IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3318-d1504656.html
   My bibliography  Save this article

Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions

Author

Listed:
  • Deepak Ranga

    (Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India)

  • Aryan Rana

    (Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India)

  • Sunil Prajapat

    (Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India)

  • Pankaj Kumar

    (Srinivasa Ramanujan Department of Mathematics, Central University of Himachal Pradesh, Dharamsala 176206, India)

  • Kranti Kumar

    (School of Liberal Studies, Dr. B. R. Ambedkar University, Delhi 110006, India)

  • Athanasios V. Vasilakos

    (Department of Networks and Communications, College of Computer Science and Information Technology, IAU, P.O. Box 1982, Dammam 31441, Saudi Arabia
    Center for AI Research (CAIR), University of Agder (UiA), 4879 Grimstad, Norway)

Abstract

Quantum computing and machine learning (ML) have received significant developments which have set the stage for the next frontier of creative work and usefulness. This paper aims at reviewing various data-encoding techniques in Quantum Machine Learning (QML) while highlighting their significance in transforming classical data into quantum systems. We analyze basis, amplitude, angle, and other high-level encodings in depth to demonstrate how various strategies affect encoding improvements in quantum algorithms. However, they identify major problems with encoding in the framework of QML, including scalability, computational burden, and noise. Future directions for research outline these challenges, aiming to enhance the excellence of encoding techniques in the constantly evolving quantum technology setting. This review shall enable the researcher to gain an enhanced understanding of data encoding in QML, and it also suggests solutions to the current limitations in this area.

Suggested Citation

  • Deepak Ranga & Aryan Rana & Sunil Prajapat & Pankaj Kumar & Kranti Kumar & Athanasios V. Vasilakos, 2024. "Quantum Machine Learning: Exploring the Role of Data Encoding Techniques, Challenges, and Future Directions," Mathematics, MDPI, vol. 12(21), pages 1-32, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3318-:d:1504656
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3318/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/21/3318/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hang Nguyen & Roger Calantone & Ranjani Krishnan, 2020. "Influence of Social Media Emotional Word of Mouth on Institutional Investors’ Decisions and Firm Value," Management Science, INFORMS, vol. 66(2), pages 887-910, February.
    2. Vojtěch Havlíček & Antonio D. Córcoles & Kristan Temme & Aram W. Harrow & Abhinav Kandala & Jerry M. Chow & Jay M. Gambetta, 2019. "Supervised learning with quantum-enhanced feature spaces," Nature, Nature, vol. 567(7747), pages 209-212, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schmidt, Christoph G. & Wuttke, David A. & Heese, H. Sebastian & Wagner, Stephan M., 2023. "Antecedents of public reactions to supply chain glitches," International Journal of Production Economics, Elsevier, vol. 259(C).
    2. Bhattacharya, Abhi & Sardashti, Hanieh, 2022. "The differential effect of new product preannouncements in driving institutional and individual investor ownership," Journal of Business Research, Elsevier, vol. 149(C), pages 811-823.
    3. Zhang, Xuetong & Zhang, Weiguo, 2023. "Information asymmetry, sentiment interactions, and asset price," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    4. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    5. Sun, Xiaochi & Foscht, Thomas & Eisingerich, Andreas B., 2021. "Does educating customers create positive word of mouth?," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    6. Warkulat, Sonja & Pelster, Matthias, 2024. "Social media attention and retail investor behavior: Evidence from r/wallstreetbets," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    7. Chin Sok Fun & Nor Shaipah Abdul Wahab & Tye Wei Ling & Premagowrie Sivanandan & Nurliyana Haji Khalid, 2023. "Tenure of independent directors and analysts following: Moderating role of institutional ownership," Cogent Business & Management, Taylor & Francis Journals, vol. 10(3), pages 2256500-225, December.
    8. Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023. "A novel approach for quantum financial simulation and quantum state preparation," Papers 2308.01844, arXiv.org, revised Apr 2024.
    9. Andranik Tumasjan, 2024. "The many faces of social media in business and economics research: Taking stock of the literature and looking into the future," Journal of Economic Surveys, Wiley Blackwell, vol. 38(2), pages 389-426, April.
    10. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
    11. Wei-Ming Li & Shi-Ju Ran, 2022. "Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    12. Haque, Md Ziaul & Qian, Aimin & Hoque, Md Rakibul & Lucky, Suraiea Akter, 2022. "A unified framework for exploring the determinants of online social networks (OSNs) on institutional investors’ capital market investment decision," Technology in Society, Elsevier, vol. 70(C).
    13. Wang, Qiping & Yiu Keung Lau, Raymond & Xie, Haoran & Liu, Hongyan & Guo, Xunhua, 2024. "Social Executives’ emotions and firm value: An empirical study enhanced by cognitive analytics," Journal of Business Research, Elsevier, vol. 175(C).
    14. Anindita Chakravarty, 2023. "Review of Marketing Relevant Real Activity Manipulation," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 10(1), pages 1-16, December.
    15. Bao, Xin & Sun, Baiqing & Han, Meini & Lin, Han & Lau, Raymond Y.K., 2023. "Quantifying the impact of CEO social media celebrity status on firm value: Novel measures from digital gatekeeping theory," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    16. Zitong Li & Tailong Xiao & Xiaoyang Deng & Guihua Zeng & Weimin Li, 2024. "Optimizing Variational Quantum Neural Networks Based on Collective Intelligence," Mathematics, MDPI, vol. 12(11), pages 1-14, May.
    17. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    18. Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020. "Quantum Computing for Finance: State of the Art and Future Prospects," Papers 2006.14510, arXiv.org, revised Jan 2021.
    19. Shunyao (Cynthia) Jin, 2023. "More than words: Can tone of consumer product reviews help predict firms’ fundamentals?," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 50(9-10), pages 1910-1942, October.
    20. Vanitha Swaminathan & Sayan Gupta & Kevin Lane Keller & Donald Lehmann, 2022. "Brand actions and financial consequences: a review of key findings and directions for future research," Journal of the Academy of Marketing Science, Springer, vol. 50(4), pages 639-664, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3318-:d:1504656. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.