TY - GEN
T1 - Assessing the Impact of Noise on Quantum Neural Networks
T2 - Proceedings of the 18th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2023
AU - Escudero, Erik Terres
AU - Alamo, Danel Arias
AU - Gómez, Oier Mentxaka
AU - Bringas, Pablo García
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Noisy Intermediate-Scale Quantum
KW - Quantum Computing
KW - Quantum Machine Learning
KW - Quantum Neural Networks
UR - https://www.scopus.com/pages/publications/85172192887
U2 - 10.1007/978-3-031-40725-3_27
DO - 10.1007/978-3-031-40725-3_27
M3 - Conference contribution
AN - SCOPUS:85172192887
SN - 9783031407246
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 325
BT - Hybrid Artificial Intelligent Systems - 18th International Conference, HAIS 2023, Proceedings
A2 - García Bringas, Pablo
A2 - Pérez García, Hilde
A2 - Martínez de Pisón, Francisco Javier
A2 - Martínez Álvarez, Francisco
A2 - Troncoso Lora, Alicia
A2 - Herrero, Álvaro
A2 - Calvo Rolle, José Luis
A2 - Quintián, Héctor
A2 - Corchado, Emilio
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 September 2023 through 7 September 2023
ER -