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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
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    References listed on IDEAS

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    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.
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