Synergizing Quantum Computing and Machine Learning: A Pathway Toward Quantum-Enhanced Intelligence
DOI:
https://doi.org/10.62411/jimat.v2i1.12947Keywords:
Quantum Artificial Intelligence, Quantum Machine Learning, Quantum Computing, Hybrid AI SystemAbstract
The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologiesReferences
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.
M. Akrom, S. Rustad, T. Sutojo, D.R.I.M. Setiadi, H.K. Dipojono, R. Maezono, M. Solomon, Quantum machine learning for corrosion resistance in stainless steel, Materials Today Quantum, 3, 100013 (2024), https://doi.org/10.1016/j.mtquan.2024.100013.
M. Akrom, S. Rustad, H.K. Dipojono, R. Maezono, H. Kasai, Quantum machine learning for ABO3 perovskite structure prediction, Comput. Mater. Sci. 250 (2025) 113694, https://doi.org/10.1016/j.commatsci.2025.113694.
M. Akrom, Quantum support vector machine for classification task: a review, J. Multiscale Mater. Inform. 1 (2) (2024) 1–8, https://doi.org/10.62411/jimat. v1i2.10965.
M. Akrom, S. Rustad, H.K. Dipojono, Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds, Mater. Today Quant. 2 (2024) 100007, https://doi. org/10.1016/j.mtquan.2024.100007.
M. Akrom, S. Rustad, H.K. Dipojono, Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds, Mater. Today Commun. (2024) 108758, https://doi.org/10.1016/J /J/J. MTCOMM.2024.108758.
M. Akrom, S. Rustad, H.K. Dipojono, R. Maezono, A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds, Artif. Intell. Chem. 2 (2) (2024) 100073, https://doi.org/ 10.1016/J.AICHEM.2024.100073.
M.R. Rosyid, L. Mawaddah, A.P. Santosa, M. Akrom, S. Rustad, H.K. Dipojono, Implementation of quantum machine learning in predicting corrosion inhibition efficiency of expired drugs, Mater. Today Commun. 40 (2024) 109830, https://doi.org/10.1016/J.MTCOMM.2024.109830.
M. Akrom, M.R. Rosyid, L. Mawaddah, A.P. Santosa, Variational Quantum Circuit-Based Quantum Machine Learning Approach for Predicting Corrosion Inhibition Efficiency of Expired Pharmaceuticals, Jurnal Online Informatika, 10(1), 1-11, 2025, https://doi.org/10.15575/join.v10i1.1333.
M. Akrom, S. Rustad, T. Sutojo, D.R.I.M Setiadi, P.N. Andono, G.F. Shidik, H.K. Dipojono, R. Maezono, A novel quantum-enhanced model cascading approach based on support vector machine in blood-brain barrier permeability prediction, Materials Today Communications, 40, 112341 (2025), https://doi.org/10.1016/j.mtcomm.2025.112341.
M. Akrom, W. Herowati, D.R.I.M. Setiadi, A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification, Journal of Computing Theories and Applications, 2(3), 355-367 (2025), https://doi.org/10.62411/jcta.11779.
M. Akrom, S. Rustad, T. Sutojo, W.A.E. Prabowo, H.K. Dipojono, R. Maezono, H. Kasai, Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds, Results in Surfaces and Interfaces, 18, 100462 (2025), https://doi.org/10.1016/j.rsurfi.2025.100462.
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.
M. Akrom, S. Rustad, T. Sutojo, D.R.I.M. Setiadi, H.K. Dipojono, R. Maezono, M. Solomon, Quantum machine learning for corrosion resistance in stainless steel, Materials Today Quantum, 3, 100013 (2024), https://doi.org/10.1016/j.mtquan.2024.100013.
M. Akrom, S. Rustad, H.K. Dipojono, R. Maezono, H. Kasai, Quantum machine learning for ABO3 perovskite structure prediction, Comput. Mater. Sci. 250 (2025) 113694, https://doi.org/10.1016/j.commatsci.2025.113694.
M. Akrom, Quantum support vector machine for classification task: a review, J. Multiscale Mater. Inform. 1 (2) (2024) 1–8, https://doi.org/10.62411/jimat. v1i2.10965.
M. Akrom, S. Rustad, H.K. Dipojono, Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds, Mater. Today Quant. 2 (2024) 100007, https://doi. org/10.1016/j.mtquan.2024.100007.
M. Akrom, S. Rustad, H.K. Dipojono, Development of quantum machine learning to evaluate the corrosion inhibition capability of pyrimidine compounds, Mater. Today Commun. (2024) 108758, https://doi.org/10.1016/J /J/J. MTCOMM.2024.108758.
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