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.

翻译：在量子计算的竞赛中，量子神经网络（QNN）的潜在优势日益显著。然而，含噪中等规模量子（NISQ）处理器容易出错，这对复杂算法或量子机器学习的执行构成了重大挑战。为确保QNN的质量和安全性，探索噪声对其性能的影响至关重要。本文全面分析了噪声对QNN的影响，研究了多种噪声模型下的Mottonen态制备算法，并考察了量子态经过多层QNN时的退化情况。此外，本文评估了噪声对预训练QNN性能的影响，并重点指出了量子计算中噪声模型带来的挑战。本研究结果对量子软件开发具有重要启示，强调在开发QNN时优先考虑稳定性和噪声校正措施，以确保结果的可靠性和可信度。本文丰富了量子计算与量子机器学习领域的现有文献，为理解噪声对QNN的影响提供了新视角，并推动更鲁棒、高效的量子算法的发展。