This paper studies how noise and decoherence affect a hybrid quantum classical neural network (HQCNN) when it is applied to EEG based eye state classification, a task that closely mirrors what an edge based braincomputer interface would need to do in the field. Quantum machine learning holds real promise for embedded health systems, but the hardware of today is noisy, and nobody has clearly mapped out exactly where the accuracy starts to collapse. We built a model that uses a four qubit parameterized quantum circuit implemented in PennyLane and backed by a PyTorch classical front end, then trained it on the wellknown EEG Eye State dataset from OpenML. Once training was done, we deliberately injected depolarizing channel noise at six different error rates from a clean 0.00 all the way up to 0.15 and measured what happened to classification accuracy at each step. The results gave us a clear picture. The noiseless model reached 90.75% accuracy. That number held reasonably steady through low noise, but at a depolarizing error rate of around 0.12 the accuracy dropped below the 90% threshold of the baseline, landing at 86.00%, and by 0.15 it had fallen further to 83.75%. We define a hard mathematical threshold as a 10% relative degradation from the noiseless baseline (0.8168). However, the model did not reach this absolute floor at the maximum tested noise of 0.15 (83.75%). Instead, we identify 0.12 as the practical decoherence warning threshold, defined by a sharp inflection in the rate of change (slope) of the accuracy drop. This distinction clarifies that while the model remains mathematically above the 10% degradation floor, 0.12 represents the critical point where hardware engineers must intervene before clinical deployment. The work proves that a compact four qubit circuit can remain useful for EEG classification even under moderate hardware noise, while also making it clear that current NISQ devices would need error mitigation strategies before being trusted in a clinical edge deployment. The findings contribute a concrete benchmark that system designers can use when deciding whether quantum co-processors belong in the next generation of wearable EEG telemetry hardware.
[1] Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., Woerner, S. (2021). The power of quantum neural networks. Nature Computational Science, 1(6), 403-409.
[2] Amin, M. H., Andriyash, E., Rolfe, J., Kulchyt sky , B., Melko, R. (2023). Quantum Boltzmann machine and transferlearning for ECG classification. Quantum Machine Intelligence, 5(1), 1-14.
[3] Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195-202.
[4] Cerezo,M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., Coles, P. J. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625-644.
[5] Endo, S., Benjamin, S. C., Li, Y. (2018). Practical quantum error mitigation for near future applications. Physical Review X, 8(3), 031027.
[6] Havlicek, V., Corcoles, A. D.,Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., Gambetta, J. M. (2019) Supervised learning with quantum enhanced feature spaces. Nature, 567 (7747), 209-212.
[7] Larocca, M., Jurcevic, P., Bhole, P., Bhatt, K., Benedetti, M. (2022). Diagnosing barren plateaus with tools from quantum optimal control. Quantum, 6, 824
[8] Li, Y., Zhou, R. G., Xu, R., Luo, J., Hu, W. (2021). A quantum deep convolutional neu-ral network for image recognition. Quantum Science and Technology, 5(4), 044003.
[9] Nielsen, M. A., Chuang, I. L. (2000). Quantum Computation and Quantum Information. Cambridge University Press
[10] Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
[11] Rosler, O., Suendermann, D. (2013). A first step towards eye state prediction using EEG. Proceedings of the AIHLS
[12] Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., Faubert, J. (2019). Deep learning based electro encephalography analysis: A systematic review. Journal of Neural Engineering, 16(5), 051001.
[13] Schuld, M., Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122(4), 040504 [14] Stilck Franca, D., Garcia Patron, R. (2021). Limitations of optimization algorithms on noisy quantum devices. Nature Physics, 17(11), 1221-1227.
[15] Temme, K., Bravyi, S., Gambetta, J. M. (2017). Error mitigation for short depth quantum circuits. Physical Review Letters, 119(18), 180509.
[16] Bergholm, V., Izaac, J., Schuld,M., Gogolin,C., Ahmed, S., Ajith,V., Killoran, N. (2018). PennyLane: Automatic differentiation of hybrid quantum classical computations. arXiv:1811.04968.
[17] Zhang, X., Yao, L., Sheng, Q. Z., Kanhere, S. S., Gu, T., Zhang, D. (2020). Converting yourthoughts to texts: Enabling brain typing via deep feature learning of EEG signals. IEEE Internet of Things Journal, 7(5), 4536-4548.