measuring qubit states is explained. Linear algebra skills are exercised by representing QC coding through the application of unitary operators to qubit states. QC algorithms are designed by diagramming quantum circuits, casting those circuits into sequences of operators applied to qubit states, and converting the operators into matrix forms. These are the tools of QC. Several algorithms that demonstrate a range of computational tasks from simple arithmetic through quantum Fourier analysis complete the chapter.
In the preceding chapter we established that a suitable set of quantum gates, complemented by quantum error correction, allows us to produce a desired multiqubit unitary transformation. This transformation is one of the three steps in a quantum computation; the others, of course, are the preparation of the qubits in their initial state and the measurement of them after the transformation has been implemented. A quantum computation is designed to solve a problem or class of problems. The power of quantum computers is that they can do this, at least for some problems, very much more efficiently and quickly than any conventional computer based on classical logic operations. If we can build a quantum computer then a number of important problems which are currently intractable will become solvable. The potential for greatly enhanced computational power is, in itself, reason enough to study quantum computers, but there is another. Moore’s law is the observation that the number of transistors on a chip doubles roughly every eighteen months. A simple corollary is that computer performance also doubles on the same timescale. Associated with this exponential improvement is a dramatic reduction in the size of individual components. If the pace is to be kept up then it is inevitable that quantum effects will become increasingly important and ultimately will limit the operation of the computer. In these circumstances it is sensible to consider the possibility of harnessing quantum effects to realize quantum information processors and computers. We start with a brief introduction to the theory of computer science, the principles of which underlie the operation of what we shall refer to as classical computers. These include all existing machines and any based on the manipulation of classical bits. The development of computer science owes much to Turing, who devised a simple but powerful model of a computing device: the Turing machine. It its most elementary form, this consists of four elements. (i) A tape for data storage, which acts as a memory. This tape has a sequence of spaces, each of which has on it one of a finite set of symbols. (ii) A processor, which controls the operations of the machine.
Q uantum mechanics has had an enormous technological and societal impact.To grasp this point, it is sufficient to cite the invention of the transistor, perhaps the most remarkable among the countless other applications of quantum mechanics.It is also easy to see the enormous impact of computers on everyday life.The importance of computers is such that it is appropriate to say that we are now living in the information age.This information revolution became possible thanks to the invention of the tran sistor, that is, thanks to the synergy between computer science and quantum physics.Today this synergy offers completely new opportunities and promises exciting advances in both funda mental science and technological application.We are referring here to the fact that quantum mechanics can be used to process and transmit information [1,2].Miniaturization provides us with an intuitive way of under standing why, in the near future, quantum laws will become important for computation.The electronics industry for com puters grows hand-in-hand with the decrease in size of integrated circuits.This miniaturization is necessary to increase computa tional power, that is, the number of floating-point operations per second (flops) a computer can perform.In the 1950's, electronic computers based on vacuum-tube technology were capable of performing approximately 103 floating-point operations per second, while nowadays there exist supercomputers whose power is greater than lOteraflops (1013 flops).As we have remarked, this enormous growth of computational power has been made possi ble owing to progress in m in ia tu riz a tio n , w hich m ay be quantified empirically in Moore's law.This law is the result of a remarkable observation made by Gordon Moore in 1965: the number of transistors on a single integrated-circuit chip doubles approximately every 18 24 months.This exponential growth has not yet saturated and Moore's law is still valid.At the present time the limit is approximately 108 transistors per chip and the typical size of circuit components is of the order of 100 nanometres.Extrapolating Moore's law, one would estimate that around the year 2020 we shall reach the atomic size for storing a single bit of inform atio n .At th a t p o in t, q u a n tu m effects will becom e unavoidably dominant.One should be aware that, besides quantum effects, other fac tors could bring Moore's law to an end.In the first place, there are economic considerations.Indeed, the cost of building fabri cation facilities to m an u factu re chips has also increased exponentially with time.Nevertheless, it is important to under stand the ultimate limitations set by quantum mechanics.Even though we might overcome economic barriers by means of tech nological breakthroughs, quantum physics sets fundamental limitations on the size of the circuit components.The first ques tion under debate is whether it would be more convenient to push References
Modern computing has achieved extraordinary scale and speed. Electronic computers emerged commercially in the mid-20th century, and classical computing has since scaled through architectural advances and parallelism. However, many scientifically important problems remain computationally expensive because the underlying state spaces grow exponentially. Quantum computing addresses this challenge by representing and transforming information using quantum-mechanical state vectors and unitary operations. These conventional systems are referred to as classical computers, capable of handling everyday tasks with moderate computational power and speed. Classical computers represent information using bits and process it using deterministic logic operations. While modern classical systems use extensive parallelism (multi-core CPUs, GPUs, and clusters), many important problems still scale poorly because the resources required grow rapidly with input size. Even with significant parallelism, complex problems that involve large combinatorial structures or high-dimensional state spaces often require substantial computational time and resources, motivating the need for more powerful computational paradigms capable of handling such workloads.
The first computer originated as an ordinary calculator in 19th century. Subsequently, the rapid evolution of computers began. The massive amount of processing power generated by computer manufacturers has always failed to quench the thirst for speed and computing capacity. If, as Moore's Law states, the number of transistors on a microprocessor continues to double every 18 months, then soon we will find the circuits on a microprocessor being measured on an atomic scale. Today's advanced lithographic techniques can squeeze fraction of micron wide logic gates and wires onto the surface of silicon chips. Thus it can be seen that very soon we will be facing the need to create quantum computers which can harness the power of atoms and molecules to perform memory and processing tasks. Quantum computers have the potential to perform calculations a billion times faster than any silicon-based computer. Also, theories suggest that every physical object, even the universe, is in some sense a quantum computer. If this is the case, then according to Turing's work which says that all computers are functionally equivalent; computers should be able to model every physical process. Scientists have already built basic quantum computers that can perform certain calculations; but a practical quantum computer is still years away. In this paper, we will be discussing about the history, development and the future scope of quantum computing. The pros and cons of this future technology have also been compared and our analysis has been put forth.
In Part II of this book we show how computations can be implemented using quantum systems. As we will see, the differences between bits and qubits, briefly outlined in Part I, lead to some important consequences for quantum computing. We begin with a brief introduction to the fundamental principles underlying quantum computing: specific implementations will be considered in later chapters. Reversible computing While quantum computation in its modern form is still a relatively young discipline, researchers have been interested in the relationship between quantum mechanics and computing for a long time. Early workers were not interested in the ideas of quantum parallelism , which will be explored in the next section, but rather in the question of whether explicitly quantum mechanical systems could be used to implement classical computations. In addition to its intrinsic interest, there are two technological reasons why this might be considered an important question. The first reason is a direct consequence of Moore's laws. After the development of integrated circuits, computing technology began its headlong dash down the twin roads of ever-faster and ever-smaller devices. These two phenomena are closely related: as computing devices must communicate within themselves, and as the speed of information transfer cannot exceed the speed of light, faster computers must indeed be smaller. There is, however, a limit to this process, defined by the atomic scale: once the size of individual transistors becomes comparable with that of atoms, the old-fashioned approach of micro-electronics becomes completely untenable.