论文标题
从机器学习处理的集体测量中纠缠量化
Entanglement quantification from collective measurements processed by machine learning
论文作者
论文摘要
在本文中，我们研究了如何减少足够精确的纠缠定量所需的测量构型数量。我们使用人工神经网络而不是分析公式，而是基于集体测量结果（同时测量了所研究状态的多个实例）。这种方法使我们能够探索纠缠量化的精度，这是测量配置的函数。出于我们的研究目的，我们将一般的两分国家及其负面状态视为纠缠量化量。我们在未来的量子通信网络中概述了这种方法的好处。
In this paper, we investigate how to reduce the number of measurement configurations needed for sufficiently precise entanglement quantification. Instead of analytical formulae, we employ artificial neural networks to predict the amount of entanglement in a quantum state based on results of collective measurements (simultaneous measurements on multiple instances of the investigated state). This approach allows us to explore the precision of entanglement quantification as a function of measurement configurations. For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier. We outline the benefits of this approach in future quantum communication networks.
