nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

Patterns for Hybrid Quantum Algorithms

verfasst von : Manuela Weigold, Johanna Barzen, Frank Leymann, Daniel Vietz

Erschienen in: Service-Oriented Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Quantum computers have the potential to solve certain problems faster than classical computers. However, the computations that can be executed on current quantum devices are still limited. Hybrid algorithms split the computational tasks between classical and quantum computers circumventing some of these limitations. Therefore, they are regarded as promising candidates for useful applications in the near future. But especially for novices in quantum computing, it is hard to identify why a particular splitting strategy is proposed by an algorithm. In this work, we describe the best practices for splitting strategies as patterns to foster a common understanding of hybrid algorithms.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel How to Bake Quantum into Your Pet Petri Nets and Have Your Net Theory Too

Nächstes Kapitel QSOC: Quantum Service-Oriented Computing

https://github.com/UST-QuAntiL/qhana.

https://www.qutac.de/.

[32] proves this by showing how this algorithm relates to the adiabatic algorithm [33], which also inspired the ansatz of the algorithm.

https://www.qutac.de/.

https://quantumalgorithmzoo.org/.

http://planqk.de/.

IBM makes quantum computing available on IBM cloud to accelerate innovation. https://www-03.ibm.com/press/us/en/pressrelease/49661.wss. (2016)

LaRose, R.: Overview and comparison of gate level quantum software platforms. Quantum 3, 130 (2019)CrossRef

Horodecki, R., Horodecki, P., Horodecki, M., Horodecki, K.: Quantum entanglement. Rev. Mod. Phy. 81(2), 865 (2009)MathSciNetCrossRef

Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge and New York (2010)CrossRef

Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proceedings 35th Annual Symposium on Foundations of Computer Science, pp. 124–134, IEEE (1994)

Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2009)MathSciNetCrossRef

Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)CrossRef

Amico, M., Saleem, Z.H., Kumph, M.: Experimental study of shor’s factoring algorithm using the IBM q experience. Phys. Rev. A 100(1), (2019)

Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., Biswas, R.: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quant. Sci. Technol. 3(3), 030502 (2018)CrossRef

10.

Weigold, M., Barzen, J., Leymann, F., Salm, M.: Data encoding patterns for quantum computing. In: Proceedings of the \(27^{{\rm th}}\) Conference on Pattern Languages of Programs. The Hillside Group (2021, to appear)

11.

Weigold, M., Barzen, J., Leymann, F., Salm, M.: Expanding data encoding patterns for quantum algorithms. In: 2021 IEEE \(18^{{\rm th}}\) International Conference on Software Architecture Companion (ICSA-C). pp. 95–101. IEEE, March 2021. https://ieeexplore.ieee.org/document/9425837/

12.

Leymann, F.: Towards a pattern language for quantum algorithms. In: Feld, S., Linnhoff-Popien, C. (eds.) QTOP 2019. LNCS, vol. 11413, pp. 218–230. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14082-3_19CrossRef

13.

Falkenthal, M., et al.: Leveraging pattern application via pattern refinement. In: Proceedings of the International Conference on Pursuit of Pattern Languages for Societal Change (PURPLSOC 2015). epubli, June 2015

14.

Leymann, F., Barzen, J.: The bitter truth about gate-based quantum algorithms in the NISQ era. Quant. Sci. Technol. 5, 1–28 (2020). https://doi.org/10.1088/2058-9565/abae7d

15.

Schuld, M., Killoran, N.: Quantum machine learning in feature hilbert spaces. Phys. Rev. Lett. 122(4), 040504 (2019)CrossRef

16.

Havlíček, V., Córcoles, A.D., Temme, K., Harrow, A.W., Kandala, A., Chow, J.M., Gambetta, J.M.: Supervised learning with quantum-enhanced feature spaces. Nature 567(7747), 209–212 (2019)CrossRef

17.

Bartkiewicz, K., Gneiting, C., Černoch, A., Jiráková, K., Lemr, K., Nori, F.: Experimental kernel-based quantum machine learning in finite feature space. Sci. Rep. 10(1), 12356 (2020)CrossRef

18.

Ghobadi, R., Oberoi, R.S., Zahedinejhad, E.: The power of one qubit in machine learning. arXiv preprint arXiv:1905.01390 (2009)

19.

Barzen, J.: From digital humanities to quantum humanities: Potentials and applications. arXiv preprint arXiv:2103.11825 (2021)

20.

Cerezo, M., et al.: Variational quantum algorithms. arXiv preprint arXiv:2012.09265v1(2020)

21.

Mitarai, K., Negoro, M., Kitagawa, M., Fujii, K.:Quantum circuit learning. Phy. Rev. A 98(3) (2018)

22.

Farhi, E., Neven, H.: Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002(2018)

23.

Anschuetz, E.N., Olson, J.R., Aspuru-Guzik, A., Cao, Y.: Variational quantum factoring. arXiv preprint arXiv:1808.08927 (2008)

24.

Taube, A.G., Bartlett, R.J.: New perspectives on unitary coupled-cluster theory. Int. J. Quant. Chem. 106(15), 3393–3401 (2006). https://onlinelibrary.wiley.com/doi/abs/10.1002/qua.21198

25.

Peruzzo, A., et al.: A variational eigenvalue solver on a photonic quantum processor. Nat. Commun. 5(1), 4213 (2014)CrossRef

26.

Higgott, O., Wang, D., Brierley, S.: Variational quantum computation of excited states. Quantum 3, 156 (2019. https://doi.org/10.22331/q-2019-07-01-156

27.

Cao, Y., et al.: Quantum chemistry in the age of quantum computing. Chem. Rev. 119(19), 10 856–10 915 (2019)

28.

Industry quantum computing applications - qutac application group. https://www.qutac.de/wp-content/uploads/2021/07/QUTAC_Paper.pdf (2021)

29.

Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E.G., Venturelli, D., Biswas, R.: From the quantum approximate optimization algorithm to a quantum alternating operator ansatz. Algorithms 12(2), (2019). https://www.mdpi.com/1999-4893/12/2/34

30.

Wang, Z., Rubin, N.C., Dominy, J.M., Rieffel, E.G.: \(xy\) mixers: analytical and numerical results for the quantum alternating operator ansatz. Phys. Rev. A 101, 012320 (2020). https://link.aps.org/doi/10.1103/PhysRevA.101.012320

31.

Fingerhuth, M., Babej, T., Ing, C.: A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding. arXiv preprint arXiv:1810.13411(2018)

32.

Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm. http://arxiv.org/pdf/1411.4028v1

33.

Farhi, E., Goldstone, J., Gutmann, E., Sipser, M.: Quantum computation by adiabatic evolution. arXiv preprint arXiv:quant-ph/0001106 (2000)

34.

Farhi, E., Goldstone, J., Gutmann, C.: A quantum approximate optimization algorithm applied to a bounded occurrence constraint problem. arXiv preprint arXiv:1412.6062(2015)

35.

Barak, B., et al.: Beating the random assignment on constraint satisfaction problems of bounded degree. CoRR, vol. abs/1505.03424 (2015). http://arxiv.org/abs/1505.03424

36.

Tate, R., Farhadi, M., Herold, C., Mohler, E., Gupta, S.: Bridging classical and quantum with sdp initialized warm-starts for qaoa. arXiv preprint arXiv:2010.14021(2020)

37.

Egger, D.J., Marecek, J., Woerner, S.: Warm-starting quantum optimization. Xiv preprint arXiv:quant-ph/0001106(2020)

38.

Barkoutsos, P.K., et al.: Quantum algorithms for electronic structure calculations: particle-hole hamiltonian and optimized wave-function expansions. Phys. Rev. A 98(2) (2018)

39.

Alexander, C., Ishikawa, S., Silverstein, M.: A Pattern Language: Towns, Buildings Construction. Oxford University Press, Oxford (1977)

40.

Ramezani, S.B., Sommers, A., Manchukonda, H.K., Rahimi, S., Amirlatifi, A.: Machine learning algorithms in quantum computing: a survey. In: International Joint Conference on Neural Networks (IJCNN), vol. 2020, pp. 1–8 (2020)

41.

Grover, L.M.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing (STOC 1996) (1996)

42.

Schuld, M., Petruccione, F.: Supervised Learning with Quantum Computers, Springer International Publishing, Quantum Science and Technology (2018). https://doi.org/10.1007/978-3-319-96424-9

43.

National Academies of Sciences: Engineering and Medicine, Quantum Computing: Progress and Prospects. The National Academies Press, Washington, DC (2019)

44.

Moll, N.: Quantum optimization using variational algorithms on near-term quantum devices. Quant. Sci. Technol. 3(3), 030503 (2018). https://iopscience.iop.org/article/10.1088/2058-9565/aab822/meta

45.

McClean, J.R., Romero, J., Babbush, R., Aspuru-Guzik, A.: The theory of variational hybrid quantum-classical algorithms. New J. Phys. 18(2), 023023 (2016). https://doi.org/10.1088/1367-2630/18/2/023023

46.

Sim, S., Johnson, P.D., Aspuru-Guzik, A.: Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv. Quant. Technol. 2(12), 1900070 (2019)CrossRef

47.

Lee, Y., Joo, J., Lee, S.: Hybrid quantum linear equation algorithm and its experimental test on IBN quantum experience. Sci. Rep. 9(1), 4778 (2019)CrossRef

48.

Lloyd, S.: Quantum approximate optimization is computationally universal. arXiv preprint arXiv:1812.11075 (2018)

49.

Schuld, M.: Quantum machine learning models are kernel methods. arXiv preprint arXiv:2101.11020 (2021)

50.

Weder, B., Barzen, J., Leymann, F., Salm, S., Vietz, D.: The quantum software lifecycle. In: Proceedings of the 1st ACM SIGSOFT International Workshop on Architectures and Paradigms for Engineering Quantum Software (APEQS 2020). ACM, Workshop, pp. 2–9, November 2020. https://doi.org/10.1145/3412451.3428497

51.

Fehling, C., Barzen, J., Breitenbücher, U., Leymann, F.: A Process for pattern identification, authoring, and application. In: Proceedings of the \(19^{{\rm th}}\) European Conference on Pattern Languages of Programs (EuroPLoP 2014), ACM, January 2014

Titel: Patterns for Hybrid Quantum Algorithms
verfasst von: Manuela Weigold
Johanna Barzen
Frank Leymann
Daniel Vietz
Verlag: Springer International Publishing
Buch: Service-Oriented Computing
Print ISBN: 978-3-030-87567-1

Electronic ISBN: 978-3-030-87568-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87568-8_2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner