Variational Quantum Circuits Design Principles, Applications, and Challenges Toward Practical: A Review
DOI:
https://doi.org/10.62411/jimat.v2i2.14935Abstract
Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applicationsReferences
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.
M. Akrom, DFT Investigation of Syzygium Aromaticum and Nicotiana Tabacum Extracts as Corrosion Inhibitor, Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi, 8(1), 42-48 (2022). http://dx.doi.org/10.30738/st.vol8.no1.a11775.
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.
M. Akrom, Quantum machine learning for corrosion resistance in stainless steel, Materials Today Quantum, 3, 100013 (2024), https://doi.org/10.1016/j.mtquan.2024.100013.
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. Akrom, A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds, Artificial Intelligence Chemistry, 2(2), 100073 (2024), https://doi.org/10.1016/j.aichem.2024.100073.
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).
