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Journal articles on the topic 'Quantum computer'

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Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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1

Rachappa, Halkar. "Studying the Concept of Quantum Computing and Analysis of Its Components, Benefits and Challenges." International Journal on Recent and Innovation Trends in Computing and Communication 8, no. 11 (November 30, 2020): 17–22. http://dx.doi.org/10.17762/ijritcc.v8i11.5517.

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This paper will discuss about the use of quantum computing in the computer engineering field to make computers reliable and fast to perform various typical computations. It is the process of using concept of quantum theory in computing algorithms and other computations and calculations along with the use of computer systems. The computer system always understands the binary language of bits and bytes or in other words we can say that everything which is computed using computers should be first converted into stream of 0’s and 1’s so that computer can understand and then perform the calculations. This will restrict the scope of certain calculations for the computers. Scientists and engineers have come together to implement the quantum computing along with computers and due to this they are able to make calculations which were not possible before its introduction. The quantum theory uses bits and qubits of quantum theory and allows them to be available in more than one state and make possible various typical calculations easy and fast. The paper will explain various types of quantum computing techniques and how they are useful for the organisations. The benefits and challenges of the quantum computing in the field of computer systems will also be discussed.

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2

Caicedo Ortiz, H. E., E. Santiago Cortés, and D. A. Mantilla Sandoval. "Construyendo compuertas cuánticas con IBM’s cloud quantum computer." Journal de Ciencia e Ingeniería 9, no. 1 (August 31, 2017): 42–56. http://dx.doi.org/10.46571/jci.2017.1.7.

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En este artículo, se describe de manera didáctica los elementos esenciales que permiten realizar cálculos elementales en un computador cuantico. Revisamos las características de las compuertas cuánticas más relevantes de 1-qubit y 2-qubits, ademas de implementarlas en el computador cuántico de IBM.

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3

Bukashkin, S. А., and М. А. Cherepniov. "Quantum Computer and Post-Quantum Cryptography." Programmnaya Ingeneria 12, no. 4 (July 14, 2021): 171–78. http://dx.doi.org/10.17587/prin.12.171-178.

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An overview of the current state of the problem of building a quantum computer and its hypothetical use for breaking cryptographic protocols is presented. The necessary parameters are considered. An overview of existing quantum algorithms and post-quantum cryptographic protocols that are strong with respect to them is presented. The problem of constructing a quantum computer is considered in comparison with the development of the theory and practice of conventional mechanical and electronic computers. The results of contests on the topic of post-quantum cryptography are presented.

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4

Kendon, Vivien M., Kae Nemoto, and William J. Munro. "Quantum analogue computing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1924 (August 13, 2010): 3609–20. http://dx.doi.org/10.1098/rsta.2010.0017.

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We briefly review what a quantum computer is, what it promises to do for us and why it is so hard to build one. Among the first applications anticipated to bear fruit is the quantum simulation of quantum systems. While most quantum computation is an extension of classical digital computation, quantum simulation differs fundamentally in how the data are encoded in the quantum computer. To perform a quantum simulation, the Hilbert space of the system to be simulated is mapped directly onto the Hilbert space of the (logical) qubits in the quantum computer. This type of direct correspondence is how data are encoded in a classical analogue computer. There is no binary encoding, and increasing precision becomes exponentially costly: an extra bit of precision doubles the size of the computer. This has important consequences for both the precision and error-correction requirements of quantum simulation, and significant open questions remain about its practicality. It also means that the quantum version of analogue computers, continuous-variable quantum computers, becomes an equally efficient architecture for quantum simulation. Lessons from past use of classical analogue computers can help us to build better quantum simulators in future.

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5

Wang, Junchao, Guoping Guo, and Zheng Shan. "SoK: Benchmarking the Performance of a Quantum Computer." Entropy 24, no. 10 (October 14, 2022): 1467. http://dx.doi.org/10.3390/e24101467.

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The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer’s benchmarking and propose setting up the QTOP100.

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6

Liu, Xiaonan, Ming He, Junchao Wang, Haoshan Xie, and Chenyan Zhao. "Automated Quantum Volume Test." Journal of Physics: Conference Series 2221, no. 1 (May 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2221/1/012029.

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Abstract As a benchmark for the overall performance of quantum computers, quantum volume has the advantage of being able to reflect the depth of running quantum circuits. But, the quantum volume test code provided by IBM needs to be executed manually, and the simulation result of the quantum simulator is used as the result of the volume test, so that users cannot quickly and accurately test the quantum volume of the actual quantum computer required. In response to this problem, this paper designs an automated quantum volume test program. The program automatically generates quantum volume sequences, selects the number of executions of quantum circuits, and defines real quantum computers to facilitate users to perform quantum volume tests on quantum computers provided by the IBM Quantum Cloud Platform. Simultaneously, according to the automated test program, the quantum volume of IBM’s four small superconducting quantum computers was tested. The test results show that (1) the quantum computer is different, and the qubit layout and execution times ntrials are the same, will cause the quantum volume is uncertain; (2) the same quantum computer, whether ntrials is the same, the robustness of qubit coupling will be affected to a certain extent.

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7

Ding, Yongshan, and Frederic T. Chong. "Quantum Computer Systems: Research for Noisy Intermediate-Scale Quantum Computers." Synthesis Lectures on Computer Architecture 15, no. 2 (June 16, 2020): 1–227. http://dx.doi.org/10.2200/s01014ed1v01y202005cac051.

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8

Borisevich, M. N., and V. I. Kozlovsky. "ABOUT QUANTUM COMPUTER AND QUANTUM MEDICINE." Vestnik of Vitebsk State Medical University 20, no. 2 (April 15, 2021): 18–24. http://dx.doi.org/10.22263/2312-4156.2021.2.18.

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The foundations of quantum physics have been laid by Max Planck, who suggested that energy couldn’t be absorbed and radiated continuously, but only in separate portions - these portions were called quanta. His ideas were confirmed in numerous physical experiments on the photo effect, the structure of the atom and atomic nucleus, brilliantly performed by Bohr and Rutherford. All this in the aggregate made it possible to eliminate the border between matter and waves, predicted by Louis de Broil. In this way the foundations of quantum mechanics were laid = Heisenberg and Schrödinger did this work. Many manifestations of quantum physics can already be observed in everyday life. These are optical quantum generators, computer CDs, and integrated circuits and lots and lots of this. In recent years, the researchers have drawn their attention to other quantum physics applications related to queries. By their design, this work will be carried out in the future by quantum computers. The article presents a short report on the quantum computer and the prospects for its use in quantum medicine.

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9

KWEK, L. C., and ARTUR EKERT. "QUANTUM COMPUTER: HOW FEASIBLE IS THE IDEA?" COSMOS 02, no. 01 (May 2006): 101–10. http://dx.doi.org/10.1142/s0219607706000195.

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The need for increased memory space and higher speed in computers has fueled the demand for smaller and faster computers. However, as the computer chips miniaturize, it becomes inevitable that we need to look at the possibility of manipulating and addressing atoms and molecules individually. One such possibility is a feasibility study of a quantum computer. In this report, we summarize some of the progress made in experimental realization of quantum computer in the last few years.

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10

MIHARA, Takashi, and Tetsuro NISHINO. "Quantum Computer." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 1 (1998): 11–20. http://dx.doi.org/10.3156/jfuzzy.10.1_11.

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11

INAKAZU, Kosuke, Ryosuke TAKAHASHI, Daiji KOBUSE, Yuma YAZAKI, Junpei TAKEHARA, and Ryuta SOMEYA. "Quantum Computer." Journal of the Institute of Electrical Engineers of Japan 135, no. 12 (2015): 843–46. http://dx.doi.org/10.1541/ieejjournal.135.843.

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12

Sparkes, Matthew. "Quantum computer helps design a better quantum computer." New Scientist 251, no. 3353 (September 2021): 11. http://dx.doi.org/10.1016/s0262-4079(21)01674-2.

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13

Ivancova, Olga, Vladimir Korenkov, Olga Tyatyushkina, Sergey Ulyanov, and Toshio Fukuda. "Quantum supremacy in end-to-end intelligent IT. PT. III. Quantum software engineering – quantum approximate optimization algorithm on small quantum processors." System Analysis in Science and Education, no. 2 (2020) (June 30, 2020): 115–76. http://dx.doi.org/10.37005/2071-9612-2020-2-115-176.

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Principles and methodologies of quantum algorithmic gate-based design on small quantum computer described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. A new approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. SW & HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation on small quantum programmable computer algorithm gate (that can program in SW to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates) described

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14

Korolyov, Vyacheslav, and Oleksandr Khodzinskyi. "Solving Combinatorial Optimization Problems on Quantum Computers." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 5–13. http://dx.doi.org/10.34229/2707-451x.20.2.1.

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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.

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15

AKL, SELIM G. "THREE COUNTEREXAMPLES TO DISPEL THE MYTH OF THE UNIVERSAL COMPUTER." Parallel Processing Letters 16, no. 03 (September 2006): 381–403. http://dx.doi.org/10.1142/s012962640600271x.

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It is shown that the concept of a Universal Computer cannot be realized. Specifically, instances of a computable function [Formula: see text] are exhibited that cannot be computed on any machine [Formula: see text] that is capable of only a finite and fixed number of operations per step. This remains true even if the machine [Formula: see text] is endowed with an infinite memory and the ability to communicate with the outside world while it is attempting to compute [Formula: see text]. It also remains true if, in addition, [Formula: see text] is given an indefinite amount of time to compute [Formula: see text]. This result applies not only to idealized models of computation, such as the Turing Machine and the like, but also to all known general-purpose computers, including existing conventional computers (both sequential and parallel), as well as contemplated unconventional ones such as biological and quantum computers. Even accelerating machines (that is, machines that increase their speed at every step) cannot be universal.

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16

Gordon, Michal, and Goren Gordon. "Quantum computer games: quantum minesweeper." Physics Education 45, no. 4 (June 23, 2010): 372–77. http://dx.doi.org/10.1088/0031-9120/45/4/008.

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17

Zhu, D., N. M. Linke, M. Benedetti, K. A. Landsman, N. H. Nguyen, C. H. Alderete, A. Perdomo-Ortiz, et al. "Training of quantum circuits on a hybrid quantum computer." Science Advances 5, no. 10 (October 2019): eaaw9918. http://dx.doi.org/10.1126/sciadv.aaw9918.

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Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swarm and Bayesian optimization to this task. We show that the convergence of the quantum circuit to the target distribution depends critically on both the quantum hardware and classical optimization strategy. Our study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.

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18

Francis, Akhil, Ephrata Zelleke, Ziyue Zhang, Alexander F. Kemper, and James K. Freericks. "Determining Ground-State Phase Diagrams on Quantum Computers via a Generalized Application of Adiabatic State Preparation." Symmetry 14, no. 4 (April 13, 2022): 809. http://dx.doi.org/10.3390/sym14040809.

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Quantum phase transitions materialize as level crossings in the ground-state energy when the parameters of the Hamiltonian are varied. The resulting ground-state phase diagrams are straightforward to determine by exact diagonalization on classical computers, but are challenging on quantum computers because of the accuracy needed and the near degeneracy of the competing states close to the level crossings. On the other hand, classical computers are limited to small system sizes, which quantum computers may help overcome. In this work, we use a local adiabatic ramp for state preparation to allow us to directly compute ground-state phase diagrams on a quantum computer via time evolution. This methodology is illustrated by examining the ground states of the XY model with a magnetic field in the z-direction in one dimension. We are able to calculate an accurate phase diagram on both two- and three-site systems using IBM quantum machines.

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19

Lu, Xuandiyang. "Research on Biological Population Evolutionary Algorithm and Individual Adaptive Method Based on Quantum Computing." Wireless Communications and Mobile Computing 2022 (March 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/5188335.

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On the basis of classical computer, quantum computer has been developed. In dealing with some large-scale parallel problems, quantum computer is simpler and faster than traditional computer. Nowadays, physical qubit computers have many limitations. Classical computers have many ways to simulate quantum computing, the most effective of which are quantum superiority and quantum algorithm. Ensuring computational efficiency, accuracy, and precision is of great significance to the study of large-scale quantum computing. Compared with other algorithms, genetic algorithm has more advantages, so it can be more widely used. For example, strong adaptability and global optimization ability are the advantages of genetic algorithm. Through the research in Chapter 4, we can conclude that the variance of A2C is obviously smaller than that of PPO. Furthermore, it can be concluded that A2C has better robustness.

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20

YEPEZ, JEFFREY. "TYPE-II QUANTUM COMPUTERS." International Journal of Modern Physics C 12, no. 09 (November 2001): 1273–84. http://dx.doi.org/10.1142/s0129183101002668.

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This paper discusses a computing architecture that uses both classical parallelism and quantum parallelism. We consider a large parallel array of small quantum computers, connected together by classical communication channels. This kind of computer is called a type-II quantum computer, to differentiate it from a globally phase-coherent quantum computer, which is the first type of quantum computer that has received nearly exclusive attention in the literature. Although a hybrid, a type-II quantum computer retains the crucial advantage allowed by quantum mechanical superposition that its computational power grows exponentially in the number of phase-coherent qubits per node, only short-range and short time phase-coherence is needed, which significantly reduces the level of engineering facility required to achieve its construction. Therefore, the primary factor limiting its computational power is an economic one and not a technological one, since the volume of its computational medium can in principle scale indefinitely.

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21

Resch, Salonik, and Ulya R. Karpuzcu. "Benchmarking Quantum Computers and the Impact of Quantum Noise." ACM Computing Surveys 54, no. 7 (July 2021): 1–35. http://dx.doi.org/10.1145/3464420.

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Benchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appropriate benchmark and metrics to extract meaningful results. Different benchmarks test the system in different ways, and each individual metric may or may not be of interest. Choosing the appropriate approach is tricky. The situation is even more open ended for quantum computers, where there is a wider range of hardware, fewer established guidelines, and additional complicating factors. Notably, quantum noise significantly impacts performance and is difficult to model accurately. Here, we discuss benchmarking of quantum computers from a computer architecture perspective and provide numerical simulations highlighting challenges that suggest caution.

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Jordan, Stephen P., Hari Krovi, Keith S. M. Lee, and John Preskill. "BQP-completeness of scattering in scalar quantum field theory." Quantum 2 (January 8, 2018): 44. http://dx.doi.org/10.22331/q-2018-01-08-44.

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Recent work has shown that quantum computers can compute scattering probabilities in massive quantum field theories, with a run time that is polynomial in the number of particles, their energy, and the desired precision. Here we study a closely related quantum field-theoretical problem: estimating the vacuum-to-vacuum transition amplitude, in the presence of spacetime-dependent classical sources, for a massive scalar field theory in (1+1) dimensions. We show that this problem is BQP-hard; in other words, its solution enables one to solve any problem that is solvable in polynomial time by a quantum computer. Hence, the vacuum-to-vacuum amplitude cannot be accurately estimated by any efficient classical algorithm, even if the field theory is very weakly coupled, unless BQP=BPP. Furthermore, the corresponding decision problem can be solved by a quantum computer in a time scaling polynomially with the number of bits needed to specify the classical source fields, and this problem is therefore BQP-complete. Our construction can be regarded as an idealized architecture for a universal quantum computer in a laboratory system described by massive phi^4 theory coupled to classical spacetime-dependent sources.

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23

Khatri, Sumeet, Ryan LaRose, Alexander Poremba, Lukasz Cincio, Andrew T. Sornborger, and Patrick J. Coles. "Quantum-assisted quantum compiling." Quantum 3 (May 13, 2019): 140. http://dx.doi.org/10.22331/q-2019-05-13-140.

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Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for this is to evaluate an algorithm's cost on a quantum computer. To this end, we propose a variational hybrid quantum-classical algorithm called quantum-assisted quantum compiling (QAQC). In QAQC, we use the overlap between a target unitaryUand a trainable unitaryVas the cost function to be evaluated on the quantum computer. More precisely, to ensure that QAQC scales well with problem size, our cost involves not only the global overlapTr(V†U)but also the local overlaps with respect to individual qubits. We introduce novel short-depth quantum circuits to quantify the terms in our cost function, and we prove that our cost cannot be efficiently approximated with a classical algorithm under reasonable complexity assumptions. We present both gradient-free and gradient-based approaches to minimizing this cost. As a demonstration of QAQC, we compile various one-qubit gates on IBM's and Rigetti's quantum computers into their respective native gate alphabets. Furthermore, we successfully simulate QAQC up to a problem size of 9 qubits, and these simulations highlight both the scalability of our cost function as well as the noise resilience of QAQC. Future applications of QAQC include algorithm depth compression, black-box compiling, noise mitigation, and benchmarking.

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Da Rosa, Evandro Chagas Ribeiro, and Rafael De Santiago. "Ket Quantum Programming." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (January 31, 2022): 1–25. http://dx.doi.org/10.1145/3474224.

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Quantum programming languages (QPL) fill the gap between quantum mechanics and classical programming constructions, simplifying the development of quantum applications. However, most QPL addresses the inherent quantum programming problem, neglecting quantum computer implementation constraints. We present a runtime architecture for classical-quantum execution that mitigates the limitation of interaction between classical and quantum computers originated from the cloud-based model of quantum computation provided by several vendors, which implies a quantum computer processing in batch. In the proposed runtime architecture, we introduce (i) runtime quantum code generation to enable generic quantum programming and dynamic quantum execution; and (ii) the concept of futures to handle dynamic interaction between classical and quantum computers. To support our proposal, we have implemented the Ket Quantum Programming framework that features a Python-embedded classical-quantum programming language named Ket, the C++ quantum programming library Libket, and Ket Bitwise (quantum computing) Simulator. The last one improves over the bitwise representation, making the simulation time not dependent on the number of qubits but the amount of superposition and entanglement of simulation.

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Li, Haoxin, and Guangyu Zhao. "The Comparison of the computing ability of quantum and conventional computer." Highlights in Science, Engineering and Technology 5 (July 7, 2022): 68–74. http://dx.doi.org/10.54097/hset.v5i.725.

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Contemporarily, whether quantum computing performs better than conventional computers remain an unresolved issue. In this paper, we compare the time complexity between quantum and conventional computers for a specific type of issue. Theoretically, quantum computation is better in solving nonlinear calculations, the quantitative evaluation of the computing ability should be investigated. In this paper, the Fourier transform was applied in both classical and quantum logical circuits to calculate the theoretical time complexities respectively and make a comparison after that. According to the analysis, the ideal quantum computer performed really fast as the number of tasks increased, but the gate-level accurate quantum circuit, which was a simulated circuit was running slower. The paper shows that the current technology cannot perform the perfect quantum computer, but the future of it would be very promising. Overall, these results shed light on guiding further exploration of quantum computing.

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26

Cho, Adrian. "Ordinary computer matches Google’s quantum computer." Science 377, no. 6606 (August 5, 2022): 563–64. http://dx.doi.org/10.1126/science.ade2360.

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Li, Hang, Pan Gao, Jiang Zhang, Zhiyuan Liu, Hai Wei, Kai Wen, Shijie Wei, and Gui-Lu Long. "BQ-Chem: A Quantum Software Program for Chemistry Simulation Based on the Full Quantum Eigensolver Algorithm." Quantum Engineering 2022 (December 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/5872283.

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We describe a quantum chemistry simulation software program BQ-Chem, which can calculate the low-energy spectrum and potential energy surface of molecules on a quantum computer. BQ-Chem is based on the full quantum eigensolver (FQE), which is implemented with a quantum gradient descent algorithm. Benefiting from FQE, BQ-Chem can perform all the calculations on a quantum computer. Compared with the classical optimization methods which encounter the optimization difficulty of high-dimensional and multivariable functions in dealing with multielectron orbitals of macromolecules, FQE provides an exponential speedup. FQE works fully on a quantum computer; thus, BQ-Chem can be smoothly transited to future large-scale quantum computers.

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Reiher, Markus, Nathan Wiebe, Krysta M. Svore, Dave Wecker, and Matthias Troyer. "Elucidating reaction mechanisms on quantum computers." Proceedings of the National Academy of Sciences 114, no. 29 (July 3, 2017): 7555–60. http://dx.doi.org/10.1073/pnas.1619152114.

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With rapid recent advances in quantum technology, we are close to the threshold of quantum devices whose computational powers can exceed those of classical supercomputers. Here, we show that a quantum computer can be used to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. Our resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. Our results demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources.

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Gyongyosi, Laszlo. "Adaptive Problem Solving Dynamics in Gate-Model Quantum Computers." Entropy 24, no. 9 (August 26, 2022): 1196. http://dx.doi.org/10.3390/e24091196.

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Gate-model quantum computer architectures represent an implementable model used to realize quantum computations. The mathematical description of the dynamical attributes of adaptive problem solving and iterative objective function evaluation in a gate-model quantum computer is currently a challenge. Here, a mathematical model of adaptive problem solving dynamics in a gate-model quantum computer is defined. We characterize a canonical equation of adaptive objective function evaluation of computational problems. We study the stability of adaptive problem solving in gate-model quantum computers.

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Otgonbaatar, Soronzonbold, and Mihai Datcu. "Assembly of a Coreset of Earth Observation Images on a Small Quantum Computer." Electronics 10, no. 20 (October 12, 2021): 2482. http://dx.doi.org/10.3390/electronics10202482.

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Satellite instruments monitor the Earth’s surface day and night, and, as a result, the size of Earth observation (EO) data is dramatically increasing. Machine Learning (ML) techniques are employed routinely to analyze and process these big EO data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM poses a quadratic programming problem, and quantum computers including quantum annealers (QA) as well as gate-based quantum computers promise to solve an SVM more efficiently than a conventional computer; training the SVM by employing a quantum computer/conventional computer represents a quantum SVM (qSVM)/classical SVM (cSVM) application. However, quantum computers cannot tackle many practical EO problems by using a qSVM due to their very low number of input qubits. Hence, we assembled a coreset (“core of a dataset”) of given EO data for training a weighted SVM on a small quantum computer, a D-Wave quantum annealer with around 5000 input quantum bits. The coreset is a small, representative weighted subset of an original dataset, and its performance can be analyzed by using the proposed weighted SVM on a small quantum computer in contrast to the original dataset. As practical data, we use synthetic data, Iris data, a Hyperspectral Image (HSI) of Indian Pine, and a Polarimetric Synthetic Aperture Radar (PolSAR) image of San Francisco. We measured the closeness between an original dataset and its coreset by employing a Kullback–Leibler (KL) divergence test, and, in addition, we trained a weighted SVM on our coreset data by using both a D-Wave quantum annealer (D-Wave QA) and a conventional computer. Our findings show that the coreset approximates the original dataset with very small KL divergence (smaller is better), and the weighted qSVM even outperforms the weighted cSVM on the coresets for a few instances of our experiments. As a side result (or a by-product result), we also present our KL divergence findings for demonstrating the closeness between our original data (i.e., our synthetic data, Iris data, hyperspectral image, and PolSAR image) and the assembled coreset.

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CHONG, Yonuk, and Jaewoo JOO. "Superconducting Quantum Computer." Physics and High Technology 28, no. 3 (March 31, 2019): 12–17. http://dx.doi.org/10.3938/phit.28.008.

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32

Rieffel, Eleanor. "Quantum computer science." ACM SIGACT News 41, no. 3 (September 3, 2010): 39–44. http://dx.doi.org/10.1145/1855118.1855127.

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Chuang, Isaac L., and Yoshihisa Yamamoto. "Simple quantum computer." Physical Review A 52, no. 5 (November 1, 1995): 3489–96. http://dx.doi.org/10.1103/physreva.52.3489.

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De Raedt, Hans, Anthony H. Hams, Kristel Michielsen, and Koen De Raedt. "Quantum Computer Emulator." Computer Physics Communications 132, no. 1-2 (October 2000): 1–20. http://dx.doi.org/10.1016/s0010-4655(00)00132-6.

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Lanzagorta, Marco, and Jeffrey Uhlmann. "Quantum Computer Science." Synthesis Lectures on Quantum Computing 1, no. 1 (January 2008): 1–124. http://dx.doi.org/10.2200/s00159ed1v01y200810qmc002.

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Liu, Hai Yan. "The Realization of New Computing Model and System Research Related to Quantum Computer." Advanced Materials Research 1078 (December 2014): 413–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.413.

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The ultimate goal of quantum calculation is to build high performance practical quantum computers. With quantum mechanics model of computer information coding and computational principle, it is proved in theory to be able to simulate the classical computer is currently completely, and with more classical computer, quantum computation is one of the most popular fields in physics research in recent ten years, has formed a set of quantum physics, mathematics. This paper to electronic spin doped fullerene quantum aided calculation scheme, we through the comprehensive use of logic based network and based on the overall control of the two kinds of quantum computing model, solve the addressing problem of nuclear spin, avoids the technical difficulties of pre-existing. We expect the final realization of the quantum computer will depend on the integrated use of in a variety of quantum computing model and physical realization system, and our primary work shows this feature..

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Somaroo, S., C. H. Tseng, T. F. Havel, R. Laflamme, and D. G. Cory. "Quantum Simulations on a Quantum Computer." Physical Review Letters 82, no. 26 (June 28, 1999): 5381–84. http://dx.doi.org/10.1103/physrevlett.82.5381.

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Vedral, Vlatko. "Quantum information: Building a quantum computer." New Scientist 219, no. 2924 (July 2013): iv—v. http://dx.doi.org/10.1016/s0262-4079(13)61637-1.

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Sanders, G. D., K. W. Kim, and W. C. Holton. "Optically driven quantum-dot quantum computer." Physical Review A 60, no. 5 (November 1, 1999): 4146–49. http://dx.doi.org/10.1103/physreva.60.4146.

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Kempf, Achim. "Quantum Gravity on a Quantum Computer?" Foundations of Physics 44, no. 5 (August 3, 2013): 472–82. http://dx.doi.org/10.1007/s10701-013-9735-3.

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Benkoczi, Robert, Daya Gaur, Naya Nagy, Marius Nagy, and Shahadat Hossain. "Quantum Bitcoin Mining." Entropy 24, no. 3 (February 24, 2022): 323. http://dx.doi.org/10.3390/e24030323.

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This paper studies the effect of quantum computers on Bitcoin mining. The shift in computational paradigm towards quantum computation allows the entire search space of the golden nonce to be queried at once by exploiting quantum superpositions and entanglement. Using Grover’s algorithm, a solution can be extracted in time O(2256/t), where t is the target value for the nonce. This is better using a square root over the classical search algorithm that requires O(2256/t) tries. If sufficiently large quantum computers are available for the public, mining activity in the classical sense becomes obsolete, as quantum computers always win. Without considering quantum noise, the size of the quantum computer needs to be ≈104 qubits.

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McClean, Jarrod R., Nicholas C. Rubin, Joonho Lee, Matthew P. Harrigan, Thomas E. O’Brien, Ryan Babbush, William J. Huggins, and Hsin-Yuan Huang. "What the foundations of quantum computer science teach us about chemistry." Journal of Chemical Physics 155, no. 15 (October 21, 2021): 150901. http://dx.doi.org/10.1063/5.0060367.

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With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.

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Kosobutskyy, Petro. "Physical foundations of quantum informatics: from quantum mechanics through quantum computing to quantum cryptography." Computer Design Systems. Theory and Practice 4, no. 1 (2022): 33–47. http://dx.doi.org/10.23939/cds2022.01.033.

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A methodical analysis of the basic problem related to quantum calculations of parameters of physical systems was made. Emphasis is placed on the physical principles of the operation of a quantum computer, with an emphasis on the fact that simultaneous access to all quantum states is important in quantum computing, which allows the simultaneous change of the quantum state from all superpositions in the qubit system. Emphasis is placed on the fact that in quantum algorithms the Fourier transform and the Hadamard transform are the basic operations - as a simple discrete Fourier transform. The reader's attention is drawn to the fact that quantum computing is primarily implemented in quantum objects with the properties of elementary NOT gates and controlled CNOT, which can be implemented on a Mach-Zehnder interferometer using the phenomena of photon interference and rotation of its polarization vector. Despite the progress of conventional computers, the need for the development of quantum computing is due to the technological limitation due to the dimensional quantization of the electronic spectrum and the exponential increase in the time of calculations by classical algorithms when the volume of data increases. However, the widespread use of quantum computers is limited by a number of problems. This is, first of all, insufficient accuracy and high sensitivity to external influences that can destroy the quantum state. Therefore, to increase the accuracy of calculations on a quantum computer, the calculation algorithm must be repeated a certain number of times, and to avoid the destruction of the quantum states of the qubit, low temperatures are used.

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Fujii, Toshiyuki, Shigemasa Matsuo, and Noriyuki Hatakenaka. "Fluxon-controlled quantum computer." International Journal of Quantum Information 14, no. 07 (October 2016): 1650040. http://dx.doi.org/10.1142/s0219749916500404.

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We propose a fluxon-controlled quantum computer incorporated with three-qubit quantum error correction using special gate operations, i.e. joint-phase and SWAP gate operations, inherent in capacitively coupled superconducting flux qubits. The proposed quantum computer acts exactly like a knitting machine at home.

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Mielczarek, Jakub. "Spin Foam Vertex Amplitudes on Quantum Computer—Preliminary Results." Universe 5, no. 8 (July 26, 2019): 179. http://dx.doi.org/10.3390/universe5080179.

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Vertex amplitudes are elementary contributions to the transition amplitudes in the spin foam models of quantum gravity. The purpose of this article is to make the first step towards computing vertex amplitudes with the use of quantum algorithms. In our studies we are focused on a vertex amplitude of 3+1 D gravity, associated with a pentagram spin network. Furthermore, all spin labels of the spin network are assumed to be equal j = 1 / 2 , which is crucial for the introduction of the intertwiner qubits. A procedure of determining modulus squares of vertex amplitudes on universal quantum computers is proposed. Utility of the approach is tested with the use of: IBM’s ibmqx4 5-qubit quantum computer, simulator of quantum computer provided by the same company and QX quantum computer simulator. Finally, values of the vertex probability are determined employing both the QX and the IBM simulators with 20-qubit quantum register and compared with analytical predictions.

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Liu, Hai Yan. "Coding and Decoding Device Design of Three-Value Quantum Computer." Applied Mechanics and Materials 713-715 (January 2015): 1015–18. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1015.

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A quantum computer is the computer technology and the micro physical scientists will combine the technology achievements. Quantum computer is the processing of information by using the quantum properties of particle, a new concept quantum laws to control information transmission and processing of computer. Light has the advantages in space time parallel and high-frequency radiation, which makes the optical become the main target of research model the future of computers. This did not prevent the three valued logic calculus is still an important part of the three values of optical computer. It is precisely because the three value of optical code decoder and the three value, importance, ALU logic optical so, in three valued optical computer all the key parts, we firstly research the value of three logic optical light coding, decoder and a value of three.

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Song, Gyeong Ju, Min Ho Song, and Hwa Jeong Seo. "Comparative analysis of quantum circuit implementation for domestic and international hash functions." Korean Institute of Smart Media 12, no. 2 (March 30, 2023): 83–90. http://dx.doi.org/10.30693/smj.2023.12.2.83.

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The advent of quantum computers threatens the security of existing hash functions. In this paper, we confirmed the implementation results of quantum circuits for domestic/international hash functions, LSH, SHA2, SHA3 and SM3, and conducted a comparative analysis. To operate the existing hash function in a quantum computer, it must be implemented as a quantum circuit, and the quantum security strength can be confirmed by estimating the necessary quantum resources. We compared methods of quantum circuit implementation and results of quantum resource estimation in various aspects and discussed ways to meet quantum computer security in the future.

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Xiao, Kang. "Review of Quantum Computer Development." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 45–52. http://dx.doi.org/10.54097/hset.v34i.5373.

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In recent years, quantum computers have been attracting attention. Quantum computers have become a hot topic of research in today's scientific research. This paper first introduces the basic principles of quantum computers, including the storage principle and quantum logic gates, then introduces a quantum algorithm, then introduces the organization of quantum computers, and finally is a discussion that will evaluate the strengths and weaknesses of quantum computers, and discusses the physical implementation of quantum computers and the outlook on quantum computing.

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Vianna, Reinaldo O., Wilson R. M. Rabelo, and C. H. Monken. "The Semi-Quantum Computer." International Journal of Quantum Information 01, no. 02 (June 2003): 279–88. http://dx.doi.org/10.1142/s021974990300019x.

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We discuss the performance of the Search and Fourier Transform algorithms on a hybrid computer constituted of classical and quantum processors working together. We show that this semi-quantum computer would be an improvement over a pure classical architecture, no matter how few qubits are available and, therefore, it suggests an easier implementable technology than a pure quantum computer with arbitrary number of qubits.

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Berman, G. P., D. I. Kamenev, and V. I. Tsifrinovich. "Perturbation approach for nuclear magnetic resonance solid-state quantum computation." Journal of Applied Mathematics 2003, no. 1 (2003): 35–53. http://dx.doi.org/10.1155/s1110757x03110182.

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A dynamics of a nuclear-spin quantum computer with a large number(L=1000)of qubits is considered using a perturbation approach. Small parameters are introduced and used to compute the error in an implementation of an entanglement between remote qubits, using a sequence of radio-frequency pulses. The error is computed up to the different orders of the perturbation theory and tested using exact numerical solution.

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