Quantum Support Vector Machine for Classification Task: A Review
DOI:
https://doi.org/10.62411/jimat.v1i2.10965Abstract
Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.References
A. Zulehner, R. Wille, Simulation and design of quantum circuits, in I. Ulidowski, I. Lanese, U.P. Schultz, C. Ferreira (Eds.), Reversible Computation: Extending Horizons of Computing: Selected Results of the COST Action IC1405, Springer International Publishing, Cham, 60–82 (2020), http://dx.doi.org/10.1007/978-3-030-47361-7_3.
M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Parameterized quantum circuits as machine learning models, Quantum Sci. Technol., 4(4), (2019), http://dx.doi.org/10.1088/2058-9565/ab4eb5, arXiv:1906.07682.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd. Quantum Machine Learning. Nature, 549(7671), 195-202 (2017).
S. Budi, M. Akrom, G.A. Trisnapradika, T. Sutojo, W.A.E. Prabowo, Optimization of Polynomial Functions on the NuSVR Algorithm Based on Machine Learning: Case Studies on Regression Datasets, Scientific Journal of Informatics, 10(2), (2023), https://doi.org/10.15294/sji.v10i2.43929.
M. Benedetti, J. Realpe-Gómez, and R. Biswas, Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models. Physical Review A, 99(4), 042306 (2019).
S. Lloyd, M. Mohseni, and P. Rebentrost, Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411.
M. Schuld, I. Sinayskiy, and F. Petruccione, The quest for a quantum support vector machine. Quantum Information Processing, 13(11), 2567-2586 (2014).
V. Havlíček, A.D. Córcoles, K. Temme, A.W. Harrow, A. Kandala, J.M. Chow, and J.M. Gambetta. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209-212 (2019).
M. Akrom, S. Rustad, H.K. Dipojono, A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors, Phys Scr, 99(3), 036006 (2024), https://doi.org/10.1088/1402-4896/ad28a9.
M. Akrom, Investigation of natural extracts as green corrosion inhibitors in steel using density functional theory, Jurnal Teori dan Aplikasi Fisika, 10(1), 89-102 (2022), https://doi.org/10.23960%2Fjtaf.v10i1.2927.
Nielsen, M. A., & Chuang, I. L. (2010). "Quantum Computation and Quantum Information: 10th Anniversary Edition." Cambridge University Press.
Preskill, J. (1998). "Quantum Computing: Prologue." arXiv preprint quant-ph/9712048.
Mermin, N. D. (2007). "Quantum Computer Science: An Introduction." Cambridge University Press.
Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O'Brien, J. L. (2010). "Quantum computers." Nature, 464(7285), 45-53.
Aaronson, S., & Arkhipov, A. (2011). "The Computational Complexity of Linear Optics." Proceedings of the ACM Symposium on Theory of Computing (STOC).
Wang, D., Guo, F., & Guo, Y. (2016). "A novel solution to multi-class classification problem using support vector machine." Journal of Ambient Intelligence and Humanized Computing, 7(4), 563-571.
Chang, H., Liu, Y., & Bai, Y. (2017). "A new multi-category support vector machine algorithm." Soft Computing, 21(6), 1377-1389.
M.-Z. Ai, Y. Ding, Y. Ban, J.D. Martín-Guerrero, J. Casanova, J.-M. Cui, Y.-F. Huang, X. Chen, C.-F. Li, G.-C. Guo, Experimentally realizing efficient quantum control with reinforcement learning, 2021, arXiv:2101.09020.
M. Akrom, S. Rustad, H.K. Dipojono. Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds. Materials Today Communications, 39, 108758 (2024), https://doi.org/10.1016/j.mtcomm.2024.108758.
D. Alaminos, M.B. Salas, M.A. Fernández-Gámez, Quantum computing and deep learning methods for GDP growth forecasting, Comput. Econ. (2021) http://dx.doi.org/10.1007/s10614-021-10110-z.
F.J. García-Peñalvo, Desarrollo de estados de la cuestión robustos: Revisiones sistemáticas de literatura, Educ. Knowl. Soc. (EKS) 23 (2022) http://dx.doi.org/10.14201/eks.28600, URL http://repositorio.grial.eu/handle/grial/2568.
W. O’Quinn, S. Mao, Quantum machine learning: Recent advances and outlook, IEEE Wirel. Commun. 27 (3) (2020) 126–131, http://dx.doi.org/10.1109/MWC.001.1900341.
D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement, Int. J. Surg. 8 (5) (2010) 336–341, http://dx.doi.org/10.1016/j.ijsu.2010.02.007.
M. Petticrew, H. Roberts, Systematic Reviews in the Social Sciences: A Practical Guide, vol. 11, 2006, http://dx.doi.org/10.1002/9780470754887.
Y. Huang, H. Lei, X. Li, Q. Zhu, W. Ren, X. Liu, Quantum generative model with variable-depth circuit, Comput. Mater. Contin. 65 (1) (2020) 445–458, http://dx.doi.org/10.32604/cmc.2020.010390.
M. Srikumar, C.D. Hill, L.C.L. Hollenberg, Clustering and enhanced classification using a hybrid quantum autoencoder, Quantum Sci. Technol. 7 (1) (2021) 015020, http://dx.doi.org/10.1088/2058-9565/ac3c53.
D. Konar, S. Bhattacharyya, B.K. Panigrahi, E.C. Behrman, Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation, IEEE Trans. Neural Netw. Learn. Syst. (2021) 1–15, http://dx.doi.org/10. 1109/tnnls.2021.3077188, arXiv:2009.06767.
M. Lukac, K. Abdiyeva, M. Kameyama, CNOT-measure quantum neural networks, in: Proceedings of the International Symposium on Multiple-Valued Logic, Vol. 2018-May, IEEE Computer Society, 2018, pp. 186–191, http://dx.doi.org/10.1109/ISMVL.2018.00040.
Y. Li, R.G. Zhou, R. Xu, J. Luo, W. Hu, A quantum deep convolutional neural network for image recognition, Quantum Sci. Technol. 5 (4) (2020) http://dx.doi.org/10.1088/2058-9565/ab9f93.
F. Tacchino, P. Barkoutsos, C. Macchiavello, I. Tavernelli, D. Gerace, D. Bajoni, Quantum implementation of an artificial feed-forward neural network, Quantum Sci. Technol. 5 (4) (2020) http://dx.doi.org/10.1088/2058-9565/abb8e4, arXiv:1912.12486.
H. Wang, J. Zhao, B. Wang, L. Tong, A quantum approximate optimization algorithm with metalearning for maxcut problem and its simulation via tensorflow quantum, Math. Probl. Eng. 2021 (2021) http://dx.doi.org/10.1155/2021/6655455.
A. Ceschini, A. Rosato, M. Panella, Design of an LSTM cell on a quantum hardware, IEEE Trans. Circuits Syst. II 69 (3) (2022) 1822–1826, http://dx.doi.org/10.1109/TCSII.2021.3126204.
Y.-Y. Hong, C.J.E. Arce, T.-W. Huang, A robust hybrid classical and quantum model for short-term wind speed forecasting, IEEE Access 11 (2023) 90811–90824, http://dx.doi.org/10.1109/ACCESS.2023.3308053.
S.Y.-C. Chen, Asynchronous training of quantum reinforcement learning, Procedia Comput. Sci. 222 (2023) 321–330, http://dx.doi.org/10.1016/j.procs.2023.08.171, International Neural Network Society Workshop on Deep Learning Innovations and Applications (INNS DLIA 2023).
J. Preskill, Quantum Computing in the NISQ era and beyond, Quantum 2 (2018) 79, http://dx.doi.org/10.22331/q-2018-08-06-79.
M. Akrom, S. Rustad, H.K. Dipojono. Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds. Materials Today Quantum, 2, 100007 (2024), https://doi.org/10.1016/j.mtquan.2024.100007.
Y. Du, Y. Qian, X. Wu, D. Tao, A distributed learning scheme for variational quantum algorithms, IEEE Trans. Quantum Eng. 3 (2022) 1–16, http://dx.doi.org/10.1109/TQE.2022.3175267.
R. Sharma, B. Kaushik, N.K. Gondhi, M. Tahir, M.K.I. Rahmani, Quantum particle swarm optimization based convolutional neural network for handwritten script recognition, Comput. Mater. Contin. 71 (3) (2022) 5855–5873, http://dx.doi.org/10.32604/cmc.2022.024232.
M. Akrom, T. Sutojo, A. Pertiwi, S. Rustad, H.K. Dipojono, Investigation of Best QSPR-Based Machine Learning Model to Predict Corrosion Inhibition Performance of Pyridine-Quinoline Compounds, J Phys Conf Ser, 2673(1), 012014 (2023), https://doi.org/10.1088/1742-6596/2673/1/012014.
M.L. Wall, M.R. Abernathy, G. Quiroz, Generative machine learning with tensor networks: Benchmarks on near-term quantum computers, Phys. Rev. Res. 3 (2) (2021) http://dx.doi.org/10.1103/physrevresearch.3.023010.
Downloads
Published
Issue
Section
License
Authors who publish their articles in this journal agree to the following conditions:
- Copyright remains with the author and gives the JIMAT journal the right as first priority to publish the article under a Creative Commons Attribution License which allows articles to be shared with acknowledgment of the author of the article and this journal as the place of publication.
- Authors can distribute their published articles non-exclusively (for example: in university repositories or in books) with notification or acknowledgment of publication in JIMAT.
- Authors are permitted to list their work online (for example: on a personal website or in a university repository) before and after the submission process (see The Effect of Open Access).
