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wesley7137
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quantum

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are particularly useful for gate-based quantum computations and gate-model quantum computer
architectures.
Acknowledgements
TheresearchreportedinthispaperhasbeensupportedbytheNationalResearch,Developmentand
Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program). This work was par-
tially supported by the National Research Development and Innovation Office of Hungary (Project
No. 2017-1.2.1-NKP-2017-00001), by the Hungarian Scientific Research Fund - OTKA K-112125
and in part by the BME Artificial Intelligence FIKP grant of EMMI (BME FIKP-MI/SC).
References
[1] Preskill, J. Quantum Computing in the NISQ era and beyond, Quantum 2, 79 (2018).
[2] Harrow, A. W. and Montanaro, A. Quantum Computational Supremacy, Nature, vol 549,
pages 203-209 (2017).
16
[3] Aaronson, S. and Chen, L. Complexity-theoretic foundations of quantum supremacy experi-
ments. Proceedings of the 32nd Computational Complexity Conference, CCC ’17, pages 22:1-
22:67, (2017).
[4] Biamonte, J. et al. Quantum Machine Learning. Nature, 549, 195-202 (2017).
[5] LeCun, Y., Bengio, Y. and Hinton, G. Deep Learning. Nature 521, 436-444 (2014).
[6] Goodfellow, I., Bengio, Y.andCourville, A.Deep Learning.MITPress.Cambridge, MA,2016.
[7] Debnath, S. et al. Demonstration of a small programmable quantum computer with atomic
qubits. Nature 536, 63-66 (2016).
[8] Monz, T. et al. Realization of a scalable Shor algorithm. Science 351, 1068-1070 (2016).
[9] Barends, R. et al. Superconducting quantum circuits at the surface code threshold for fault
tolerance. Nature 508, 500-503 (2014).
[10] Kielpinski,D.,Monroe,C.andWineland,D.J.Architectureforalarge-scaleion-trapquantum
computer. Nature 417, 709-711 (2002).
[11] Ofek,N.etal.Extendingthelifetimeofaquantumbitwitherrorcorrectioninsuperconducting
circuits. Nature 536, 441-445 (2016).
[12] Farhi, E., Goldstone, J. and Gutmann, S. A Quantum Approximate Optimization Algorithm.
arXiv:1411.4028. (2014).
[13] Farhi, E., Goldstone, J., Gutmann, S. and Neven, H. Quantum Algorithms for Fixed Qubit
Architectures. arXiv:1703.06199v1 (2017).
[14] Farhi, E. and Neven, H. Classification with Quantum Neural Networks on Near Term Proces-
sors, arXiv:1802.06002v1 (2018).
[15] Farhi, E., Goldstone, J. and Gutmann, S. A Quantum Approximate Optimization Algorithm
Applied to a Bounded Occurrence Constraint Problem. arXiv:1412.6062. (2014).
[16] Rebentrost, P., Mohseni, M. and Lloyd, S. Quantum Support Vector Machine for Big Data
Classification. Phys. Rev. Lett. 113. (2014).
[17] Lloyd, S. The Universe as Quantum Computer, A Computable Universe: Understand-
ing and exploring Nature as computation, H. Zenil ed., World Scientific, Singapore, 2012,
arXiv:1312.4455v1 (2013).
[18] Lloyd, S., Mohseni, M. and Rebentrost, P. Quantum algorithms for supervised and unsuper-
vised machine learning, arXiv:1307.0411v2 (2013).
[19] Lloyd, S., Garnerone, S. and Zanardi, P. Quantum algorithms for topological and geometric
analysis of data. Nat. Commun., 7, arXiv:1408.3106 (2016).
[20] Lloyd, S., Shapiro, J. H., Wong, F. N. C., Kumar, P., Shahriar, S. M. and Yuen, H. P.
Infrastructure for the quantum Internet. ACM SIGCOMM Computer Communication Review,
34, 9-20 (2004).
17
[21] Lloyd, S., Mohseni, M. and Rebentrost, P. Quantum principal component analysis. Nature
Physics, 10, 631 (2014).
[22] Gyongyosi, L., Imre, S. and Nguyen, H. V. A Survey on Quantum Channel Capacities, IEEE
Communications Surveys and Tutorials 99, 1, doi: 10.1109/COMST.2017.2786748 (2018).
[23] Schuld, M., Sinayskiy, I. and Petruccione, F. An introduction to quantum machine learning.
Contemporary Physics 56, pp. 172-185. arXiv: 1409.3097 (2015).
[24] Van Meter, R. Quantum Networking, John Wiley and Sons Ltd, ISBN 1118648927,
9781118648926 (2014).
[25] Imre, S. and Gyongyosi, L. Advanced Quantum Communications - An Engineering Approach.
Wiley-IEEE Press (New Jersey, USA), (2012).
[26] Pirandola, S., Laurenza, R., Ottaviani, C. and Banchi, L. Fundamental limits of repeaterless
quantum communications, Nature Communications, 15043, doi:10.1038/ncomms15043 (2017).
[27] Pirandola, S., Braunstein, S. L., Laurenza, R., Ottaviani, C., Cope, T. P. W., Spedalieri, G.
andBanchi, L.Theoryofchannelsimulationandboundsforprivatecommunication, Quantum
Sci. Technol. 3, 035009 (2018).
[28] Pirandola, S. Capacities of repeater-assisted quantum communications, arXiv:1601.00966
(2016).
[29] Pirandola, S. End-to-end capacities of a quantum communication network, Commun. Phys. 2,
51 (2019).
[30] Petz, D. Quantum Information Theory and Quantum Statistics, Springer-Verlag, Heidelberg,
Hiv: 6. (2008).
[31] Chawky, B. S., Elons, A. S., Ali, A. and Shedeed, H. A. A Study of Action Recognition
Problems: Dataset and Architectures Perspectives, In: Hassanien, A. E. and Oliva, D. A.
(eds.), Advances in Soft Computing and Machine Learning in Image Processing, Studies in
Computational Intelligence 730 (2018).
[32] Miao, J., Xu, X., Xing, X. and Tao, D. Manifold Regularized Slow Feature Analysis for
Dynamic Texture Recognition, arXiv:1706.03015v1 (2017).
[33] Wiskott, L. and Sejnowski, T. J. Slow feature analysis: Unsupervised learning of invariances,
Neural Computation, vol. 14, pp. 715–770, (2002).
[34] Mika, S., Scholkopf, B., Smola, A., Muller, K. R., Scholz, M. and Ratsch, G. Kernel pca
and de-noising in feature spaces, Advances in Neural Information Processing Systems 11. pp.
536–542, MIT Press, (1999).
[35] Shawe-Taylor, J. and Cristianini, N. Kernel Methods for Pattern Analysis. Cambridge Univer-
sity Press (2004).
[36] Liu,W.,Principe,J.andHaykin,S.KernelAdaptiveFiltering: AComprehensiveIntroduction.
Wiley (2010).
18
[37] Cherian, A. and Gould, S. Second-order Temporal Pooling for Action Recognition,
arXiv:1704.06925v1 (2017).
[38] Zhou, X., Leung, D. W. and Chuang, I. L. Methodology for quantum logic gate construction,
Phys. Rev. A, vol. 62, p. 052316 (2000).
[39] Gottesman, D., Chuang, I. L. Quantum Teleportation is a Universal Computational Primitive,
Nature 402, 390-393 (1999).
[40] Amy, M., Maslov, D., Mosca, M. and Roetteler, M. A meet-in-the middle algorithm for fast
synthesis of depth-optimal quantum circuits, IEEE Transactions on Computer-Aided Design
of Integrated Circuits and Systems, vol. 32, no. 6, pp. 818–830, (2013).
[41] Paler, A., Polian, I., Nemoto, K. and Devitt, S. J. Fault-tolerant, high level quantum circuits:
form, compilation and description, Quantum Science and Technology, vol. 2, no. 2, p. 025003,
(2017).
[42] Brandao, F. G. S. L., Broughton, M., Farhi, E., Gutmann, S. and Neven, H. For Fixed Control
Parameters the Quantum Approximate Optimization Algorithm’s Objective Function Value