论文标题
连续可变量子MNIST分类器
Continuous Variable Quantum MNIST Classifiers
论文作者
论文摘要
在本文中，使用MNIST数据集提出了经典和连续变量（CV）量子神经网络混合多分类剂。 CV模型中的截止维度和概率测量方法的组合允许量子电路产生大小的输出向量等于n的升高，其中n代表临界值和m（qumodes的数量）。然后将它们翻译为单热编码标签，并用适当数量的零填充。基于连续变量量子神经网络提出的二进制分类器架构，使用2,3，...，8个Qumodes构建了总共八个不同的分类器。 CV模型中的位移门和KERR门允许经典神经网络的偏置添加和非线性激活成分与量子。分类器由经典的前馈神经网络，量子数据编码电路和CV量子神经网络电路组成。在600个样本的截短的MNIST数据集中，4个Qumode混合分类器可实现100％的训练精度。
In this paper, classical and continuous variable (CV) quantum neural network hybrid multiclassifiers are presented using the MNIST dataset. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size equal to n raised to the power of n where n represents cutoff dimension and m, the number of qumodes. They are then translated as one-hot encoded labels, padded with an appropriate number of zeros. The total of eight different classifiers are built using 2,3,...,8 qumodes, based on the binary classifier architecture proposed in Continuous variable quantum neural networks. The displacement gate and the Kerr gate in the CV model allow for the bias addition and nonlinear activation components of classical neural networks to quantum. The classifiers are composed of a classical feedforward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4 qumode hybrid classifier achieves 100% training accuracy.
