Quantum Neural Networks - Architectures and Training: Exploring architectures and training methods for quantum neural networks (QNNs) to perform quantum-enhanced learning and decision-making tasks
Keywords:
QNN modelsAbstract
Quantum Neural Networks (QNNs) have emerged as a promising approach to leverage quantum computing advantages in machine learning tasks. This paper provides a comprehensive overview of QNN architectures and training methods for quantum-enhanced learning and decision-making. We discuss various QNN models, including Quantum Boltzmann Machines, Quantum Hopfield Networks, and Quantum Perceptrons, highlighting their unique features and applications. Furthermore, we review state-of-the-art training algorithms such as quantum gradient descent and quantum backpropagation, emphasizing their role in optimizing QNNs. Through this paper, we aim to provide researchers and practitioners with a deeper understanding of QNNs and inspire further advancements in this rapidly evolving field.
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