Machine-learning enhanced quantum state tomography and quantum noise reduction to the advanced gravitational wave detectors

With this talk, I will first illustrate the implementation of our machine-learning (ML) enhanced quantum state tomography (QST) for continuous variables, through the experimentally measured data generated from squeezed vacuum states, as an example of quantum machine learning. At the same time, as a collaborator for LIGO-VirgoKAGRA (LVK) gravitational wave network and Einstein Telescope, our plan to inject this squeezed vacuum field into the advanced gravitational wave detectors (GWD) will be introduced. Finally, I will report our recent progress in applying such a ML-QST as a crucial diagnostic toolbox for applications with squeezed states, from Wigner currents, optical cat state generation, and Bayesian estimation for GWD.