论文标题
神经网络量子状态断层扫描
Neural-network quantum state tomography
论文作者
论文摘要
我们重新审视神经网络技术在量子状态层析成像中的应用。我们确认，可以通过训练有素的网络成功实施阳性约束，这些网络将输出从标准的前馈神经网络转换为量子状态的有效描述。任何标准的神经网络结构都可以通过我们的方法进行调整。我们的结果开放了使用最先进的深度学习方法在各种类型的噪声下进行量子状态重建的可能性。
We revisit the application of neural networks techniques to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.
