Quantum phase space dynamics with machine learning tomography

Hsien Yi Hsieh^1*

¹Institute of photonics, Tsing Hua University, Hsinchu, Taiwan

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

In this work, we implemented a high-performance, lightweight and easy-to-install supervised machine learning model in the laboratory. It can estimate truncated density matrices with high quality in a large Hilbert space and can make physical parameter estimation of quantum states roughly. With this machine learning assisted tomography, full monitoring of experimental Wigner flow (phase space flux) as well as the degradation information can be accomplished by single shot inference. The observed quantum dynamics are illustrated for the stagnation point and non-trivial topological order in squeezers, as well as the identification of decoherence current due to the interactions with the environment. Our experimental demonstration provides a novel paradigm to measure the quantumness and non-classicality through the flux of quantum information in the phase space.

Keywords: Quantum optics, Machine learning, Quantum experiments