论文标题
通过混合量子 - 古典机器学习框架量化未知的量子纠缠框架
Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical Machine Learning Framework
论文作者
论文摘要
实验上量化未知的量子纠缠是一项艰巨的任务，但由于量子工程的快速发展，也变得越来越必要。机器学习为这个基本问题提供了实用的解决方案，其中必须训练适当的机器学习模型，以预测基于实验可测量数据的未知量子状态的纠缠度量，例如当地测量结果产生的矩或相关数据。在本文中，我们系统地比较了这两种不同的机器学习方法的性能。特别是，我们首先表明，基于时刻的方法比相关数据具有显着的优势，尽管测量矩的成本要高得多。接下来，由于相关数据更容易通过实验获得，因此我们通过为此问题提出一个混合量子量子的机器学习框架来改善其性能，其中关键是训练最佳的本地测量以生成更有信息的相关性数据。我们的数值模拟表明，新框架为我们带来了基于矩量的矩量的方法，使我们的性能与方法相当。我们的工作表明，在近期量子设备上完成此类任务已经很可行。
Quantifying unknown quantum entanglement experimentally is a difficult task, but also becomes more and more necessary because of the fast development of quantum engineering. Machine learning provides practical solutions to this fundamental problem, where one has to train a proper machine learning model to predict entanglement measures of unknown quantum states based on experimentally measurable data, say moments or correlation data produced by local measurements. In this paper, we compare the performance of these two different machine learning approaches systematically. Particularly, we first show that the approach based on moments enjoys a remarkable advantage over that based on correlation data, though the cost of measuring moments is much higher. Next, since correlation data is much easier to obtain experimentally, we try to better its performance by proposing a hybrid quantum-classical machine learning framework for this problem, where the key is to train optimal local measurements to generate more informative correlation data. Our numerical simulations show that the new framework brings us comparable performance with the approach based on moments to quantify unknown entanglement. Our work implies that it is already practical to fulfill such tasks on near-term quantum devices.
