Quantum Machine Learning Unveiled: A Comprehensive Review

Taghandiki, Kazem

doi:10.48301/jear.2024.446673.1021

Quantum Machine Learning Unveiled: A Comprehensive Review

Document Type : Review Article

Author

Kazem Taghandiki

Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

10.48301/jear.2024.446673.1021

Abstract

Quantum Machine Learning (QML) is a burgeoning field at the convergence of quantum computing and machine learning, with the potential to revolutionize traditional algorithms through principles of quantum mechanics. This article presents a thorough examination of foundational concepts in QML, elucidating qubits, quantum gates, superposition, and entanglement. It explores various QML algorithms, such as quantum neural networks, quantum support vector machines, and quantum clustering, which leverage quantum properties to tackle intricate computational tasks. Additionally, it explores the diverse applications of QML, including quantum chemistry, optimization, cryptography, and big data analysis. The article also discusses applications and various types of quantum machine learning libraries. Despite its promise, QML encounters challenges like scalability, noise, and error correction. Addressing these hurdles and realizing QML's full potential necessitates sustained research efforts and collaborative initiatives, poised to drive transformative progress across industries. This research, spanning four months and drawing insights from over 20 reputable scholarly articles, offers a comprehensive investigation into QML.

Keywords

Machine Learning

Quantum Computing

Superposition

Qubits

Quantum Algorithms

Subjects

Computer Engineering

[1] Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172-185. https://doi.org/10.1080/001 07514.2014.964942

[2] Zhang, Y., & Ni, Q. (2020). Recent advances in quantum machine learning. Quantum Engineering, 2(1), e34. https://doi.org/10.1002/que2.34

[3] Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195-202. https://doi.org/10.1038/natu re23474

[4] Wittek, P. (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science. https://books.google.com/books/about/Quant um_Machine_Learning.html?id=92hzAwAAQBAJ&source=kp_book_description

[5] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv,1-16. https://doi.org/10.48550/arXiv.1411.4028

[6] Liu, J-G., & Wang, L. (2018). Differentiable learning of quantum circuit born machines. Physical Review A, 98(6), 062324. https://doi.org/10.1103/PhysRevA.98.062324

[7] Schuld, M., & Killoran, N. (2019). Quantum Machine Learning in Feature Hilbert Spaces. Physical review letters, 122(4), 040504. https://doi.org/10.1103/PhysRevLett. 122.040504

[8] Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., & Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209-212. https://doi.org/10.1038/s41586-019-0980-2

[9] Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A. (2017). Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models. Physical Review X, 7(4), 041052. https://doi.org/10.1103/PhysRevX.7.041052

[10] Sheng, Y-B., & Zhou, L. (2017). Distributed secure quantum machine learning. Science Bulletin, 62(14), 1025-1029. https://doi.org/10.1016/j.scib.2017.06.007

[11] Martín-Guerrero, J. D., & Lamata, L. (2022). Quantum Machine Learning: A tutorial. Neurocomputing, 470, 457-461. https://doi.org/10.1016/j.neucom.2021.02.102

[12] Schuld, M., & Killoran, N. (2022). Is quantum advantage the right goal for quantum machine learning? Physical Review journal Quantum, 3(3), 030101. https://doi. org/10.1103/PRXQuantum.3.030101

[13] Broughton, M., Verdon, G., McCourt, T., Martinez, A. J., Yoo, J. H., Isakov, S. V., Massey, P., Halavati, R., Niu, M. Y., & Zlokapa, A. (2020). Tensorflow quantum: A software framework for quantum machine learning. arXiv, 1-56. https://doi.org/10.485 50/arXiv.2003.02989

[14] Xia, R., & Kais, S. (2018). Quantum machine learning for electronic structure calculations. Nature Communications, 9(1), 4195. https://doi.org/10.1038/s41467-018-06 598-z

[15] Huggins, W., Patil, P., Mitchell, B., Whaley, K. B., & Stoudenmire, E. M. (2019). Towards quantum machine learning with tensor networks. Quantum Science and technology, 4(2), 024001. https://doi.org/10.1088/2058-9565/aaea94

[16] Cerezo, M., Verdon, G., Huang, H-Y., Cincio, L., & Coles, P. J. (2022). Challenges and opportunities in quantum machine learning. Nature Computational Science, 2(9), 567-576. https://doi.org/10.1038/s43588-022-00311-3

[17] Dunjko, V., & Wittek, P. (2020). A non-review of quantum machine learning: trends and explorations. Quantum Views, 4, 32. https://doi.org/10.22331/qv-2020-0 3-17-32

[18] Lloyd, S., Schuld, M., Ijaz, A., Izaac, J., & Killoran, N. (2020). Quantum embeddings for machine learning. arXiv, 1-11. https://doi.org/10.48550/arXiv.2001.03622

[19] Dunjko, V., Taylor, J. M., & Briegel, H. J. (2016). Quantum-enhanced machine learning. Physical review letters, 117(13), 130501. https://doi.org/10.1103/PhysRevLet t.117.130501

[20] Lamata, L. (2020). Quantum machine learning and quantum biomimetics: A perspective. Machine Learning Science and Technology, 1(3), 033002. https://doi.org/10.10 88/2632-2153/ab9803

[21] Preskill, J. (2023). Quantum computing 40 years later. In T. Hey (Ed.), Feynman Lectures on Computation (2 ed.). Chemical Rubber Company Press. https://doi.org/10.1 201/9781003358817-7

[22] Bova, F., Goldfarb, A., & Melko, R. G. (2021). Commercial applications of quantum computing. European Physical Journal Quantum Technology, 8(1), 2. https://doi .org/10.1140/epjqt/s40507-021-00091-1

[23] Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. https://doi.org/10.1016/j.revip.2019 .100028