论文标题
通过同时处理多个样本，用于在变异电路中快速监督学习的算法
An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples
论文作者
论文摘要
我们提出了一种新型算法，用于通过处理多个样本相反，以快速训练变异分类器。该算法可以适用于变异电路中使用的任何ANSATZ。提出的算法在前通时利用QRAM和其他量子电路。此外，我们使用掉期测试电路计算损失，而不是通常的经典计算损失的做法。因此，当在n个样本的数据集上训练时，算法将变异分类器的训练成本从通常的O（n）带到了O（logN）。尽管我们仅在论文中讨论二进制分类，但算法可以很容易地推广到多类分类。
We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.
