论文标题
量子光学卷积神经网络：量子计算的新型图像识别框架
Quantum Optical Convolutional Neural Network: A Novel Image Recognition Framework for Quantum Computing
论文作者
论文摘要
基于卷积神经网络（CNN）的大型机器学习模型，该模型的参数迅速增加，培训了大量数据，正在部署在从自动驾驶汽车到医疗成像的各种计算机视觉任务中。训练这些模型所需的计算资源的无限需求正在快速超过经典计算硬件的进步，以及包括光学神经网络（ONNS）和量子计算在内的新框架正在作为未来的替代方案进行探索。 在这项工作中，我们报告了一种基于量子计算的新型深度学习模型，量子光学卷积神经网络（QOCNN），以减轻未来计算机视觉应用中的计算瓶颈。使用流行的MNIST数据集，我们根据基于开创性LENET模型的传统CNN对这种新体系结构进行了基准测试。我们还将表现与先前报道的ONN进行了比较，即Gridnet和Constermnet，以及我们通过将复合网与基于量子的正弦非线性相结合而构建的量子光学神经网络（QONN）。从本质上讲，我们的工作通过添加量子卷积和汇总层之前的QONN进行了先前的研究。 我们通过确定其准确性，混乱矩阵，接收器操作特征（ROC）曲线和Matthews相关系数来评估所有模型。模型的性能总体相似，ROC曲线表明新的QOCNN模型很强。最后，我们估计了在量子计算机上执行这个新颖框架的计算效率的提高。我们得出的结论是，改用基于量子计算的方法进行深度学习可能会导致与经典模型的可比精度，同时在计算性能方面实现了前所未有的提升和功耗的急剧降低。
Large machine learning models based on Convolutional Neural Networks (CNNs) with rapidly increasing number of parameters, trained with massive amounts of data, are being deployed in a wide array of computer vision tasks from self-driving cars to medical imaging. The insatiable demand for computing resources required to train these models is fast outpacing the advancement of classical computing hardware, and new frameworks including Optical Neural Networks (ONNs) and quantum computing are being explored as future alternatives. In this work, we report a novel quantum computing based deep learning model, the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the computational bottleneck in future computer vision applications. Using the popular MNIST dataset, we have benchmarked this new architecture against a traditional CNN based on the seminal LeNet model. We have also compared the performance with previously reported ONNs, namely the GridNet and ComplexNet, as well as a Quantum Optical Neural Network (QONN) that we built by combining the ComplexNet with quantum based sinusoidal nonlinearities. In essence, our work extends the prior research on QONN by adding quantum convolution and pooling layers preceding it. We have evaluated all the models by determining their accuracies, confusion matrices, Receiver Operating Characteristic (ROC) curves, and Matthews Correlation Coefficients. The performance of the models were similar overall, and the ROC curves indicated that the new QOCNN model is robust. Finally, we estimated the gains in computational efficiencies from executing this novel framework on a quantum computer. We conclude that switching to a quantum computing based approach to deep learning may result in comparable accuracies to classical models, while achieving unprecedented boosts in computational performances and drastic reduction in power consumption.
