The article discusses the possibility of creating a vector processor based on quantum computing. The need to solve this problem follows from the fact that there is a wide class of problems that are formalized in vector and matrix algebras. Quantum parallelism allows one to simultaneously add a number of pairs of numbers. However, the main approaches in the literature are reduced to the creation of conventional adders like modern computers adders. The article proposes a method for constructing a quantum vector processor for adding k-dimensional (k - degree 2) vectors. The adder of such a processor consists of k, which contains a pair of coordinates in the interval [0, 1]. The computational experiment is given.
The article discusses the possibility of creating a vector processor based on quantum computing. The need to solve this problem follows from the fact that there is a wide class of problems that are formalized in vector and matrix algebras. Quantum parallelism allows one to simultaneously add a number of pairs of numbers. However, the main approaches in the literature are reduced to the creation of conventional adders like modern computers adders. The article proposes a method for constructing a quantum vector processor for adding k-dimensional (k - degree 2) vectors. The adder of such a processor consists of k, which contains a pair of coordinates in the interval [0, 1]. The computational experiment is given.
Introduction. Quantum computers provide several times faster solutions to several NP-hard combinatorial optimization problems in comparison with computing clusters. The trend of doubling the number of qubits of quantum computers every year suggests the existence of an analog of Moore's law for quantum computers, which means that soon they will also be able to get a significant acceleration of solving many applied large-scale problems. The purpose of the article is to review methods for creating algorithms of quantum computer mathematics for combinatorial optimization problems and to analyze the influence of the qubit-to-qubit coupling and connections strength on the performance of quantum data processing. Results. The article offers approaches to the classification of algorithms for solving these problems from the perspective of quantum computer mathematics. It is shown that the number and strength of connections between qubits affect the dimensionality of problems solved by algorithms of quantum computer mathematics. It is proposed to consider two approaches to calculating combinatorial optimization problems on quantum computers: universal, using quantum gates, and specialized, based on a parameterization of physical processes. Examples of constructing a half-adder for two qubits of an IBM quantum processor and an example of solving the problem of finding the maximum independent set for the IBM and D-wave quantum computers are given. Conclusions. Today, quantum computers are available online through cloud services for research and commercial use. At present, quantum processors do not have enough qubits to replace semiconductor computers in universal computing. The search for a solution to a combinatorial optimization problem is performed by achieving the minimum energy of the system of coupled qubits, on which the task is mapped, and the data are the initial conditions. Approaches to solving combinatorial optimization problems on quantum computers are considered and the results of solving the problem of finding the maximum independent set on the IBM and D-wave quantum computers are given. Keywords: quantum computer, quantum computer mathematics, qubit, maximal independent set for a graph.
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.
It has been proven that a quantum computer is superior to an electronic com puter in solving some NP problems. Based on quantum operations, this article proposes a new quantum sum and quantum multiplication, and then the floating point quantum multiplier and quantum sum are created on the basis of fixed point number operations. These studies lay the foundation for the quantum implementation of digital filters. This article provides a new way to calculate the summator on a quantum computer. This method uses the quantum Fourier transform (QFT) and reduces the number of qubits needed to be added by eliminating the need to temporarily transfer bits. This approach also allows you to add a classical number to a quantum superposition without encoding a classical number in a quantum register. This method also allows for mass parallelization during its execution. Adding and multiplying capabilities based on QFT are improved with some changes. The proposed operations are compared with the operations of close quantum arithmetic.
Summary form only given, as follows. Gives a system theoretic approach to the concept of quantum computing. A quantum computer is envisaged to be a system of quantum circuits, acting on a state space, which is a finite-say 2/sup n/-dimensional-complex Hilbert space. The circuitry is a sequence of unitary transforms U/sub t/ /spl epsiv/ SU(2/sup n/) followed by a measurement. These transforms, so-called quantum gates, are controlled by a classical computer, usually especially electromagnetic fields. The state space of a quantum computer has the structure of a Hermitian vector space. Thus it allows "simultaneous" superposition of orthogonal basis states (corresponding to classical states) with the possibility of constructive and destructive interference between different paths of computation. This principle allows the usage of so-called entangled states by preparing the superposition of special "bent" configurations of basis vectors in the product Hilbert space, e.g., as they are known from error control and cryptography. This latter "entanglement" not only promises to make a quantum computer much more powerful than a probabilistic one, but it also allows a method of parallelism. The reason for this lies not only in the fact that the 2/sup n/ dimensional Hilbert space is the n-fold tensor product of 2-dimensional spaces as it is given by spin-1/2-particles, like photons, representing a quantum bit. First of all, we shall address the fact that the power of quantum computing lies in the properties of entangled states as opposed to those of separable states which essentially would resemble classical circuits without giving the feature of exponential speed-up. We shall briefly describe possible physical realisations of quantum (optic, electronic, magnetic,...) devices allowing the engineering of Hamiltonians needed for quantum systems. Basic quantum gates, especially the quantum Horner-Toffoli gate will be discussed to display the principles and methodology for designing quantum circuits. An outlook towards the possible availability of nano- and mesoscopic technologies supporting this new architecture in future generations concludes based on a speculative worst/best case forecast of possible applications, e.g., in public key and conventional cryptography.
Quantum computing (Q-computing) is a revolutionary technology that applies quantum mechanics (QM) to implement calculations significantly quicker than traditional computers. Q-computing employs Q-bits (qubits) rather than classical bits, which can take on a superposition state, making it possible to represent both 0 and 1 at once. This distinctive property allows qubits to perform multiple calculations simultaneously, making Q computers exponentially more powerful as more qubits are added, compared to the linear scaling of classical computers. However, challenges such as qubit instability and error rates remain significant obstacles to fully realizing this potential. A vital aspect of this technology is the development of Q arithmetic circuits, which act as the foundational blocks for efficiently executing Q algorithms. Q half-adder and full-adder circuits are integral to performing fundamental arithmetic operations within Q computers. In contrast to classical adders, Q adders rely on Q gates such as Controlled NOT (CNOT) and Toffoli gates. These gates exploit Q phenomena like entanglement and superposition, allowing for more efficient computations. The Q half-adder consists of two qubits and adds two single bits, producing a carry and sum output. It utilizes the CNOT gate to compute the sum and the Toffoli gate (controlled-controlled-NOT [CCNOT]) to compute the carry. The Q full-adder builds on this, adding three qubits to produce a sum and a carry output, further increasing computational capacity. Through the entanglement of qubits, these circuits allow for more parallelism and speed compared to classical adders. Q carry-look-ahead adders (QCLAs) and Q ripple-carry adders (QRCAs) are used to perform more complex arithmetic operations based on these half-adder and full-adder structures. Q arithmetic circuits have broad applications, boosting secure key generation in cryptography, speeding up matrix operations for faster model training in machine learning, and enabling more precise, efficient image transformations in Q image processing, benefiting fields like medical imaging and computer vision.