The work includes the analyses of the theoretical capabilities of quantum-mechanical versus classical computer models in terms of their universality, their application domain, their efficacy in solving the problems and their technological feasibility. The concepts of deterministic Turing machine, probabilistic Turing machine and quantum-mechanical Turing machine are exposed. The concept of basic information unit of quantum-mechanical computer modelqubit is introduced and the qubit is represented through the use of Bloch sphere. The use of Dirac's bra-ket notation in description of quantum-mechanical state is explained, and the notation is used to form state equation of a quantummechanical Turing machine cell. Especially, consequences of quantum parallelism, quantum interference and wave function collapse are studied in respect to capabilities of quantum-mechanical computer models. The interrelationship between classes of problems that can be efficiently solved by quantum mechanical and/or classical computer models is displayed.
The work includes the analyses of the theoretical capabilities of quantum-mechanical versus classical computer models in terms of their universality, their application domain, their efficacy in solving the problems and their technological feasibility. The concepts of deterministic Turing machine, probabilistic Turing machine and quantum-mechanical Turing machine are exposed. The concept of basic information unit of quantum-mechanical computer modelqubit is introduced and the qubit is represented through the use of Bloch sphere. The use of Dirac's bra-ket notation in description of quantum-mechanical state is explained, and the notation is used to form state equation of a quantummechanical Turing machine cell. Especially, consequences of quantum parallelism, quantum interference and wave function collapse are studied in respect to capabilities of quantum-mechanical computer models. The interrelationship between classes of problems that can be efficiently solved by quantum mechanical and/or classical computer models is displayed.
The physics and the mathematics of computation are examined to provide a foundation and perspective for the investigation of the quantum mechanics of computation. Our purpose is to explore the fundamental limits and constraints imposed on computation by Nature through the laws of physics and the mathematics of computational complexity. Inasmuch as information storage and transmission are an integral part of computation, their physical bounds are considered. The computer is viewed both physically and mathematically as a dynamical system, and is depicted in terms of the basic Turing machine paradigm. Three fundamental classes of the Turing machine are defined; the deterministic, stochastic and quantum Turing machines. Hamiltonian models and physical realizations of quantum computing are described. Quantum computers can perform some tasks which have no classical analogue, but they cannot compute functions that are non-computable by classical means. Some classically intractable problems can be solved with quantum computers.
This presentation provides a comprehensive overview of the foundational principles of quantum computation. It begins by contrasting classical and quantum information, highlighting how quantum bits (qubits) leverage superposition, entanglement, and interference to process information in ways that surpass classical limits. The document explains the four postulates of quantum mechanics: state space (qubits as unit vectors in a Hilbert space), time evolution (via unitary operators), measurement (probabilistic collapse of the state), and composite systems (tensor products of subsystems). Key concepts such as the Bloch sphere representation, quantum gates (e.g., Hadamard, CNOT, Pauli gates), and quantum circuits are discussed, illustrating how quantum operations manipulate qubit states. The presentation also covers quantum evolution, the gate model for computation, and the universality of certain gate sets, emphasizing the potential for quantum parallelism and the transformative impact of quantum computing on fields like cryptography and optimization.
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
The article deals with new approaches in studying the features of the structure and principles of functioning of quantum computers, as one of the components of the elemental base of modern computer electronics, due to more efficient layout and supply of the corresponding lecture material using the multimedia presentation system. It is shown that at the present stage of the creation of quantum computers goes in two directions: specialized quantum computers that are aimed at solving one particular specific task, such as optimization tasks (quantum computers D-Wave); and universal quantum computers that are capable of implementing arbitrary quantum algorithms (Shore, Simon, Grover) - in implementing from IBM, Google, etc. The work of quantum registers is analyzed. When processing this material, students show that the ability of devices that are based on the classical principles of computing machines, in recent years reached a level in which most tasks can be solved relatively quickly and quite accurately. However, in various areas of knowledge, there are problems to decide which, including using the best supercomputers of the world, is not possible. After all, modern transistors have already reached a few nanometers, and in the future, electronic semiconductor components cannot be reduced infinitely, because their elements will consist of one atom. The development of classical computers in the form of cloud computing capacities is also limited in the area and consumed energy. In addition, not each algorithm is subject to parallelization. Effective processing of large volumes of data requires high-performance computations in the field of artificial intelligence, science-intensive industrial areas, modeling of chemical and physical phenomena and processes, that is, where it ceases to have the capabilities of modern supercomputers. Solving this problem is possible to apply the latest technologies of quantum computers. It has been shown that the quantum computer is a computing device that uses the phenomenon of quantum superposition (parallelism) and quantum confusion to transmit data and data processing, that is, the basis of its central processor is laid logic of quantum mechanics using special quantum algorithms. Such a computer is fundamentally different from a traditional computer that perceives any information in the form of a binary code, which may be in one of two possible states: 0 or 1. Any classic processor represents these two states of low or high voltage levels inside the semiconductor transistor. Its task to select zero or unit. Quantum processors are made according to the same principle, but a radical cancellation that instead of a transistor, they use kubit (Qubit) – quantum bits.
Abstract According to the Church-Turing Thesis, a Turing machine, and thus the programming languages and computers in existence, can simulate any reasonable computing device. However, there are physical devices that are exponentially more efficient than a Turing machine, and hence would prove exponentially difficult to simulate using such a machine. This chapter explores the concept of quantum computers and their potential not only to simulate quantum physics exponentially faster than classical computers but also to solve other problems exponentially faster. It considers algorithms that break cryptosystems, find needles in haystacks, and predict who can win a game. It first describes particles, waves, and amplitudes in quantum computation before turning to states and operators. It then examines how interference lets a quantum computer learn some things about a function, how quantum computers can solve factoring in polynomial time, the link between factoring and modern cryptography, graph isomorphism and the hidden subgroup problem, and quantum walks and scattering.
Quantum computing allows us to solve some problems much faster than existing classical algorithms. Yet, the quantum computer has been believed to be no more powerful than the most general computing model—the Turing machine. Undecidable problems, such as the halting problem, and unrecognizable inputs, such as the real numbers, are beyond the theoretical limit of the Turing machine. I suggest a model for a quantum computer, which is less general than the Turing machine, but may solve the halting problem for any task programmable on it. Moreover, inputs unrecognizable by the Turing machine can be recognized by the model, thus breaking the theoretical limit for a computational task. A quantum computer is not just a successful design of the Turing machine as it is widely perceived now, but is a different, less general but more powerful model for computing, the practical realization of which may need different strategies than those in use now.