Investigating the Effect of Noise on the Training Performance of Hybrid Quantum Neural Networks

TL;DR

This paper analyzes how various quantum noise gates affect the training performance of Hybrid Quantum Neural Networks, highlighting the importance of error mitigation strategies for practical quantum computing applications.

Contribution

It provides a comprehensive quantitative analysis of the impact of different quantum noise gates on HyQNN training, revealing their varying effects and resilience patterns.

Findings

Phase Flip and Bit Flip errors allow HyQNNs to adapt at high noise probabilities.

Phase Damping and Amplitude Damping disrupt information, challenging HyQNNs at high noise levels.

Depolarizing Channel severely hampers HyQNN training, showing no improvement regardless of noise probability.

Abstract

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.