Toward real-time quantum state tomography with machine learning

Ray Kuang Lee¹, Hsien Yi Hsieh^1*

¹Photonics, National Tsing Hua University, Hsinchu, Taiwan

* Presenter:Hsien Yi Hsieh, email:moro1905@hotmail.com

By implementing machine learning architecture with a deep convolutional neural network, we illustrate a fast and robust quantum state tomography(QST) for continuous variables.Demonstrations on the reconstruction of Wigner function and its corresponding density matrix are illustrated for experimental data on squeezed vacuum states and squeezed thermal states.Compared to the commonly adopted maximum-likelihood method,our methodology provides a faster and more efficient solution toward real-time quantum state tomography

Keywords: Machine learning, Quantum State of light, Quantum Optics