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Quantum enhanced machine learning: An overview

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dc.contributor.author Zahorodko, Pavlo V.
dc.contributor.author Modlo, Yevhenii O.
dc.contributor.author Kalinichenko, Olga O.
dc.contributor.author Selivanova, Tetiana V.
dc.contributor.author Семеріков, Сергій Олексійович
dc.contributor.author Загородько, Павло Володимирович
dc.contributor.author Модло, Євгеній Олександрович
dc.contributor.author Калініченко, Ольга Олександрівна
dc.contributor.author Селіванова, Тетяна Валеріївна
dc.date.accessioned 2021-06-21T14:36:41Z
dc.date.available 2021-06-21T14:36:41Z
dc.date.issued 2021-03-23
dc.identifier.citation Zahorodko P. V. Quantum enhanced machine learning: An overview [Electronic resource] / Pavlo V. Zahorodko, Yevhenii O. Modlo, Olga O. Kalinichenko, Tetiana V. Selivanova, Serhiy O. Semerikov // Proceedings of the 3rd Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2020), Kryvyi Rih, Ukraine, November 27, 2020 / Edited by : Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk // CEUR Workshop Proceedings. – 2021. – Vol. 2832. – P. 94-103. – Access mode : http://ceur-ws.org/Vol-2832/paper13.pdf uk
dc.identifier.issn 1613-0073
dc.identifier.uri http://ceur-ws.org/Vol-2832/paper13.pdf
dc.identifier.uri http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4357
dc.identifier.uri https://doi.org/10.31812/123456789/4357
dc.description [1] Gartner, Quantum Computing Gartner Glossary, 2021. URL: https://www.gartner.com/ en/information-technology/glossary/quantum-computing. [2] K. Panetta, The CIO’s Guide to Quantum Computing, 2019. URL: https://www.gartner. com/smarterwithgartner/the-cios-guide-to-quantum-computing/. [3] J. Clark, S. Stepney, Quantum software engineering, in: Workshop on Grand Challenges for Computing Research, e-Science Institute, Edinburgh, 2002. URL: http://web.archive. org/web/20200721161705/http://www.ukcrc.org.uk/press/news/call/a5.cfm. [4] C.-H. Chenf, L.-Y. Wei, New entropy clustering analysis method based on adaptive learning, in: P. P. Wang (Ed.), Information Sciences 2007: Proceedings of the 10th Joint Conference, Salt Lake City, Utah, USA, 18–24 July 2007, 2007, pp. 1196–1202. doi:10.1142/9789812709677_0169. [5] Q-SE2020, First International Workshop on Quantum Software Engineering (Q-SE 2020) co-located with ICSE 2020, 2021. URL: https://q-se.github.io/qse2020/. [6] M. Piattini, G. Peterssen, R. Perez-Castillo, J. L. Hevia, M. A. Serrano, G. Hernández, I. G. R. de Guzmán, C. A. Paradela, M. Polo, E. Murina, L. Jiménez, J. C. Marqueño, R. Gallego, J. Tura, F. Phillipson, J. M. Murillo, A. Niño, M. Rodríguez, The Talavera Manifesto for Quantum Software Engineering and Programming, CEUR Workshop Proceedings 2561 (2020) 1–5. [7] M. Rahaman, M. M. Islam, A Review on Progress and Problems of Quantum Computing as aService (QCaas) in the Perspective of Cloud Computing, Global Journal of Computer Science and Technology: B Cloud and Distributed 15 (2015) 15 – 18. URL: https://globaljournals.org/GJCST_Volume15/3-Cloud-Data-Storage.pdf. [8] D-Wave Systems Inc, D-Wave Ocean Software Documentation, 2021. URL: https://ocean. dwavesys.com/. [9] D. Steiger, T. Häner, ProjectQ – Open Source Software for Quantum Computing, 2017. URL: https://projectq.ch/. [10] Qiskit, Qiskit, 2021. URL: https://qiskit.org/. [11] Cambridge Quantum Computing, Technology, 2020. URL: https://cambridgequantum. com/technology/. [12] Microsoft, Language-Integrated Quantum Operations: LIQUi|>, 2016. URL: https://www. microsoft.com/en-us/research/project/language-integrated-quantum-operations-liqui/. [13] Microsoft, Microsoft Quantum Documentation and Q# API Reference - Microsoft Quantum, 2021. URL: https://docs.microsoft.com/en-us/quantum/. [14] Rigetti Computing, Rigetti QCS, 2020. URL: https://qcs.rigetti.com/sdk-downloads. [15] Google, Quantum Computing Playground, 2016. URL: http://www.quantumplayground. net. [16] S. Arunachalam, R. de Wolf, A Survey of Quantum Learning Theory, 2017. arXiv:1701.06806. [17] F. Phillipson, Quantum Machine Learning: Benefits and Practical Examples, CEUR Workshop Proceedings 2561 (2020) 51–56. [18] P. Wittek, Quantum Machine Learning: What Quantum Computing Means to Data Mining, Elsevier Insights, Academic Press, San Diego, 2016. [19] V. Dunjko, P. Wittek, A non-review of Quantum Machine Learning: trends and explorations, Quantum Views 4 (2020) 17. doi:10.22331/qv-2020-03-17-32. [20] M. Pistoia, J. Gambetta, Qiskit Aqua – A Library of Quantum Algorithms and Applications, 2018. URL: https://medium.com/qiskit/ qiskit-aqua-a-library-of-quantum-algorithms-and-applications-33ecf3b36008.
dc.description.abstract Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer. uk
dc.language.iso en uk
dc.publisher CEUR Workshop Proceedings uk
dc.subject machine learning uk
dc.subject quantum computing uk
dc.subject quantum software engineering uk
dc.title Quantum enhanced machine learning: An overview uk
dc.type Article uk


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