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

Deepak Ranga, Aryan Rana, Sunil Prajapat, Pankaj Kumar (), Kranti Kumar and Athanasios V. Vasilakos ()
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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

Mathematics, 2024, vol. 12, issue 21, 1-32

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.

Keywords: quantum computing; machine learning (ML); data encoding; Quantum Machine Learning (QML) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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