论文标题
完全量子神经网络
Completely Quantum Neural Networks
论文作者
论文摘要
人工神经网络是现代深度学习算法的核心。我们描述了如何在量子退火器中嵌入和训练一般的神经网络，而无需在训练中引入任何经典元素。为了在最先进的量子退火器上实现网络，我们开发了三种至关重要的成分：编码网络的自由参数，激活函数的多项式近似以及将二进制高级多项式的二进制近似值降低为二次。这些想法共同将损失函数编码为哈密顿模型。然后，量子退火器通过找到基态来训练网络。我们将其用于基础网络，并说明了量子培训的优势：它在找到损失函数的全球最小值以及网络培训以单个退火步骤中收敛的事实，这导致训练时间很短，同时保持高分类性能。我们的方法为通用机器学习模型的量子培训开辟了新的途径。
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum annealer then trains the network by finding the ground state. We implement this for an elementary network and illustrate the advantages of quantum training: its consistency in finding the global minimum of the loss function and the fact that the network training converges in a single annealing step, which leads to short training times while maintaining a high classification performance. Our approach opens a novel avenue for the quantum training of general machine learning models.
