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Journal articles on the topic 'Quantum computation'

To see the other types of publications on this topic, follow the link: Quantum computation.

Author: Grafiati

Published: 4 June 2021

Last updated: 18 May 2024

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1

Raussendorf, Robert. "Cohomological framework for contextual quantum computations." quantum Information and Computation 19, no. 13&14 (November 2019): 1141–70. http://dx.doi.org/10.26421/qic19.13-14-4.

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We describe a cohomological framework for measurement-based quantum computation in which symmetry plays a central role. Therein, the essential information about the computation is contained in either of two topological invariants, namely two cohomology groups. One of them applies only to deterministic quantum computations, and the other to general probabilistic ones. Those invariants characterize the computational output, and at the same time witness quantumness in the form of contextuality. In result, they give rise to fundamental algebraic structures underlying quantum computation.

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2

SADAKANE, Kunihiko, Noriko SUGAWARA, and Takeshi TOKUYAMA. "Quantum Computation in Computational Geometry." Interdisciplinary Information Sciences 8, no. 2 (2002): 129–36. http://dx.doi.org/10.4036/iis.2002.129.

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3

Gudder, Stan. "Quantum Computation." American Mathematical Monthly 110, no. 3 (March 2003): 181. http://dx.doi.org/10.2307/3647933.

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4

Ding, Dawei. "Quantum computation." XRDS: Crossroads, The ACM Magazine for Students 23, no. 1 (September 20, 2016): 7–8. http://dx.doi.org/10.1145/2983467.

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5

Hughes, R. "Quantum Computation." Computing in Science and Engineering 3, no. 2 (March 2001): 26. http://dx.doi.org/10.1109/mcise.2001.908998.

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6

Grover, L. "Quantum computation." IEEE Potentials 18, no. 2 (1999): 4–8. http://dx.doi.org/10.1109/45.755839.

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7

Deutsch, David, and Artur Ekert. "Quantum computation." Physics World 11, no. 3 (March 1998): 47–52. http://dx.doi.org/10.1088/2058-7058/11/3/31.

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8

DiVincenzo, D. P. "Quantum Computation." Science 270, no. 5234 (October 13, 1995): 255–61. http://dx.doi.org/10.1126/science.270.5234.255.

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9

Deutsch, David. "Quantum computation." Physics World 5, no. 6 (June 1992): 57–61. http://dx.doi.org/10.1088/2058-7058/5/6/38.

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10

Patel, Apoorva. "Quantum computation." Resonance 16, no. 9 (September 2011): 821–35. http://dx.doi.org/10.1007/s12045-011-0100-6.

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11

MARGOLUS, NORMAN. "Quantum Computation." Annals of the New York Academy of Sciences 480, no. 1 New Technique (December 1986): 487–97. http://dx.doi.org/10.1111/j.1749-6632.1986.tb12451.x.

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12

Gudder, Stan. "Quantum Computation." American Mathematical Monthly 110, no. 3 (March 2003): 181–201. http://dx.doi.org/10.1080/00029890.2003.11919955.

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13

Pieper, Jaden, and Manuel Lladser. "Quantum Computation." Scholarpedia 13, no. 2 (2018): 52499. http://dx.doi.org/10.4249/scholarpedia.52499.

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14

DATTA, ANIMESH, and ANIL SHAJI. "QUANTUM DISCORD AND QUANTUM COMPUTING — AN APPRAISAL." International Journal of Quantum Information 09, no. 07n08 (October 2011): 1787–805. http://dx.doi.org/10.1142/s0219749911008416.

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We discuss models of computing that are beyond classical. The primary motivation is to unearth the cause of non-classical advantages in computation. Completeness results from computational complexity theory lead to the identification of very disparate problems, and offer a kaleidoscopic view into the realm of quantum enhancements in computation. Emphasis is placed on the "power of one qubit" model, and the boundary between quantum and classical correlations as delineated by quantum discord. A recent result by Eastin on the role of this boundary in the efficient classical simulation of quantum computation is discussed. Perceived drawbacks in the interpretation of quantum discord as a relevant certificate of quantum enhancements are addressed.

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15

Xu, Qingshan, Xiaoqing Tan, and Rui Huang. "Improved Resource State for Verifiable Blind Quantum Computation." Entropy 22, no. 9 (September 7, 2020): 996. http://dx.doi.org/10.3390/e22090996.

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Recent advances in theoretical and experimental quantum computing raise the problem of verifying the outcome of these quantum computations. The recent verification protocols using blind quantum computing are fruitful for addressing this problem. Unfortunately, all known schemes have relatively high overhead. Here we present a novel construction for the resource state of verifiable blind quantum computation. This approach achieves a better verifiability of 0.866 in the case of classical output. In addition, the number of required qubits is 2N+4cN, where N and c are the number of vertices and the maximal degree in the original computation graph, respectively. In other words, our overhead is less linear in the size of the computational scale. Finally, we utilize the method of repetition and fault-tolerant code to optimise the verifiability.

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16

DALLA CHIARA, MARIA LUISA, ROBERTO GIUNTINI, and ROBERTO LEPORINI. "LOGICS FROM QUANTUM COMPUTATION." International Journal of Quantum Information 03, no. 02 (June 2005): 293–337. http://dx.doi.org/10.1142/s0219749905000943.

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The theory of logical gates in quantum computation has suggested new forms of quantum logic, called quantum computational logics. The basic semantic idea is the following: the meaning of a sentence is identified with a quregister (a system of qubits) or, more generally, with a mixture of quregisters (called qumix). In this framework, any sentence α of the language gives rise to a quantum tree: a kind of quantum circuit that transforms the quregister (qumix) associated to the atomic subformulas of α into the quregister (qumix) associated to α. A variant of the quantum computational semantics is represented by the quantum holistic semantics, which permits us to represent entangled meanings. Physical models of quantum computational logics can be built by means of Mach–Zehnder interferometers.

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17

CASTAGNOLI, GIUSEPPE. "QUANTUM STEADY COMPUTATION." International Journal of Modern Physics B 05, no. 13 (August 10, 1991): 2253–69. http://dx.doi.org/10.1142/s0217979291000870.

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Current conceptions of "quantum mechanical computers" inherit from conventional digital machines two apparently interacting features, machine imperfection and temporal development of the computational process. On account of machine imperfection, the process would become ideally reversible only in the limiting case of zero speed. Therefore the process is irreversible in practice and cannot be considered to be a fundamental quantum one. By giving up classical features and using a linear, reversible and non-sequential representation of the computational process — not realizable in classical machines — the process can be identified with the mathematical form of a quantum steady state. This form of steady quantum computation would seem to have an important bearing on the notion of cognition.

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18

Kuczerski, Tomasz. "EXAMPLES OF QUANTUM IT IN NEW TECHNOLOGIES OF COMPUTATION." PROBLEMY TECHNIKI UZBROJENIA 158, no. 3-4 (December 30, 2021): 65–89. http://dx.doi.org/10.5604/01.3001.0015.6777.

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The paper includes definitions of elements of quantum IT referred to classical technologies of computation. It explains the principles of transformation of calculating algorithms to the domain of quantum computations using the optimisation and matrix calculus. Exemplary applications of classical algorithms are presented with possibilities of their realisation in domain of quantum IT. Autor presents some possibilities for using quantum algorithms in new computation technologies concerning quantum cryptography and data analyses with complex computations.

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19

Krishnamurthy, E. V. "Computational Power of Quantum Machines, Quantum Grammars and Feasible Computation." International Journal of Modern Physics C 09, no. 02 (March 1998): 213–41. http://dx.doi.org/10.1142/s0129183198000170.

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This paper studies the computational power of quantum computers to explore as to whether they can recognize properties which are in nondeterministic polynomial-time class (NP) and beyond. To study the computational power, we use the Feynman's path integral (FPI) formulation of quantum mechanics. From a computational point of view the Feynman's path integral computes a quantum dynamical analogue of the k-ary relation computed by an Alternating Turing machine (ATM) using AND-OR Parallelism. Hence, if we can find a suitable mapping function between an instance of a mathematical problem and the corresponding interference problem, using suitable potential functions for which FPI can be integrated exactly, the computational power of a quantum computer can be bounded to that of an alternating Turing machine that can solve problems in NP (e.g, factorization problem) and in polynomial space. Unfortunately, FPI is exactly integrable only for a few problems (e.g., the harmonic oscillator) involving quadratic potentials; otherwise, they may be only approximately computable or noncomputable. This means we cannot in general solve all quantum dynamical problems exactly except for those special cases of quadratic potentials, e.g., harmonic oscillator. Since there is a one to one correspondence between the quantum mechanical problems that can be analytically solved and the path integrals that can be exactly evaluated, we can say that the noncomputability of FPI implies quantum unsolvability. This is the analogue of classical unsolvability. The Feynman's path graph can be considered as a semantic parse graph for the quantum mechanical sentence. It provides a semantic valuation function of the terminal sentence based on probability amplitudes to disambiguate a given quantum description and obtain an interpretation in a linear time. In Feynman's path integral, the kernels are partially ordered over time (different alternate paths acting concurrently at the same time) and multiplied. The semantic valuation is computable only if the FPI is computable. Thus both the expressive power and complexity aspects quantum computing are mirrored by the exact and efficient integrability of FPI.

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20

Aharonov, Dorit, Wim van Dam, Julia Kempe, Zeph Landau, Seth Lloyd, and Oded Regev. "Adiabatic Quantum Computation is Equivalent to Standard Quantum Computation." SIAM Journal on Computing 37, no. 1 (January 2007): 166–94. http://dx.doi.org/10.1137/s0097539705447323.

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21

Aharonov, Dorit, Wim van Dam, Julia Kempe, Zeph Landau, Seth Lloyd, and Oded Regev. "Adiabatic Quantum Computation Is Equivalent to Standard Quantum Computation." SIAM Review 50, no. 4 (January 2008): 755–87. http://dx.doi.org/10.1137/080734479.

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22

Bollinger, Terry. "Biomolecular Quantum Computation." Terry's Archive Online 2020, no. 10 (October 22, 2020): 1007. http://dx.doi.org/10.48034/20201007.

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In terms of leveraging the total power of quantum computing, the prevalent current (2020) model of designing quantum computation devices to follow the von Neuman model of abstraction is highly unlikely to be making full use of the full range of computational assistance possible at the atomic and molecular level. This is particularly the case for molecular modeling, in using computational models that more directly leverage the quantum effects of one set of molecules to estimate the behavior of some other set of molecules would remove the bottleneck of insisting that modeling first be converted to the virtual binary or digital format of quantum von Neuman machines. It is argued that even though this possibility of “fighting molecular quantum dynamics with molecular quantum dynamics” was recognized by early quantum computing founders such as Yuri Manin and Richard Feynman, the idea was quickly overlooked in favor of the more computer-compatible model that later developed into qubits and qubit processing.

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23

Zhang, Xin, Hai-Ou Li, Gang Cao, Ming Xiao, Guang-Can Guo, and Guo-Ping Guo. "Semiconductor quantum computation." National Science Review 6, no. 1 (December 22, 2018): 32–54. http://dx.doi.org/10.1093/nsr/nwy153.

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AbstractSemiconductors, a significant type of material in the information era, are becoming more and more powerful in the field of quantum information. In recent decades, semiconductor quantum computation was investigated thoroughly across the world and developed with a dramatically fast speed. The research varied from initialization, control and readout of qubits, to the architecture of fault-tolerant quantum computing. Here, we first introduce the basic ideas for quantum computing, and then discuss the developments of single- and two-qubit gate control in semiconductors. Up to now, the qubit initialization, control and readout can be realized with relatively high fidelity and a programmable two-qubit quantum processor has even been demonstrated. However, to further improve the qubit quality and scale it up, there are still some challenges to resolve such as the improvement of the readout method, material development and scalable designs. We discuss these issues and introduce the forefronts of progress. Finally, considering the positive trend of the research on semiconductor quantum devices and recent theoretical work on the applications of quantum computation, we anticipate that semiconductor quantum computation may develop fast and will have a huge impact on our lives in the near future.

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24

Ulyanov, Sergey, Andrey Reshetnikov, Olga Tyatyushkina, and Vladimir Korenkov. "Quantum software engineering. Pt. I: Quantum Circuit (Gate) Model based Computing – education Lectures and pedagogical workshop." System Analysis in Science and Education, no. 3 (2020) (September 30, 2020): 129–201. http://dx.doi.org/10.37005/2071-9612-2020-3-129-201.

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All the quantum algorithms are based on a certain quantum computing model, varying from the quantum circuit, one-way quantum computation, adiabatic quantum computation and topological quantum computation. These four models are equivalent in computational power; among them, the quantum circuit model is most frequently used. In the circuit model, it has been proved that arbitrary single-qubit rotations plus twoqubit controlled-NOT gates are universal, i.e. they can provide a set of gates to implement any quantum algorithm. This article discusses the goal for this research: it is to given a lightning-fast (as-barebones-as-possible) definition of the quantum circuit model computing and leisurely development of quantum computation before actually getting around to sophisticated algorithms. In this article the main ideas of quantum software engineering is described.

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25

KRISHNAMURTHY, E. V. "INTEGRABILITY, ENTROPY AND QUANTUM COMPUTATION." International Journal of Modern Physics C 10, no. 07 (October 1999): 1205–28. http://dx.doi.org/10.1142/s012918319900098x.

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The important requirements are stated for the success of quantum computation. These requirements involve coherent preserving Hamiltonians as well as exact integrability of the corresponding Feynman path integrals. Also we explain the role of metric entropy in dynamical evolutionary system and outline some of the open problems in the design of quantum computational systems. Finally, we observe that unless we understand quantum nondemolition measurements, quantum integrability, quantum chaos and the direction of time arrow, the quantum control and computational paradigms will remain elusive and the design of systems based on quantum dynamical evolution may not be feasible.

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26

NAGY, MARIUS, and SELIM G. AKL. "COPING WITH DECOHERENCE: PARALLELIZING THE QUANTUM FOURIER TRANSFORM." Parallel Processing Letters 20, no. 03 (September 2010): 213–26. http://dx.doi.org/10.1142/s012962641000017x.

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Rank-varying computational complexity describes those computations in which the complexity of executing each step is not a constant, but evolves throughout the computation as a function of the order of execution of each step [2]. This paper identifies practical instances of this computational paradigm in the procedures for computing the quantum Fourier transform and its inverse. It is shown herein that under the constraints imposed by quantum decoherence, only a parallel approach can guarantee a reliable solution or, alternatively, improve scalability.

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27

Fan Heng. "Quantum computation and quantum simulation." Acta Physica Sinica 67, no. 12 (2018): 120301. http://dx.doi.org/10.7498/aps.67.20180710.

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28

Brodutch, Aharon, Alexei Gilchrist, Daniel R. Terno, and Christopher J. Wood. "Quantum discord in quantum computation." Journal of Physics: Conference Series 306 (July 8, 2011): 012030. http://dx.doi.org/10.1088/1742-6596/306/1/012030.

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29

Loss, Daniel, and David P. DiVincenzo. "Quantum computation with quantum dots." Physical Review A 57, no. 1 (January 1, 1998): 120–26. http://dx.doi.org/10.1103/physreva.57.120.

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30

Nielsen, Michael A., Isaac Chuang, and Lov K. Grover. "Quantum Computation and Quantum Information." American Journal of Physics 70, no. 5 (May 2002): 558–59. http://dx.doi.org/10.1119/1.1463744.

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31

Law, Jim. "Quantum computation and quantum information." ACM SIGSOFT Software Engineering Notes 26, no. 4 (July 2001): 91. http://dx.doi.org/10.1145/505482.505499.

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32

Zizzi, P. A. "Quantum Computation Toward Quantum Gravity." General Relativity and Gravitation 33, no. 8 (August 2001): 1305–18. http://dx.doi.org/10.1023/a:1012053424024.

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33

Wang, Yazhen. "Quantum Computation and Quantum Information." Statistical Science 27, no. 3 (August 2012): 373–94. http://dx.doi.org/10.1214/11-sts378.

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34

Nagy, Marius, and Selim G. Akl. "Quantum computation and quantum information†." International Journal of Parallel, Emergent and Distributed Systems 21, no. 1 (February 2006): 1–59. http://dx.doi.org/10.1080/17445760500355678.

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35

Loveridge, Leon, Raouf Dridi, and Robert Raussendorf. "Topos logic in measurement-based quantum computation." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2176 (April 2015): 20140716. http://dx.doi.org/10.1098/rspa.2014.0716.

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We report first steps towards elucidating the relationship between contextuality, measurement-based quantum computation (MBQC) and the non-classical logic of a topos associated with the computation. We show that, in a class of MBQC, classical universality requires non-classical logic, which is ‘consumed’ during the course of the computation, thereby pinpointing another potential quantum computational resource.

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36

Nivelkar, Mukta, and S. G. Bhirud. "Modeling of Supervised Machine Learning using Mechanism of Quantum Computing." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2161/1/012023.

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Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

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37

Matsuno, Koichiro. "Biological computation running on quantum computation." Biosystems 207 (September 2021): 104467. http://dx.doi.org/10.1016/j.biosystems.2021.104467.

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38

Horvat, Sebastian, Xiaoqin Gao, and Borivoje Dakić. "Universal quantum computation via quantum controlled classical operations." Journal of Physics A: Mathematical and Theoretical 55, no. 7 (January 25, 2022): 075301. http://dx.doi.org/10.1088/1751-8121/ac4393.

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Abstract A universal set of gates for (classical or quantum) computation is a set of gates that can be used to approximate any other operation. It is well known that a universal set for classical computation augmented with the Hadamard gate results in universal quantum computing. Motivated by the latter, we pose the following question: can one perform universal quantum computation by supplementing a set of classical gates with a quantum control, and a set of quantum gates operating solely on the latter? In this work we provide an affirmative answer to this question by considering a computational model that consists of 2n target bits together with a set of classical gates controlled by log (2n + 1) ancillary qubits. We show that this model is equivalent to a quantum computer operating on n qubits. Furthermore, we show that even a primitive computer that is capable of implementing only SWAP gates, can be lifted to universal quantum computing, if aided with an appropriate quantum control of logarithmic size. Our results thus exemplify the information processing power brought forth by the quantum control system.

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39

Miszczak, J. "Models of quantum computation and quantum programming languages." Bulletin of the Polish Academy of Sciences: Technical Sciences 59, no. 3 (September 1, 2011): 305–24. http://dx.doi.org/10.2478/v10175-011-0039-5.

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Models of quantum computation and quantum programming languagesThe goal of the presented paper is to provide an introduction to the basic computational models used in quantum information theory. We review various models of quantum Turing machine, quantum circuits and quantum random access machine (QRAM) along with their classical counterparts. We also provide an introduction to quantum programming languages, which are developed using the QRAM model. We review the syntax of several existing quantum programming languages and discuss their features and limitations.

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40

Roumen, Frank. "Coalgebraic Quantum Computation." Electronic Proceedings in Theoretical Computer Science 158 (July 29, 2014): 29–38. http://dx.doi.org/10.4204/eptcs.158.3.

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41

Kashefi, Elham, and Petros Wallden. "Garbled Quantum Computation." Cryptography 1, no. 1 (April 7, 2017): 6. http://dx.doi.org/10.3390/cryptography1010006.

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42

Freedman, Michael H., Alexei Kitaev, Michael J. Larsen, and Zhenghan Wang. "Topological quantum computation." Bulletin of the American Mathematical Society 40, no. 01 (October 10, 2002): 31–39. http://dx.doi.org/10.1090/s0273-0979-02-00964-3.

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43

Ekert, Artur, Marie Ericsson, Patrick Hayden, Hitoshi Inamori, Jonathan A. Jones, Daniel K. L. Oi, and Vlatko Vedral. "Geometric quantum computation." Journal of Modern Optics 47, no. 14-15 (November 2000): 2501–13. http://dx.doi.org/10.1080/09500340008232177.

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44

Sarma, Sankar Das, Michael Freedman, and Chetan Nayak. "Topological quantum computation." Physics Today 59, no. 7 (July 2006): 32–38. http://dx.doi.org/10.1063/1.2337825.

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45

Knill, E. "Resilient Quantum Computation." Science 279, no. 5349 (January 16, 1998): 342–45. http://dx.doi.org/10.1126/science.279.5349.342.

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46

Crutchfield, James P., and Karoline Wiesner. "Intrinsic quantum computation." Physics Letters A 372, no. 4 (January 2008): 375–80. http://dx.doi.org/10.1016/j.physleta.2007.07.052.

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47

Zanardi, Paolo, and Mario Rasetti. "Holonomic quantum computation." Physics Letters A 264, no. 2-3 (December 1999): 94–99. http://dx.doi.org/10.1016/s0375-9601(99)00803-8.

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48

ARRIGHI, PABLO, and LOUIS SALVAIL. "BLIND QUANTUM COMPUTATION." International Journal of Quantum Information 04, no. 05 (October 2006): 883–98. http://dx.doi.org/10.1142/s0219749906002171.

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We investigate the possibility of having someone carry out the work of executing a function for you, but without letting him learn anything about your input. Say Alice wants Bob to compute some known function f upon her input x, but wants to prevent Bob from learning anything about x. The situation arises for instance if client Alice has limited computational resources in comparison with mistrusted server Bob, or if x is an inherently mobile piece of data. Could there be a protocol whereby Bob is forced to compute ,f(x)blindly, i.e. without observing x? We provide such a blind computation protocol for the class of functions which admit an efficient procedure to generate random input–output pairs, e.g. factorization. The cheat-sensitive security achieved relies only upon quantum theory being true. The security analysis carried out assumes the eavesdropper performs individual attacks.

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49

Jones, J. A. "NMR quantum computation." Progress in Nuclear Magnetic Resonance Spectroscopy 38, no. 4 (June 2001): 325–60. http://dx.doi.org/10.1016/s0079-6565(00)00033-9.

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50

Finkelstein, David Ritz, and Giuseppe Castagnoli. "Quantum Interference Computation." International Journal of Theoretical Physics 47, no. 8 (October 10, 2007): 2158–64. http://dx.doi.org/10.1007/s10773-007-9580-2.

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