Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96 Gothenburg, Sweden.
Departamento de Fisica Teorica de la Materia Condensada and Condensed Matter Physics Center (IFIMAC), Universidad Autonoma de Madrid, Madrid 28049, Spain.
Phys Rev Lett. 2021 Oct 1;127(14):140502. doi: 10.1103/PhysRevLett.127.140502.
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 dueling neural networks, a generator and a discriminator, learn multimodal 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, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pretrained on similar quantum states.
量子态层析（QST）是中等规模量子设备中的一项具有挑战性的任务。在这里，我们将条件生成对抗网络（CGAN）应用于 QST。在 CGAN 框架中，两个决斗神经网络，一个生成器和一个鉴别器，从数据中学习多模态模型。我们通过自定义神经网络层来扩充 CGAN，这些层可以将任何标准神经网络的输出转换为物理密度矩阵。为了重建密度矩阵，生成器和鉴别器网络使用标准基于梯度的方法在数据上相互训练。我们证明，我们的 QST-CGAN 使用数量级更少的迭代步骤和更少的数据，比加速的基于投影梯度的和迭代最大似然估计都能更准确地重建光学量子态。我们还表明，如果生成器网络已经在类似的量子态上进行了预训练，那么 QST-CGAN 可以在对生成器网络的单次评估中重建量子态。
