Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis

• URL: http://arxiv.org/abs/2311.14057v1
• Date: Thu, 23 Nov 2023 15:22:22 GMT
• Title: Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis
• Authors: Erik B. Terres Escudero, Danel Arias Alamo, Oier Mentxaka G\'omez, Pablo Garc\'ia Bringas
• Abstract summary: In quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. 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. This paper provides a comprehensive analysis of the impact of noise on QNNs, examining the Mottonen state preparation algorithm under various noise models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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