Classification of optical quantum states using machine learning

Machine-learning techniques for quantum state tomography can give significantly faster results, e.g., by using adaptive measurements to avoid redundant data acquisition, or by finding efficient parameterizations of a quantum state to escape the "curse of dimensionality". Here, we explore a similar idea for optical quantum states by using deep neural networks (DNNs). We train a DNN to classify different optical quantum states, e.g., cat or thermal states, with a high accuracy. Our DNN can also predict interesting physical properties, such as Wigner negativity, directly from measurement data without requiring a full reconstruction. We study the influence of various factors such as noise, Hilbert-space cutoff, and measurement settings on the predictions and show that the DNN approach is robust. We also apply standard methods for analyzing neural network predictions, such as Grad-CAM, to determine the features used by the network to make its predictions. To benchmark our method, we compare with a naive classifier using maximum likelihood estimation. Our results indicate that the DNN can be a fast and efficient real-time classifier to distinguish various optical quantum states in the lab.