@article{4828,
  author = {Sasiram Anupoju},
  title = {Benchmarking the Decoherence Threshold of Hybrid Quantum Classical Networks for Edge based EEG Telemetry},
  journal = {Journal of Digital Information Management},
  year = {2026},
  volume = {24},
  number = {3},
  doi = {https://doi.org/10.6025/jdim/2026/24/3/158-165},
  url = {https://www.dline.info/fpaper/jdim/v24i3/jdimv24i3_3.pdf},
  abstract = {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.},
}