论文标题
带有条件生成对抗网络的量子状态断层扫描
Quantum State Tomography with Conditional Generative Adversarial Networks
论文作者
论文摘要
量子状态层析成像（QST）是中间量子设备中的一项具有挑战性的任务。在这里，我们将有条件的生成对抗网络（CGAN）应用于QST。在CGAN框架中，两个决斗神经网络，一个发电机和一个歧视器，从数据中学习多模式模型。我们使用自定义的神经网络层增强了CGAN，该层可将输出从任何标准的神经网络转换为物理密度矩阵。为了重建密度矩阵，使用基于标准梯度的方法在数据上互相训练。我们证明，与标准的最大似然法相比，我们的QST-CGAN以高保真度的数量级和更少的数据来重建光量子状态。我们还表明，如果已经在类似的量子状态下对发电机网络进行单个评估，则可以在对发电机网络的单个评估中重建量子状态。
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two duelling neural networks, a generator and a discriminator, learn multi-modal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity orders of magnitude faster, and from less data, than a standard maximum-likelihood method. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pre-trained on similar quantum states.
