论文标题
深度神经网络作为拓扑量子神经网络的半古典限制：泛化问题
Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
论文作者
论文摘要
深度神经网络错过了其操作的原则模型。最近已经探索了一个基于拓扑量子场理论的新型监督学习框架，该框架看起来特别适合在量子处理器上实现。我们建议使用此框架来了解深层神经网络中的概括问题。更具体地说，在这种方法中，深度神经网络被视为拓扑量子神经网络的半古典限制。这种框架解释了在训练步骤和相应的概括能力中深神网络的过度拟合行为。我们探讨了感知量的范式案例，我们将其作为拓扑量子神经网络的半经典极限实现。我们应用了一种新型算法，表明它获得了与标准神经网络相似的结果，但无需训练（优化）。
Deep Neural Networks miss a principled model of their operation. A novel framework for supervised learning based on Topological Quantum Field Theory that looks particularly well suited for implementation on quantum processors has been recently explored. We propose using this framework to understand the problem of generalisation in Deep Neural Networks. More specifically, in this approach, Deep Neural Networks are viewed as the semi-classical limit of Topological Quantum Neural Networks. A framework of this kind explains the overfitting behavior of Deep Neural Networks during the training step and the corresponding generalisation capabilities. We explore the paradigmatic case of the perceptron, which we implement as the semiclassical limit of Topological Quantum Neural Networks. We apply a novel algorithm we developed, showing that it obtains similar results to standard neural networks, but without the need for training (optimisation).
