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Journal articles on the topic 'Quantum Processing'

Author: Grafiati

Published: 9 March 2023

Last updated: 28 July 2025

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1

Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discu

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2

Ahlswede, R., and P. Lober. "Quantum data processing." IEEE Transactions on Information Theory 47, no. 1 (2001): 474–78. http://dx.doi.org/10.1109/18.904565.

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3

Eldar, Y. C., and A. V. Oppenheim. "Quantum signal processing." IEEE Signal Processing Magazine 19, no. 6 (2002): 12–32. http://dx.doi.org/10.1109/msp.2002.1043298.

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4

Ferry, D. K., R. Akis, and J. Harris. "Quantum wave processing." Superlattices and Microstructures 30, no. 2 (2001): 81–94. http://dx.doi.org/10.1006/spmi.2001.0998.

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5

Qiang, Xiaogang, Xiaoqi Zhou, Kanin Aungskunsiri, Hugo Cable, and Jeremy L. O’Brien. "Quantum processing by remote quantum control." Quantum Science and Technology 2, no. 4 (2017): 045002. http://dx.doi.org/10.1088/2058-9565/aa78d6.

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6

Cirac, J. I., L. M. Duan, D. Jaksch, and P. Zoller. "Quantum Information Processing with Quantum Optics." Annales Henri Poincaré 4, S2 (2003): 759–81. http://dx.doi.org/10.1007/s00023-003-0960-8.

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7

Nagy, Marius, and Naya Nagy. "Image processing: why quantum?" Quantum Information and Computation 20, no. 7&8 (2020): 616–26. http://dx.doi.org/10.26421/qic20.7-8-6.

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Quantum Image Processing has exploded in recent years with dozens of papers trying to take advantage of quantum parallelism in order to offer a better alternative to how current computers are dealing with digital images. The vast majority of these papers define or make use of quantum representations based on very large superposition states spanning as many terms as there are pixels in the image they try to represent. While such a representation may apparently offer an advantage in terms of space (number of qubits used) and speed of processing (due to quantum parallelism), it also harbors a fun

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8

TAKEOKA, Masahiro, and Masahide SASAKI. "Introduction to Optical Quantum Information Processing 3. Quantum Information Processing Protocols and Quantum Computation." Review of Laser Engineering 33, no. 1 (2005): 57–61. http://dx.doi.org/10.2184/lsj.33.57.

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9

KIM, Jaewan. "Quantum Physics and Information Processing: Quantum Computers." Physics and High Technology 21, no. 12 (2012): 21. http://dx.doi.org/10.3938/phit.21.052.

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Benhelm, J., G. Kirchmair, R. Gerritsma, et al. "Ca+quantum bits for quantum information processing." Physica Scripta T137 (December 2009): 014008. http://dx.doi.org/10.1088/0031-8949/2009/t137/014008.

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11

Benincasa, Dionigi M. T., Leron Borsten, Michel Buck, and Fay Dowker. "Quantum information processing and relativistic quantum fields." Classical and Quantum Gravity 31, no. 7 (2014): 075007. http://dx.doi.org/10.1088/0264-9381/31/7/075007.

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12

Knight, P. "QUANTUM COMPUTING:Enhanced: Quantum Information Processing Without Entanglement." Science 287, no. 5452 (2000): 441–42. http://dx.doi.org/10.1126/science.287.5452.441.

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13

An-Min, Wang. "Quantum Central Processing Unit and Quantum Algorithm." Chinese Physics Letters 19, no. 5 (2002): 620–22. http://dx.doi.org/10.1088/0256-307x/19/5/304.

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14

Deng, Y., M. X. Luo, and S. Y. Ma. "Efficient Quantum Information Processing via Quantum Compressions." International Journal of Theoretical Physics 55, no. 1 (2015): 212–31. http://dx.doi.org/10.1007/s10773-015-2652-9.

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15

Troiani, F., U. Hohenester, and E. Molinari. "Quantum-Information Processing in Semiconductor Quantum Dots." physica status solidi (b) 224, no. 3 (2001): 849–53. http://dx.doi.org/10.1002/(sici)1521-3951(200104)224:3<849::aid-pssb849>3.0.co;2-q.

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16

Roussel, B., C. Cabart, G. Fève, E. Thibierge, and P. Degiovanni. "Electron quantum optics as quantum signal processing." physica status solidi (b) 254, no. 3 (2017): 1600621. http://dx.doi.org/10.1002/pssb.201600621.

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17

Dong, Yulong, Lin Lin, Hongkang Ni, and Jiasu Wang. "Infinite quantum signal processing." Quantum 8 (December 10, 2024): 1558. https://doi.org/10.22331/q-2024-12-10-1558.

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Quantum signal processing (QSP) represents a real scalar polynomial of degree d using a product of unitary matrices of size 2&#x00D7;2, parameterized by (d+1) real numbers called the phase factors. This innovative representation of polynomials has a wide range of applications in quantum computation. When the polynomial of interest is obtained by truncating an infinite polynomial series, a natural question is whether the phase factors have a well defined limit as the degree d&#x2192;&#x221E;. While the phase factors are generally not unique, we find that there exists a consistent ch

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18

Ramanathan, Chandrasekhar, Nicolas Boulant, Zhiying Chen, David G. Cory, Isaac Chuang, and Matthias Steffen. "NMR Quantum Information Processing." Quantum Information Processing 3, no. 1-5 (2004): 15–44. http://dx.doi.org/10.1007/s11128-004-3668-x.

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19

Kok, Pieter. "Photonic quantum information processing." Contemporary Physics 57, no. 4 (2016): 526–44. http://dx.doi.org/10.1080/00107514.2016.1178472.

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20

Mosca, M., R. Jozsa, A. Steane, and A. Ekert. "Quantum–enhanced information processing." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 358, no. 1765 (2000): 261–79. http://dx.doi.org/10.1098/rsta.2000.0531.

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21

Ruan, Yue, Xiling Xue, and Yuanxia Shen. "Quantum Image Processing: Opportunities and Challenges." Mathematical Problems in Engineering 2021 (January 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/6671613.

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Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing

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22

LEE, Donghwa, and Yong-Su KIM. "Quantum Information Processing Technology Based on Quantum Optics." Physics and High Technology 32, no. 11 (2023): 23–28. http://dx.doi.org/10.3938/phit.32.031.

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We outline ongoing endeavors in the development of quantum information processing technology utilizing quantum optics. We highlight the distinctive attributes of quantum optical platforms and explore two distinct approaches: discrete variable and continuous variable quantum optics, for the realization of quantum information processing. In addition, we showcase recent achievements in the implementation of quantum simulators, aiming to address practical challenges using today’s available technologies.

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23

ALTAISKY, MIKHAIL V., and NATALIA E. KAPUTKINA. "QUANTUM HIERARCHIC MODELS FOR INFORMATION PROCESSING." International Journal of Quantum Information 10, no. 02 (2012): 1250026. http://dx.doi.org/10.1142/s0219749912500268.

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Both classical and quantum computations operate with the registers of bits. At nanometer scale the quantum fluctuations at the position of a given bit, say, a quantum dot, not only lead to the decoherence of quantum state of this bit, but also affect the quantum states of the neighboring bits, and therefore affect the state of the whole register. That is why the requirement of reliable separate access to each bit poses the limit on miniaturization, i.e. constrains the memory capacity and the speed of computation. In the present paper we suggest an algorithmic way to tackle the problem of const

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24

Sarah, L. Johnson. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 31–41. https://doi.org/10.5281/zenodo.15147384.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behi

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25

Khabbazi Oskouei, Samad, Stefano Mancini, and Mark M. Wilde. "Union bound for quantum information processing." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (2019): 20180612. http://dx.doi.org/10.1098/rspa.2018.0612.

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In this paper, we prove a quantum union bound that is relevant when performing a sequence of binary-outcome quantum measurements on a quantum state. The quantum union bound proved here involves a tunable parameter that can be optimized, and this tunable parameter plays a similar role to a parameter involved in the Hayashi–Nagaoka inequality (Hayashi & Nagaoka 2003 IEEE Trans. Inf. Theory 49 , 1753–1768. ( doi:10.1109/TIT.2003.813556 )), used often in quantum information theory when analysing the error probability of a square-root measurement. An advantage of the proof delivered here is tha

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26

Wu, Shuyue, and Jingfang Wang. "A Quantum Pointer Signal Processing Research." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (2016): 675. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp675-683.

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<span lang="EN-US">In quantum gray-scale image processing, the storage in quantum states is the color information and the position information According to the advantage of small range of the gray scale in a gray-scale image, a novel storage expression of quantum gray-scale image is proposed and demonstrated in this study. Besides, a new concept of "quantum pointer" is put forward based on the expression. Quantum pointer is the vinculum between the information of gray-scale and position of each pixel in quantum gray-scale images. The feasibility is verified for the proposed quantum point

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27

Aiqing WANG. "Quantum ComputingDriven Social Data Processing Methods." Quantum Social Science 1, no. 1 (2025): 93–116. https://doi.org/10.6914/qss.010103.

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Quantum computing introduces a revolutionary computational paradigm for social data analysis. Traditional social data processing methods face significant challenges in handling high-dimensional, heterogeneous, and dynamic datasets. Quantum computing, leveraging quantum superposition, entanglement, and parallel computation, provides efficient solutions for large-scale optimization, pattern recognition, and decision analysis. This paper systematically explores the applications of quantum computing in social sciences, covering theoretical foundations, methodological frameworks, experimental valid

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28

Sungheetha, Akey. "Applications and Challenges of Quantum Image Processing – A Comprehensive Review." Recent Research Reviews Journal 2, no. 1 (2023): 112–21. http://dx.doi.org/10.36548/rrrj.2023.1.09.

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The tremendous parallel processing capability of quantum computers allow quantum image processing, a multidisciplinary field combining image processing and quantum computing, to expand the potential outcomes for image processing. The problem of quantum computation is to create effective quantum algorithms since quantum computers need extremely effective algorithms than classical algorithm. Additionally, information storage, communication, and computing power are increasing in relevance with the number and importance of processing digital images. Some of these issues might be resolved by encodi

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29

Blais, Alexandre, Steven M. Girvin, and William D. Oliver. "Quantum information processing and quantum optics with circuit quantum electrodynamics." Nature Physics 16, no. 3 (2020): 247–56. http://dx.doi.org/10.1038/s41567-020-0806-z.

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30

Stamatopoulos, Nikitas, and William J. Zeng. "Derivative Pricing using Quantum Signal Processing." Quantum 8 (April 30, 2024): 1322. http://dx.doi.org/10.22331/q-2024-04-30-1322.

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Pricing financial derivatives on quantum computers typically includes quantum arithmetic components which contribute heavily to the quantum resources required by the corresponding circuits. In this manuscript, we introduce a method based on Quantum Signal Processing (QSP) to encode financial derivative payoffs directly into quantum amplitudes, alleviating the quantum circuits from the burden of costly quantum arithmetic. Compared to current state-of-the-art approaches in the literature, we find that for derivative contracts of practical interest, the application of QSP significantly reduces th

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31

Smt., Maheshwari. S. Hiremath. ""A Delineate On Quantum Computing"." International Journal of Advance and Applied Research 4, no. 10 (2023): 144–47. https://doi.org/10.5281/zenodo.7820731.

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What makes a computer a computer is the processor; size over time. The number of processors is decreasing and the processing speed is steadily increasing current size. The processor is very small, but in the future, it will be the size of an atom. It will not be possible for classic computers to have such a small processor and delivered a huge amount of processing speed. Here the quantum computer takes the lead. On quantum mechanical phenomena like overlap, entanglement, and tunneling, among others, quantum computers are founded. In this report, we're going to talk about quantum computers,

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32

Sinanan-Singh, Jasmine, Gabriel L. Mintzer, Isaac L. Chuang, and Yuan Liu. "Single-shot Quantum Signal Processing Interferometry." Quantum 8 (July 30, 2024): 1427. http://dx.doi.org/10.22331/q-2024-07-30-1427.

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Quantum systems of infinite dimension, such as bosonic oscillators, provide vast resources for quantum sensing. Yet, a general theory on how to manipulate such bosonic modes for sensing beyond parameter estimation is unknown. We present a general algorithmic framework, quantum signal processing interferometry (QSPI), for quantum sensing at the fundamental limits of quantum mechanics by generalizing Ramsey-type interferometry. Our QSPI sensing protocol relies on performing nonlinear polynomial transformations on the oscillator&apos;s quadrature operators by generalizing quantum signal proce

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33

Venegas-Andraca, Salvador Elías. "Introductory words: Special issue on quantum image processing published by Quantum Information Processing." Quantum Information Processing 14, no. 5 (2015): 1535–37. http://dx.doi.org/10.1007/s11128-015-1001-5.

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34

Xia, Hai-Ying, Han Zhang, Shu-Xiang Song, Haisheng Li, Yi-Jie Zhou, and Xiao Chen. "Design and simulation of quantum image binarization using quantum comparator." Modern Physics Letters A 35, no. 09 (2019): 2050049. http://dx.doi.org/10.1142/s0217732320500492.

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Compared with classical image processing, quantum image processing provides a possible solution for faster image processing, which has been widely concerned. Quantum image binarization is a basic operation and plays an important role in image processing. Hence, we proposed an efficient design of quantum image binarization using quantum comparator. To reduce quantum cost and quantum delay, the comparator was optimized by rearranging the quantum gates. Then, a complete circuit implementation of quantum image binarization was designed using the comparator. Furthermore, we analyzed the performance

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35

Jun, Liu, Wang Qiong, Kuang Le-Man, and Zeng Hao-Sheng. "Distributed quantum information processing via quantum dot spins." Chinese Physics B 19, no. 3 (2010): 030313. http://dx.doi.org/10.1088/1674-1056/19/3/030313.

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36

Long, Gui Lu. "Duality Quantum Computing and Duality Quantum Information Processing." International Journal of Theoretical Physics 50, no. 4 (2010): 1305–18. http://dx.doi.org/10.1007/s10773-010-0603-z.

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37

Yamamoto, Y. "Quantum Communication and Information Processing with Quantum Dots." Quantum Information Processing 5, no. 5 (2006): 299–311. http://dx.doi.org/10.1007/s11128-006-0027-0.

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38

Stobińska, M., A. Buraczewski, M. Moore, et al. "Quantum interference enables constant-time quantum information processing." Science Advances 5, no. 7 (2019): eaau9674. http://dx.doi.org/10.1126/sciadv.aau9674.

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It is an open question how fast information processing can be performed and whether quantum effects can speed up the best existing solutions. Signal extraction, analysis, and compression in diagnostics, astronomy, chemistry, and broadcasting build on the discrete Fourier transform. It is implemented with the fast Fourier transform (FFT) algorithm that assumes a periodic input of specific lengths, which rarely holds true. A lesser-known transform, the Kravchuk-Fourier (KT), allows one to operate on finite strings of arbitrary length. It is of high demand in digital image processing and computer

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39

An-Min, Wang. "A Universal Quantum Network-Quantum Central Processing Unit." Chinese Physics Letters 18, no. 2 (2001): 166–68. http://dx.doi.org/10.1088/0256-307x/18/2/304.

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40

D’Amico, Irene, Eliana Biolatti, Fausto Rossi, Sergio DeRinaldis, Ross Rinaldis, and Roberto Cingolani. "GaN quantum dot based quantum information/computation processing." Superlattices and Microstructures 31, no. 2-4 (2002): 117–25. http://dx.doi.org/10.1006/spmi.2002.1033.

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41

Mitarai, Kosuke, Kiichiro Toyoizumi, and Wataru Mizukami. "Perturbation theory with quantum signal processing." Quantum 7 (May 12, 2023): 1000. http://dx.doi.org/10.22331/q-2023-05-12-1000.

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Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provide a quantum algorithm to obtain perturbative energies on quantum computers. The benefit of using quantum computers is that we can start the perturbation from a Hamiltonian that is classically hard to solve. The proposed algorithm uses quantum signal processing (QSP) to achieve this goal. Along with the perturbation theory, we construct a technique for ground state preparation with detailed computational cost analysi

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42

OMAR, YASSER. "PARTICLE STATISTICS IN QUANTUM INFORMATION PROCESSING." International Journal of Quantum Information 03, no. 01 (2005): 201–5. http://dx.doi.org/10.1142/s021974990500075x.

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Particle statistics is a fundamental part of quantum physics, and yet its role and use in the context of quantum information have been poorly explored so far. After briefly introducing particle statistics and the Symmetrization Postulate, we argue that this fundamental aspect of nature can be seen as a resource for quantum information processing and present examples showing how it is possible to do useful and efficient quantum information processing using only the effects of particle statistics.

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43

Monras, A., and O. Romero-Isart. "Quantum information processing with quantum zeno many-body dynamics." Quantum Information and Computation 10, no. 3&4 (2010): 201–22. http://dx.doi.org/10.26421/qic10.3-4-3.

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We show how the quantum Zeno effect can be exploited to control quantum many-body dynamics for quantum information and computation purposes. In particular, we consider a one dimensional array of three level systems interacting via a nearest-neighbour interaction. By encoding the qubit on two levels and using simple projective frequent measurements yielding the quantum Zeno effect, we demonstrate how to implement a well defined quantum register, quantum state transfer on demand, universal two-qubit gates and two-qubit parity measurements. Thus, we argue that the main ingredients for universal q

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44

Ayorinde, Akindemowo Olayiwola. "Quantum Signal Processing in Strengthening Cyber Defense: A Comparative Study." Current Journal of Applied Science and Technology 43, no. 7 (2024): 37–46. http://dx.doi.org/10.9734/cjast/2024/v43i74404.

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Aim: To examine the strengthening of cyber defense using Quantum Signal Processing. Significance of Study: There is need for the application of Quantum Signal Processing in cyber security due to the vulnerable attack. This paper addresses the relevant issues on the use of Quantum Signal Processing in strengthening cyber defense. Problem Statement: The development of cyber space and its inestimable contribution in information dissemination has led to its wide patronage and usage. Thus, third party are intruding to hijack and hack secretive information to suite their own purpose. Discussion: The

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45

Tao, Yunpeng. "Quantum entanglement: Principles and research progress in quantum information processing." Theoretical and Natural Science 30, no. 1 (2024): 263–74. http://dx.doi.org/10.54254/2753-8818/30/20241130.

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Quantum entanglement is a peculiar phenomenon in quantum information science, characterized by nonclassical correlations between quantum states of subsystems in a quantum system. Since the proposal of the Einstein-Podolsky-Rosen (EPR) paradox by Einstein, Podolsky, and Rosen, quantum entanglement has sparked intense debates on local realism. Bells inequality experiment established the nonlocality of quantum mechanics. Currently, high-dimensional quantum entanglement of both deterministic and random states can be realized in systems such as photons and cold atoms. Technologies such as quantum t

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46

RAHIMI, ROBABEH, KAZUNOBU SATO, KOU FURUKAWA, et al. "PULSED ENDOR-BASED QUANTUM INFORMATION PROCESSING." International Journal of Quantum Information 03, supp01 (2005): 197–204. http://dx.doi.org/10.1142/s0219749905001377.

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Pulsed Electron Nuclear DOuble Resonance (pulsed ENDOR) has been studied for realization of quantum algorithms, emphasizing the implementation of organic molecular entities with an electron spin and a nuclear spin for quantum information processing. The scheme has been examined in terms of quantum information processing. Particularly, superdense coding has been implemented from the experimental side and the preliminary results are represented as theoretical expectations.

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47

Laflamme, R., D. Cory, C. Negrevergne, and L. Viola. "NMR quantum information processing and entanglement." Quantum Information and Computation 2, no. 2 (2002): 166–76. http://dx.doi.org/10.26421/qic2.2-5.

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In this essay we discuss the issue of quantum information and recent nuclear magnetic resonance (NMR) experiments. We explain why these experiments should be regarded as quantum information processing (QIP) despite the fact that, in present liquid state NMR experiments, no entanglement is found. We comment on how these experiments contribute to the future of QIP and include a brief discussion on the origin of the power of quantum computers.

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48

Alicki, Robert. "Stability versus reversibility in information processing." International Journal of Modern Physics: Conference Series 33 (January 2014): 1460353. http://dx.doi.org/10.1142/s2010194514603536.

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The paper is motivated by the discussion of feasibility of large scale quantum computations which should incorporate both unitarity of quantum dynamics for information bearing degrees of freedom and stability with respect to environmental noise. The minimal thermodynamic cost of a single CNOT gate, which is equivalent to the minimal cost of a quantum measurement of a binary observable is analyzed using a generic quantum model of one bit memory. For this model stability of memory with respect to thermal and quantum noise and the error of readout can be quantified. One obtains the relations betw

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49

Wrachtrup, Jörg, and Fedor Jelezko. "Processing quantum information in diamond." Journal of Physics: Condensed Matter 18, no. 21 (2006): S807—S824. http://dx.doi.org/10.1088/0953-8984/18/21/s08.

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50

Paesani, Stefano. "Processing quantum randomness with light." Nature Photonics 19, no. 1 (2025): 5–6. https://doi.org/10.1038/s41566-024-01606-9.

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