In the race towards quantum computing, the potential benefits of quantum
neural networks (QNNs) have become increasingly apparent. However, Noisy
Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a
significant challenge for the execution of complex algorithms or quantum
machine learning. To ensure the quality and security of QNNs, it is crucial to
explore the impact of noise on their performance. This paper provides a
comprehensive analysis of the impact of noise on QNNs, examining the Mottonen
state preparation algorithm under various noise models and studying the
degradation of quantum states as they pass through multiple layers of QNNs.
Additionally, the paper evaluates the effect of noise on the performance of
pre-trained QNNs and highlights the challenges posed by noise models in quantum
computing. The findings of this study have significant implications for the
development of quantum software, emphasizing the importance of prioritizing
stability and noise-correction measures when developing QNNs to ensure reliable
and trustworthy results. This paper contributes to the growing body of
literature on quantum computing and quantum machine learning, providing new
insights into the impact of noise on QNNs and paving the way towards the
development of more robust and efficient quantum algorithms.

该研究对噪声对量子神经网络的影响进行了全面分析，研究了 Mottonen 状态准备算法在各种噪声模型下的表现，并研究了量子状态在多层量子神经网络中的退化。此外，该论文评估了噪声对预训练量子神经网络性能的影响，并强调了噪声模型在量子计算中所带来的挑战。该研究结果对量子软件的发展具有重要意义，强调了在开发可靠和可信赖的量子神经网络时优先考虑稳定性和噪声修正措施的重要性，为量子计算和量子机器学习领域的数据库添砖加瓦，提供了对噪声对量子神经网络影响的新洞察，为更强大和有效的量子算法的发展铺平了道路。