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This paper presents a method for training quantum neural networks using the quantum information bottleneck (QIB) approach, aiming to optimize the flow of relevant information through the network. It introduces algorithms for estimating the QIB quantity and its derivatives, demonstrating that quantum neural networks can be trained efficiently under certain conditions. The work highlights the potential of QIB methodology in addressing challenges in quantum machine learning and provides practical evidence thro

Author

mmmenglish2000

Language

EN