标题： 具有量子加速的相关光学卷积神经网络 显示英文标题

标题： Correlated Optical Convolutional Neural Network with Quantum Speedup

摘要： 与电神经网络相比，光神经网络（ONNs）具有突破带宽限制和降低能耗的潜力，因此近年来引起了广泛关注。到目前为止，已经实现了几种类型的ONNs。然而，当前的ONNs无法实现像量子神经网络模型所表明的那种强大的加速效果。如何构建并实现一种具有量子加速的ONN是一项巨大的挑战。在这里，我们通过引入光学相关性，在理论上提出并实验验证了一种新型的光学卷积神经网络，称为相关光学卷积神经网络（COCNN）。我们展示了COCNN可以在训练过程中表现出量子加速。这一特性从两个方面得到了验证：一是通过比较COCNN和传统卷积神经网络（CNN）的损失函数曲线，直接展示COCNN更快的收敛速度，这一结果与最近提出的量子卷积神经网络（QCNN）的训练性能相一致；二是通过演示COCNN执行QCNN相位识别电路的能力，验证了COCNN与QCNN之间的联系。此外，我们以COCNN模拟3量子比特QCNN相位识别电路为例，并进行实验以展示其合理性和可行性。实验结果完全符合理论计算结果。我们的提议为实现具有量子加速的ONNs开辟了一条新途径，这将有利于大数据时代的信息处理。 显示英文摘要

摘要： Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs have been implemented. However, the current ONNs cannot realize the acceleration as powerful as that indicated by the models like quantum neural networks. How to construct and realize an ONN with the quantum speedup is a huge challenge. Here, we propose theoretically and demonstrate experimentally a new type of optical convolutional neural network by introducing the optical correlation. It is called the correlated optical convolutional neural network (COCNN). We show that the COCNN can exhibit quantum speedup in the training process. The character is verified from the two aspects. One is the direct illustration of the faster convergence by comparing the loss function curves of the COCNN with that of the traditional convolutional neural network (CNN). Such a result is compatible with the training performance of the recently proposed quantum convolutional neural network (QCNN). The other is the demonstration of the COCNNs capability to perform the QCNN phase recognition circuit, validating the connection between the COCNN and the QCNN. Furthermore, we take the COCNN analog to the 3-qubit QCNN phase recognition circuit as an example and perform an experiment to show the soundness and the feasibility of it. The results perfectly match the theoretical calculations. Our proposal opens up a new avenue for realizing the ONNs with the quantum speedup, which will benefit the information processing in the era of big data.