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Journal articles on the topic 'Quantum Processing'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

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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2

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

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3

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

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4

Nagy, Marius, and Naya Nagy. "Image processing: why quantum?" Quantum Information and Computation 20, no. 7&8 (June 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 fundamental flaw: only one pixel can be recovered from the quantum representation of the entire image, and even that one is obtained non-deterministically through a measurement operation applied on the superposition state. We investigate in detail this measurement bottleneck problem by looking at the number of copies of the quantum representation that are necessary in order to recover various fractions of the original image. The results clearly show that any potential advantage a quantum representation might bring with respect to a classical one is paid for dearly with the huge amount of resources (space and time) required by a quantum approach to image processing.
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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 (August 24, 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 (December 2003): 759–81. http://dx.doi.org/10.1007/s00023-003-0960-8.

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7

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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8

KIM, Jaewan. "Quantum Physics and Information Processing: Quantum Computers." Physics and High Technology 21, no. 12 (December 31, 2012): 21. http://dx.doi.org/10.3938/phit.21.052.

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9

Benhelm, J., G. Kirchmair, R. Gerritsma, F. Zähringer, T. Monz, P. Schindler, M. Chwalla, 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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10

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 (March 5, 2014): 075007. http://dx.doi.org/10.1088/0264-9381/31/7/075007.

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11

Knight, P. "QUANTUM COMPUTING:Enhanced: Quantum Information Processing Without Entanglement." Science 287, no. 5452 (January 21, 2000): 441–42. http://dx.doi.org/10.1126/science.287.5452.441.

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12

An-Min, Wang. "Quantum Central Processing Unit and Quantum Algorithm." Chinese Physics Letters 19, no. 5 (April 18, 2002): 620–22. http://dx.doi.org/10.1088/0256-307x/19/5/304.

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13

Troiani, F., U. Hohenester, and E. Molinari. "Quantum-Information Processing in Semiconductor Quantum Dots." physica status solidi (b) 224, no. 3 (April 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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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 (April 25, 2015): 212–31. http://dx.doi.org/10.1007/s10773-015-2652-9.

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15

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 (January 23, 2017): 1600621. http://dx.doi.org/10.1002/pssb.201600621.

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16

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 (October 2004): 15–44. http://dx.doi.org/10.1007/s11128-004-3668-x.

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17

Kok, Pieter. "Photonic quantum information processing." Contemporary Physics 57, no. 4 (May 10, 2016): 526–44. http://dx.doi.org/10.1080/00107514.2016.1178472.

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18

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 (January 15, 2000): 261–79. http://dx.doi.org/10.1098/rsta.2000.0531.

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19

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 this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).
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20

ALTAISKY, MIKHAIL V., and NATALIA E. KAPUTKINA. "QUANTUM HIERARCHIC MODELS FOR INFORMATION PROCESSING." International Journal of Quantum Information 10, no. 02 (March 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 constructing reliable and compact registers of quantum bits. We suggest accessing the states of a quantum register hierarchically, descending from the state of the whole register to the states of its parts. Our method is similar to quantum wavelet transform, and can be applied to information compression, quantum memory, quantum computations.
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21

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 (January 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 that it is elementary, relying only on basic properties of projectors, Pythagoras' theorem, and the Cauchy–Schwarz inequality. As a non-trivial application of our quantum union bound, we prove that a sequential decoding strategy for classical communication over a quantum channel achieves a lower bound on the channel's second-order coding rate. This demonstrates the advantage of our quantum union bound in the non-asymptotic regime, in which a communication channel is called a finite number of times. We expect that the bound will find a range of applications in quantum communication theory, quantum algorithms and quantum complexity theory.
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22

Wu, Shuyue, and Jingfang Wang. "A Quantum Pointer Signal Processing Research." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (June 1, 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 pointer, and the properties of bi-direction and sub-block are used, the storing and other operations of quantum gray-scale image are simpler and more convenient. </span>
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23

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

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24

Venegas-Andraca, Salvador Elías. "Introductory words: Special issue on quantum image processing published by Quantum Information Processing." Quantum Information Processing 14, no. 5 (April 30, 2015): 1535–37. http://dx.doi.org/10.1007/s11128-015-1001-5.

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25

OMAR, YASSER. "PARTICLE STATISTICS IN QUANTUM INFORMATION PROCESSING." International Journal of Quantum Information 03, no. 01 (March 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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26

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 (March 2010): 030313. http://dx.doi.org/10.1088/1674-1056/19/3/030313.

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27

Long, Gui Lu. "Duality Quantum Computing and Duality Quantum Information Processing." International Journal of Theoretical Physics 50, no. 4 (December 1, 2010): 1305–18. http://dx.doi.org/10.1007/s10773-010-0603-z.

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28

Yamamoto, Y. "Quantum Communication and Information Processing with Quantum Dots." Quantum Information Processing 5, no. 5 (August 29, 2006): 299–311. http://dx.doi.org/10.1007/s11128-006-0027-0.

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29

Stobińska, M., A. Buraczewski, M. Moore, W. R. Clements, J. J. Renema, S. W. Nam, T. Gerrits, et al. "Quantum interference enables constant-time quantum information processing." Science Advances 5, no. 7 (July 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 vision but features a prohibitive runtime. Here, we report a one-step computation of a fractional quantum KT. The quantum d-nary (qudit) architecture we use comprises only one gate and offers processing time independent of the input size. The gate may use a multiphoton Hong-Ou-Mandel effect. Existing quantum technologies may scale it up toward diverse applications.
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30

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

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31

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 (February 2002): 117–25. http://dx.doi.org/10.1006/spmi.2002.1033.

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32

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 (December 9, 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 of our design in terms of quantum cost, quantum delay and ancillary bits. Finally, the simulation verifies the correctness of our design.
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33

Monras, A., and O. Romero-Isart. "Quantum information processing with quantum zeno many-body dynamics." Quantum Information and Computation 10, no. 3&4 (March 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 quantum computation can be achieved in a spin chain with an {\em always-on} and {\em constant} many-body Hamiltonian. We also show some possible modifications of the initially assumed dynamics in order to create maximally entangled qubit pairs and single qubit gates.
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34

RAHIMI, ROBABEH, KAZUNOBU SATO, KOU FURUKAWA, KAZUO TOYOTA, DAISUKE SHIOMI, TOSHIHIRO NAKAMURA, MASAHIRO KITAGAWA, and TAKEJI TAKUI. "PULSED ENDOR-BASED QUANTUM INFORMATION PROCESSING." International Journal of Quantum Information 03, supp01 (November 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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35

Laflamme, R., D. Cory, C. Negrevergne, and L. Viola. "NMR quantum information processing and entanglement." Quantum Information and Computation 2, no. 2 (February 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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36

Wrachtrup, Jörg, and Fedor Jelezko. "Processing quantum information in diamond." Journal of Physics: Condensed Matter 18, no. 21 (May 12, 2006): S807—S824. http://dx.doi.org/10.1088/0953-8984/18/21/s08.

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37

Delaubert, V., N. Treps, C. Fabre, H. A. Bachor, and P. Réfrégier. "Quantum limits in image processing." EPL (Europhysics Letters) 81, no. 4 (January 18, 2008): 44001. http://dx.doi.org/10.1209/0295-5075/81/44001.

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38

Gough, John E., and Viacheslav P. Belavkin. "Quantum control and information processing." Quantum Information Processing 12, no. 3 (October 18, 2012): 1397–415. http://dx.doi.org/10.1007/s11128-012-0491-7.

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39

Mahesh, T. S. "Quantum information processing by NMR." Resonance 20, no. 11 (November 2015): 1053–65. http://dx.doi.org/10.1007/s12045-015-0273-5.

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40

Zoller, P., Th Beth, D. Binosi, R. Blatt, H. Briegel, D. Bruss, T. Calarco, et al. "Quantum information processing and communication." European Physical Journal D 36, no. 2 (September 13, 2005): 203–28. http://dx.doi.org/10.1140/epjd/e2005-00251-1.

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41

Munro, W. J., Kae Nemoto, T. P. Spiller, S. D. Barrett, Pieter Kok, and R. G. Beausoleil. "Efficient optical quantum information processing." Journal of Optics B: Quantum and Semiclassical Optics 7, no. 7 (June 30, 2005): S135—S140. http://dx.doi.org/10.1088/1464-4266/7/7/002.

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42

Andersen, U. L., G. Leuchs, and C. Silberhorn. "Continuous-variable quantum information processing." Laser & Photonics Reviews 4, no. 3 (July 13, 2009): 337–54. http://dx.doi.org/10.1002/lpor.200910010.

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43

Furusawa, Akira. "Perspective on hybrid quantum information processing: a method for large-scale quantum information processing." Journal of Optics 19, no. 7 (June 6, 2017): 070401. http://dx.doi.org/10.1088/2040-8986/aa72fc.

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44

Ren, Wanghao, Zhiming Li, Yiming Huang, Runqiu Guo, Lansheng Feng, Hailing Li, Yang Li, and Xiaoyu Li. "Quantum generative adversarial networks for learning and loading quantum image in noisy environment." Modern Physics Letters B 35, no. 21 (June 9, 2021): 2150360. http://dx.doi.org/10.1142/s0217984921503607.

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Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.
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45

Serra, R. M., and I. S. Oliveira. "Nuclear magnetic resonance quantum information processing." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1976 (October 13, 2012): 4615–19. http://dx.doi.org/10.1098/rsta.2012.0332.

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For the past decade, nuclear magnetic resonance (NMR) has been established as a main experimental technique for testing quantum protocols in small systems. This Theme Issue presents recent advances and major challenges of NMR quantum information possessing (QIP), including contributions by researchers from 10 different countries. In this introduction, after a short comment on NMR-QIP basics, we briefly anticipate the contents of this issue.
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46

Meichanetzidis, Konstantinos, Stefano Gogioso, Giovanni de Felice, Nicolò Chiappori, Alexis Toumi, and Bob Coecke. "Quantum Natural Language Processing on Near-Term Quantum Computers." Electronic Proceedings in Theoretical Computer Science 340 (September 6, 2021): 213–29. http://dx.doi.org/10.4204/eptcs.340.11.

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47

Burkard, G., and D. Loss. "Quantum Information Processing Using Electron Spins in Quantum Dots." Acta Physica Polonica A 100, no. 2 (August 2001): 109–27. http://dx.doi.org/10.12693/aphyspola.100.109.

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48

Xu, Wenling, Tiejun Wang, Cong Cao, and Chuan Wang. "High dimensional quantum logic gates and quantum information processing." Chinese Science Bulletin 64, no. 16 (May 9, 2019): 1691–701. http://dx.doi.org/10.1360/n972019-00252.

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49

Deng, Fu-Guo, Bao-Cang Ren, and Xi-Han Li. "Quantum hyperentanglement and its applications in quantum information processing." Science Bulletin 62, no. 1 (January 2017): 46–68. http://dx.doi.org/10.1016/j.scib.2016.11.007.

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50

Jacak, L., J. Krasnyj, D. Jacak, R. Gonczarek, M. Krzyżosiak, and P. Machnikowski. "Spin-Based Quantum Information Processing in Magnetic Quantum Dots." Open Systems & Information Dynamics 12, no. 02 (June 2005): 133–41. http://dx.doi.org/10.1007/s11080-005-5724-0.

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We define the qubit as a pair of singlet and triplet states of two electrons in a He-type quantum dot (QD) placed in a diluted magnetic semiconductor (DMS) medium. The molecular field is here essential as it removes the degeneracy of the triplet state and strongly enhances the Zeeman splitting. Methods of qubit rotation as well as two-qubit operations are suggested. The system of a QD in a DMS is described in a way which allows an analysis of the decoherence due to spin waves in the DMS subsystem.
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