论文标题
通过点击计数数据来识别非经典性的神经网络方法
Neural-network approach for identifying nonclassicality from click-counting data
论文作者
论文摘要
近年来，在量子科学和技术的背景下，机器学习和神经网络方法引起了极大的关注。量子技术未来开发的最重要的任务之一是对非经典资源的验证。在这里，我们提出了一种基于记录的测量统计数据来识别非古典光状态的人工神经网络方法。特别是，我们实现和训练一个能够根据具有多路复用检测器记录的点击统计信息来识别非经典状态的网络。我们使用模拟数据进行培训和测试网络，我们表明它也能够识别某些非经典状态，即使在培训阶段未使用它们。特别是，在样本量较小的情况下，我们的方法在识别非经典性方面可能比已建立的标准更敏感，这表明在预留实验数据和在线应用程序中可能应用。
Machine-learning and neural-network approaches have gained huge attention in the context of quantum science and technology in recent years. One of the most essential tasks for the future development of quantum technologies is the verification of nonclassical resources. Here, we present an artificial neural network approach for the identification of nonclassical states of light based on recorded measurement statistics. In particular, we implement and train a network which is capable of recognizing nonclassical states based on the click statistics recorded with multiplexed detectors. We use simulated data for training and testing the network, and we show that it is capable of identifying some nonclassical states even if they were not used in the training phase. Especially, in the case of small sample sizes, our approach can be more sensitive in identifying nonclassicality than established criteria which suggests possible applications in presorting of experimental data and online applications.
