论文标题
用暹罗卷积神经网络和半监督学习来识别量子纠缠
Identification of quantum entanglement with Siamese convolutional neural networks and semi-supervised learning
论文作者
论文摘要
量子纠缠是各种量子信息协议和算法中常用的基本属性。尽管如此，识别纠缠的问题仍未达到超过$ 2 \ times3 $的系统的一般解决方案。在这项研究中，我们使用深层卷积NNS（一种监督的机器学习）来识别3 Quity系统中任何两部分的量子纠缠。我们证明，在合成生成的随机密度矩阵的数据集上训练该模型，这些数据集不包括具有挑战性的正面转移纠缠状态（PPTES），这些状态（PPTE）通常无法识别（并正确标记），即使对于训练数据之外的PPTES状态，也无法确定良好的模型准确性。我们的目的是增强模型对PPTE的概括。通过以半监督方式训练的三重暹罗网络应用纠缠的对称操作，我们提高了模型的准确性和识别PPTE的能力。此外，通过构建暹罗模型的合奏，可以观察到更好的概括，类似于为不同阶层的状态寻找单独类型的纠缠见证人的想法。
Quantum entanglement is a fundamental property commonly used in various quantum information protocols and algorithms. Nonetheless, the problem of identifying entanglement has still not reached a general solution for systems larger than $2\times3$. In this study, we use deep convolutional NNs, a type of supervised machine learning, to identify quantum entanglement for any bipartition in a 3-qubit system. We demonstrate that training the model on synthetically generated datasets of random density matrices excluding challenging positive-under-partial-transposition entangled states (PPTES), which cannot be identified (and correctly labeled) in general, leads to good model accuracy even for PPTES states, that were outside the training data. Our aim is to enhance the model's generalization on PPTES. By applying entanglement-preserving symmetry operations through a triple Siamese network trained in a semi-supervised manner, we improve the model's accuracy and ability to recognize PPTES. Moreover, by constructing an ensemble of Siamese models, even better generalization is observed, in analogy with the idea of finding separate types of entanglement witnesses for different classes of states.
