Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis.
HAIS(2023)
摘要
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.
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关键词
quantum neural networks,noise,neural networks,experimental analysis
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