  In this paper, we conduct a comprehensively analyze the influence of
different quantum noise gates, including Phase Flip, Bit Flip, Phase Damping,
Amplitude Damping, and the Depolarizing Channel, on the performance of HyQNNs.
Our results reveal distinct and significant effects on HyQNNs training and
validation accuracies across different probabilities of noise. For instance,
the Phase Flip gate introduces phase errors, and we observe that HyQNNs exhibit
resilience at higher probability (p = 1.0), adapting effectively to consistent
noise patterns, whereas at intermediate probabilities, the performance
declines. Bit Flip errors, represented by the PauliX gate, impact HyQNNs in a
similar way to that Phase Flip error gate. The HyQNNs, can adapt such kind of
errors at maximum probability (p = 1.0). Unlike Phase and Bit Flip error gates,
Phase Damping and Amplitude Damping gates disrupt quantum information, with
HyQNNs demonstrating resilience at lower probabilities but facing challenges at
higher probabilities. Amplitude Damping error gate, in particular, poses
efficiency and accuracy issues at higher probabilities however with lowest
probability (p = 0.1),it has the least effect and the HyQNNs, however not very
effectively, but still tends to learn. The Depolarizing Channel proves most
detrimental to HyQNNs performance, with limited or no training improvements.
There was no training potential observed regardless of the probability of this
noise gate. These findings underscore the critical need for advanced quantum
error mitigation and resilience strategies in the design and training of
HyQNNs, especially in environments prone to depolarizing noise. This paper
quantitatively investigate that understanding the impact of quantum noise gates
is essential for harnessing the full potential of quantum computing in
practical applications.
