Layerwise Quantum Training: A Progressive Strategy for Mitigating Barren Plateaus in Quantum Neural Networks
DOI:
https://doi.org/10.62411/jimat.v2i1.12948Keywords:
Barren Plateau, Quantum Machine Learning, Quantum Neural Network, Parameterized Quantum CircuitAbstract
Barren plateaus (BP) remain a core challenge in training quantum neural networks (QNN), where gradient vanishing hinders convergence. This paper proposes a layerwise quantum training (LQT) strategy, which trains parameterized quantum circuits (PQC) incrementally by optimizing each layer separately. Our approach avoids deep circuit initialization by gradually constructing the QNN. Experimental results demonstrate that LQT mitigates the onset of barren plateaus and enhances convergence rates compared to conventional and residual-based QNN, rendering it a scalable alternative for Noisy Intermediate-Scale Quantum (NISQ)-era quantum devices.References
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.
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.
Chang, H., Liu, Y., & Bai, Y. (2017). "A new multi-category support vector machine algorithm." Soft Computing, 21(6), 1377-1389.
M. Akrom, S. Rustad, A.G. Saputro, A. Ramelan, F. Fathurrahman, H.K. Dipojono, A combination of machine learning model and density functional theory method to predict corrosion inhibition performance of new diazine derivative compounds, Mater. Today Commun. 35 (2023) 106402, https://doi.org/10.1016/J. MTCOMM.2023.106402.
M. Akrom, et al., DFT and microkinetic investigation of oxygen reduction reaction on corrosion inhibition mechanism of iron surface by Syzygium Aromaticum extract, Appl. Surf. Sci. 615 (2023), https://doi.org/10.1016/j. apsusc.2022.156319.
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.
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