论文标题
量子纠缠识别
Quantum entanglement recognition
论文作者
论文摘要
纠缠构成量子物质的关键特征。但是，它的发现仍然面临重大挑战。在这封信中，我们制定了一个基于机器学习技术探测纠缠的框架。中心元素是一种从量子多体状态产生统计图像的方案，我们通过卷积神经网络对其进行图像分类。我们表明，由此产生的量子纠缠识别任务是准确的，可以在各种量子状态下分配良好的控制误差。我们讨论了在实验中量化量子纠缠的潜在用途。我们开发的方案为平衡和非平衡量子物质的量子纠缠识别提供了普遍适用的策略。
Entanglement constitutes a key characteristic feature of quantum matter. Its detection, however, still faces major challenges. In this letter, we formulate a framework for probing entanglement based on machine learning techniques. The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks. We show that the resulting quantum entanglement recognition task is accurate and can be assigned a well-controlled error across a wide range of quantum states. We discuss the potential use of our scheme to quantify quantum entanglement in experiments. Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter.
