论文标题
量子变形神经网络
Quantum Deformed Neural Networks
论文作者
论文摘要
我们开发了一个新的量子神经网络层，旨在在量子计算机上有效运行，但是在限制其纠缠输入状态的方式时，可以在经典计算机上进行模拟。我们首先询问如何使用量子相估计在量子计算机上执行经典的神经网络体系结构。然后，我们将经典层变形为一种量子设计，该量子设计将激活和权重变成量子叠加。虽然完整的模型需要量子计算机提供的指数加速度，但受限的设计类别代表了仍然使用量子功能的有趣的新经典网络层。我们表明，这些量子变形的神经网络可以在图像等正常数据（例如图像）上进行训练和执行，甚至可以对标准体系结构进行适度的改进。
We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states. We first ask how a classical neural network architecture, both fully connected or convolutional, can be executed on a quantum computer using quantum phase estimation. We then deform the classical layer into a quantum design which entangles activations and weights into quantum superpositions. While the full model would need the exponential speedups delivered by a quantum computer, a restricted class of designs represent interesting new classical network layers that still use quantum features. We show that these quantum deformed neural networks can be trained and executed on normal data such as images, and even classically deliver modest improvements over standard architectures.
