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A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning

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Innovations in Smart Cities Applications Volume 7 (SCA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 938))

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Abstract

One of the greatest developments in computer science is undoubtedly quantum computing. It has demonstrated to give various benefits over the classical algorithms, particularly in the significant reduction of processing time, due to the parallelism and entanglement properties. One of the most crucial quantum computing algorithms is quantum phase estimation (QPE). It is called the eigenvalue finding module for unitary operators. It has helped to solve the order finding and the factoring problem, and to calculate the eigenvalues of unitary matrices and quantum sampling methods. In this paper, we study recent improved versions for the QPE procedure, their advantages and experimentation. We also propose a new approach for QPE based algorithms for machine learning (ML). These algorithms are the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, the quantum singular value thresholding (QSVT) algorithm for matrix completion in recommender systems, and the quantum principal components analysis (QPCA) for data visualization.

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References

  1. Bera, R.K.: The Amazing World of Quantum Computing. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-2471-4

    Book  Google Scholar 

  2. de Souza, D.D., Vidiella-Barranco, A.: Quantum phase estimation with squeezed quasi-Bell states. Optik 244, 167532 (2021)

    Article  Google Scholar 

  3. Duan, B., Yuan, J., Liu, Y., Li, D.: Efficient quantum circuit for singular value thresholding. Phys. Rev. A 98(1), 012308 (2018)

    Article  Google Scholar 

  4. Duan, B., Yuan, J., Yu, C.H., Huang, J., Hsieh, C.Y.: A survey on HHL algorithm: From theory to application in quantum machine learning. Phys. Lett. A 384, 126595 (2020)

    Article  MathSciNet  Google Scholar 

  5. Harrow, A.W., Hassidim, A., Lloyd, S.: Quantum algorithm for solving linear systems of equations. Phys. Rev. Lett. 103(15), 150502 (2008)

    Article  Google Scholar 

  6. Lloyd, S., Mohseni, M., Rebentrost, P.: Quantum principal component analysis. Nat. Phys. 10, 631–633 (2014)

    Article  Google Scholar 

  7. Mishra, N., et al.: Quantum machine learning: a review and current status. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds.) Data Management, Analytics and Innovation. AISC, vol. 1175, pp. 101–145. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5619-7_8

    Chapter  Google Scholar 

  8. Mohammadbagherpoor, H., Oh, Y., Dreher, P., Singh, A., Yu, X., Rindos, A.: An improved implementation approach for quantum phase estimation on quantum computers. In: IEEE International Conference on Rebooting Computing (ICRC), pp. 1–9 (2019)

    Google Scholar 

  9. Moore, A., Wang, Y., Hu, Z., Kais, S., Weiner, A.: Statistical approach to quantum phase estimation. New J. Phys. 23, 113027 (2021)

    Article  Google Scholar 

  10. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, New York (2000)

    Google Scholar 

  11. Ouedrhiri, O., Banouar, O., Raghay, S., Elhadaj, S.: Quantum phase estimation based algorithms for machine learning. In: 2nd International Informatics and Software Engineering Conference (IISEC), pp. 1–6 (2021)

    Google Scholar 

  12. Pezzè, L., Smerzi, A.: Quantum phase estimation algorithm with gaussian spin states. PRX Quant. 2, 040301 (2021)

    Article  Google Scholar 

  13. Quantum phase estimation in Qiskit. https://www.qiskit.org/textbook/ch-algorithms/quantum-phase-estimation.html

  14. Smith, J., Barnes, C., Shukur, D.: An iterative quantum-phase-estimation protocol for near-term quantum hardware. arXiv arXiv:2206.06392v1 (2022)

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Authors and Affiliations

  1. Department of Computer Science and Mathematics, University of Quebec at Trois-Riviéres, Trois-Riviéres, Canada

    Oumayma Ouedrhiri, Salah El Hadaj & Said Raghay

  2. Faculty of Sciences and Techniques, Laboratory of Computer Science Engineering and Systems, Marrakesh, Morocco

    Oumayma Banouar, Salah El Hadaj & Said Raghay

Authors
  1. Oumayma Ouedrhiri
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  2. Oumayma Banouar
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  3. Salah El Hadaj
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  4. Said Raghay
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Corresponding author

Correspondence to Oumayma Ouedrhiri .

Editor information

Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Fac Sciences et Techniques de Tanger, Université Abdelmalek Essaâdi, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. École Spéciale des Travaux Publics, Paris, France

    Rani El Meouche

  4. Computer Engineering Department, Karabük University, Karabük, Türkiye

    İsmail Rakıp Karaș

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Ouedrhiri, O., Banouar, O., El Hadaj, S., Raghay, S. (2024). A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-54376-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54375-3

  • Online ISBN: 978-3-031-54376-0

  • eBook Packages: EngineeringEngineering (R0)

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