Analisis Ketahanan Variational QNN Terhadap Bit Flip dan Depolarizing Noise Pada Subset Dataset MNIST
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Gionathan Advent Kristianto Sinaga
*
Universitas Kristen Immanuel, Yogyakarta,
Indonesia
- Suneng Sandino Berutu Universitas Kristen Immanuel, Yogyakarta, Indonesia
- Aninda Astuti Universitas Kristen Immanuel, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
The advancement of quantum computing has encouraged the use of Variational Quantum Neural Networks (VQNNs) as an alternative approach for data classification tasks. However, the implementation of VQNNs in the Noisy Intermediate-Scale Quantum (NISQ) era still faces major challenges due to quantum noise, which can significantly degrade model performance. This study aims to analyze the robustness of VQNNs against quantum noise in a binary classification task using a subset of the MNIST dataset. Image data are processed through normalization and dimensionality reduction using Principal Component Analysis (PCA), and subsequently encoded into quantum circuits using the angle encoding method. The VQNN model is designed with six qubits and a single variational layer, and evaluated under two conditions: an ideal quantum simulation (before noise, statevector) and a noisy quantum simulation (after noise). Quantum noise is simulated using Qiskit Aer, considering two types of noise, namely bit flip and depolarizing noise, across various probability levels. Experimental results show that under ideal conditions, the VQNN achieves a test accuracy of 85.2%. However, increasing noise probabilities lead to a gradual degradation in performance, approaching the random classification threshold at high noise levels. Bit flip noise causes a more severe performance decline compared to depolarizing noise. These findings confirm that quantum noise is a critical factor affecting the stability of VQNNs and highlight the importance of developing effective noise mitigation strategies for VQNN implementations in the NISQ era.
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References
M. Das and T. Bolisetti, “Variational Quantum Neural Networks (VQNNS) in Image Classification,” Mar. 2023, [Online]. Available: http://arxiv.org/abs/2303.05860
S. Sajadimanesh, H. Aghaee Rad, J. Paul Latyr Faye, and E. Atoofian, “NR-QNN: Noise-Resilient Quantum Neural Network,” IEEE Access, vol. 13, pp. 40185–40197, 2025, doi: 10.1109/ACCESS.2025.3546956.
M. Vandromme and M. Tsuji, “Post-Variational Quantum Neural Networks on a Hybrid HPC-QC System,” Association for Computing Machinery (ACM), Nov. 2025, pp. 1816–1823. doi: 10.1145/3731599.3767540.
T. Ahmed, M. Kashif, A. Marchisio, and M. Shafique, “A comparative analysis and noise robustness evaluation in quantum neural networks,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-17769-6.
G. González-García, J. I. Cirac, and R. Trivedi, “Pauli path simulations of noisy quantum circuits beyond average-case,” Quantum, vol. 9, 2025, doi: 10.22331/q-2025-05-05-1730.
Z. Li, T. Xiao, X. Deng, G. Zeng, and W. Li, “Optimizing Variational Quantum Neural Networks Based on Collective Intelligence,” Mathematics, vol. 12, no. 11, Jun. 2024, doi: 10.3390/math12111627.
M. U. Khan, M. A. Kamran, W. R. Khan, M. M. Ibrahim, M. U. Ali, and S. W. Lee, “Error Mitigation in the NISQ Era: Applying Measurement Error Mitigation Techniques to Enhance Quantum Circuit Performance,” Mathematics, vol. 12, no. 14, Jul. 2024, doi: 10.3390/math12142235.
Y. Ding, S. Zhang, X. Li, Y. Chang, and L. Yan, “Noise Mitigation An Efficient Approach for Quantum Convolutional Neural Networks to Enhance Performance in NISQ,” Jul. 03, 2024. doi: 10.21203/rs.3.rs-4594670/v1.
D. Ranga, S. Prajapat, Z. Akhtar, P. Kumar, and A. V. Vasilakos, “Hybrid Quantum–Classical Neural Networks for Efficient MNIST Binary Image Classification,” Mathematics, vol. 12, no. 23, Dec. 2024, doi: 10.3390/math12233684.
C. Long, M. Huang, X. Ye, Y. Futamura, and T. Sakurai, “Hybrid quantum-classical-quantum convolutional neural networks,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-13417-1.
Y. Yang et al., “Hybrid Classical Quantum Neural Network with High Adversarial Robustness,” 2024.
D. García-Martín, M. Larocca, and M. Cerezo, “Effects of noise on the overparametrization of quantum neural networks,” Phys. Rev. Res., vol. 6, no. 1, Jan. 2024, doi: 10.1103/PhysRevResearch.6.013295.
N. Ji, R. Bao, Z. Chen, Y. Yu, and H. Ma, “Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation,” Applied Sciences (Switzerland), vol. 14, no. 4, Feb. 2024, doi: 10.3390/app14041392.
P. S. S. Sein, M. Cañizo, and R. Orús, “Image classification with rotation-invariant variational quantum circuits,” Phys. Rev. Res., vol. 7, no. 1, Jan. 2025, doi: 10.1103/PhysRevResearch.7.013082.
J. Song, B. Lu, L. Liu, and C. Wang, “Noisy Quantum Channel Characterization Using Quantum Neural Networks,” Electronics (Switzerland), vol. 12, no. 11, Jun. 2023, doi: 10.3390/electronics12112430.
D. Winderl, N. Franco, and J. M. Lorenz, “Quantum neural networks under depolarization noise: exploring white-box attacks and defenses,” Quantum Mach. Intell., vol. 6, no. 2, Dec. 2024, doi: 10.1007/s42484-024-00208-6.
H.-F. Zhang et al., “Experimental robustness benchmark of quantum neural network on a superconducting quantum processor,” May 2025, [Online]. Available: http://arxiv.org/abs/2505.16714
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